Add Batch 6deaede3-f475-4605-b942-23cce4e9976c data

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2023/Beneath the Surface_ Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models/8b1ff79e-86f8-41a0-b232-69890219b3c0_origin.pdf

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2023/Beneath the Surface_ Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models/full.md

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+# Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
+
+Hongzhan Lin<sup>1</sup>, Ziyang Luo<sup>1</sup>, Jing Ma<sup>1*</sup>, Long Chen<sup>2</sup>
+
+$^{1}$ Hong Kong Baptist University
+
+2The Hong Kong University of Science and Technology
+
+{cshzlin,cszyluo,majing}@comp.hkbu.edu.hk,longchen@ust.hk
+
+# Abstract
+
+The age of social media is rife with memes. Understanding and detecting harmful memes pose a significant challenge due to their implicit meaning that is not explicitly conveyed through the surface text and image. However, existing harmful meme detection approaches only recognize superficial harm-indicative signals in an end-to-end classification manner but ignore in-depth cognition of the meme text and image. In this paper, we attempt to detect harmful memes based on advanced reasoning over the interplay of multimodal information in memes. Inspired by the success of Large Language Models (LLMs) on complex reasoning, we first conduct abductive reasoning with LLMs. Then we propose a novel generative framework to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning, which consists of two training stages: 1) Distill multimodal reasoning knowledge from LLMs; and 2) Fine-tune the generative framework to infer harmfulness. Extensive experiments conducted on three meme datasets demonstrate that our proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task.
+
+# 1 Introduction
+
+The development of social media platforms has given rise to a new form of multimodal content known as: meme. A meme typically comprises a picture that is combined with a concise text component. Memes possess the capacity to quickly spread across the internet, especially on social media platforms, due to their ease of dissemination. While memes are often seen as humorous, there is a potential for harm when the combination of images and texts is strategically used to promote political and sociocultural divisions. For instance as in Figure 1(a), during the COVID-19 pandemic, a widely
+
+circulated meme falsely claimed that the mRNA vaccine would alter human genetic code (DNA) $^{1}$ . Such multimodal disinformation spread caused vaccine safety and effectiveness concerns, hindering the formation of strong immune defenses in impacted areas globally (Basch et al., 2021; Lin et al., 2022). Besides, another meme example shown in Figure 1(b) perpetuates harmful stereotypes and generalizations about Asians. Therefore, it is necessary to develop automatic approaches to facilitate harmful meme detection for unveiling the dark side of memes.
+
+Harmful memes $^2$  are generally defined as “multimodal units consisting of an image and accompanying text that has the potential to cause harm to an individual, an organization, a community, or the whole society” (Sharma et al., 2022). Previous studies (Kiela et al., 2020; Pramanick et al., 2021a,b) attempted to straightforwardly utilize pretrained vision-language models (Li et al., 2019; Lu et al., 2019) for harmful meme detection by training additional task-specific classification layers. More recently, Cao et al. (2022) proposed a prompt-tuning method with the meme text and image caption as the prompt for masked language modeling (Devlin et al., 2019; Liu et al., 2019).
+
+However, existing harmful meme detection approaches oversimplified the problem as an end-to-end classification paradigm, which only recognizes the superficial signals conveyed through the surface text and image. But more in-depth investigation and cognition on the implicit meaning is required especially when the image and text are not obviously correlated (Pramanick et al., 2021b). Intuitively, the key to harmful meme detection is to excavate rich correlations beneath the surface
+
+![](images/4fd3253617473e8b9c0a3aa8b14c7057ba530622b0b3cccb8f9e5fce7ae1509b.jpg)  
+(a) Harmful
+
+![](images/bec2bfdaaa43497e208c020c586251c661f598efecc8d27cf14756fe7c634578.jpg)  
+(b) Harmful  
+Figure 1: Examples of harmful and harmless memes. Memetext: (a) Chance a virus with a  $99.97\%$  recovery rate; Alter my DNA from an experimental vaccine, with NO liability, from a corrupt industry. (b) when you date an asian boy and you trynna get his family to accept you. (c) you either die a hero, or live long enough to become the villain.
+
+![](images/9056d3473631cb7c22e2ae49b49bd32544856d7aaad9845a56e60fa512ae0264.jpg)  
+(c) Harmless
+
+of the seemingly uncorrelated text and image in the meme: 1) For example as in Figure 1(b), the image and the text are not harmful when considered in isolation, but are harmful when taken as a whole. A human checker should cognize that, the “biting” action in the image of a young woman with her pet dog ridicules Asians’ “dog-eating” behavior, which corresponds to the “asian” word in the text. 2) In contrast, some harmful signals (e.g., “die” or “villain”) are observed in the text of Figure 1(c), but the meme itself actually does not promote hate or discrimination against a particular group of people. Because the text is a quote from a popular movie and is often used as a philosophical statement about the choices people make in life. And the image further adds a celebratory and joyful tone to the overall message. In comparison, conventional detection methods just focused on recognizing shallow harm-indicative signals without such multimodal reasoning and essential background knowledge consideration, so the social dynamics of different races or the origin of the meme text from classical movie lines may not be well-cognized. Unlike such recognition-level detection models, we argue that establishing reasonable thought between textual and visual information can further improve meme understanding with background knowledge for better harmful meme detection.
+
+Inspired by the success of LLMs for reasoning at the cognition level with contextual background knowledge (Wei et al., 2022; Kojima et al., 2022; Zhang et al., 2022), we propose a novel approach: MR.HARM, by leveraging the Multimodal reasoning knowledge distilled from LLMs for Harmful meme detection. To this end, we first prompt LLMs for abductive reasoning, and then propose a two-stage generative framework based on smaller language models to learn reasonable thoughts from LLMs for better multimodal fusion
+
+and lightweight fine-tuning. More specifically, we incorporate the meme text and image into a two-stage training paradigm: 1) Reasoning Distillation: In the first stage, we fine-tune our smaller language models with the interaction of language and vision features to distill multimodal reasoning knowledge from LLMs, which empowers our framework with the ability to conduct cognitive reasoning for the harmfulness prediction. 2) Harmfulness Inference: In the second stage, we exploit the fine-tuned small language models to infer the final harmfulness prediction. In this manner, we augment the harmful meme detection model with multimodal reasoning knowledge to unmask the implicit meaning hidden in holistic multimodal information from memes.
+
+We evaluate our proposed approach based on three public meme datasets. The results not only show that our method outperforms strong harmful meme detection baselines by a large margin, but also provide fine-grained analysis for interpreting how our approach works. Our contributions are summarized as follows in three folds:
+
+- To our best knowledge, we are the first to alleviate the issue of superficial understanding for harmful meme detection by explicitly utilizing commonsense knowledge, from a fresh perspective on harnessing advanced LLMs.3  
+- We propose a novel generative framework to fine-tune smaller language models augmented with the multimodal reasoning knowledge distilled from LLMs, which facilitates better multimodal fusion and lightweight fine-tuning for harmfulness prediction.  
+- Extensive ablations on three meme datasets confirm that our method could yield superior
+
+performance than state-of-the-art baselines for the harmful meme detection task.
+
+# 2 Related Work
+
+# 2.1 Harmful Meme Detection
+
+Harmful meme detection is a rapidly growing area in the research community, driven by the recent availability of large meme benchmarks (Kiela et al., 2019; Suryawanshi et al., 2020; Pramanick et al., 2021a). The Hateful Memes Challenge organized by Facebook (Kiela et al., 2020) further encouraged researchers to develop solutions for detecting harmful memes in hate speech (Das et al., 2020). More recently, Pramanick et al. (2021a) firstly defined the harmful meme concept and demonstrated its dependence on contextual factors. The complex nature of memes, which often rely on multiple modalities, makes them challenging and struggle to yield good performance only using unimodal detection methods (Simonyan and Zisserman, 2014; He et al., 2016; Devlin et al., 2019). Therefore, recent studies in this area attempted to apply multimodal approaches on the harmful meme detection task.
+
+Previous studies have employed classical two-stream models that integrate text and vision features, which are learned from text and image encoders, using attention-based mechanisms and multimodal fusion techniques for classifying harmful memes (Kiela et al., 2019, 2020; Suryawanshi et al., 2020). Another branch was to fine-tune pre-trained multimodal models specifically for the task (Lippe et al., 2020; Muennighoff, 2020; Velioglu and Rose, 2020; Hee et al., 2022). Recent related efforts have also sought to explore the use of data augmentation techniques (Zhou et al., 2021; Zhu et al., 2022), ensemble methods (Zhu, 2020; Velioglu and Rose, 2020; Sandulescu, 2020) and harmful target disentanglement (Lee et al., 2021). More recently, Pramanick et al. (2021b) proposed a multimodal framework by using global and local perspectives to detect harmful memes which achieves state-of-the-art performances. A follow-up prompt-based approach (Cao et al., 2022) attempted to concatenate the meme text and extracted image captions to fine-tune masked language models (Liu et al., 2019) for harmful meme detection. However, existing solutions only capture the superficial signals of different modalities in memes in an end-to-end manner, which largely ignore explicit deductive reasoning to guide the model for understanding background knowledge about the complex and diverse
+
+relations between the visual and textual elements.
+
+# 2.2 Large Language Models
+
+Recently, LLMs have demonstrated remarkable capability in complex reasoning (Brown et al., 2020; Thoppilan et al., 2022; Rae et al., 2021; Chowdhery et al., 2022), such as generating intermediate inference procedures before the final output (Nye et al., 2021; Wei et al., 2022; Kojima et al., 2022; Zhang et al., 2022). Unfortunately, the large size of LLMs restricts their deployment on detecting harmful memes with different modalities, regardless of how they are enhanced with strategic text prompting. Knowledge distillation has been successfully used to transfer knowledge from larger, more competent teacher models into smaller student models affordable for practical applications (Bucilua et al., 2006; Hinton et al., 2015; Beyer et al., 2022). However, existing researches on knowledge distillation from LLMs (Wang et al., 2022; Ho et al., 2022; Magister et al., 2022) only consider the language modality, they are not suitable for harmful meme detection because harmful memes can convey holistic synergy information through multimodal features. In this work, we conduct abductive reasoning with LLMs, which further advocates a multimodal reasoning paradigm to fine-tune smaller language models (LMs) for harmful meme detection.
+
+# 3 Our Approach
+
+Problem Statement We define a harmful meme detection dataset as a set of memes where each meme  $M = \{y, \mathcal{I}, \mathcal{T}\}$  is a triplet representing an image  $\mathcal{I}$  that is associated with a text  $\mathcal{T}$ , and a ground-truth harmfulness label  $y \in \{\text{harmful, harmless}\}$ . In this work, to investigate multimodal reasoning distilled from LLMs, we convert the harmful meme detection task into a natural language generation paradigm, where our model takes the text  $\mathcal{T}$  and image  $\mathcal{I}$  as the input and generates a text sequence that contains the label  $y$  to clearly express whether the meme is harmful.
+
+Our core idea is to reason and evolve with the cognition-level rationale beyond the recognition-level perception (Davis and Marcus, 2015) by capturing the inter-relationship between visual and textual elements in memes. The overview of our framework is shown in Figure 2, which consists of abductive reasoning with LLMs (see Sec. 3.1) and two training stages, i.e., reasoning distillation (see Sec. 3.2) and harmfulness inference (see Sec. 3.3).
+
+![](images/f4517eeb2dd02664a49e957a9844149b9d15edb83a5fc409af4d6e338cf8c7d6.jpg)  
+Figure 2: The overall pipeline of our method. We first conduct abductive reasoning with LLMs to extract harmfulness rationales (pink) by the prompt consisting of the meme text (green), the image caption (blue), and the label (orange). We then use the generated rationales to train small task-specific models with multimodal inputs as the first fine-tuning stage and feed the same inputs to the updated model for harmfulness inference as the second fine-tuning stage.
+
+# 3.1 Abductive Reasoning with LLMs
+
+In this paper, we propose to utilize abductive reasoning with multimodal inputs to train smaller downstream models. LLMs can produce natural language rationales unveiling the implicit meaning beneath the surface of the memes to justify the reason why the meme is harmful or not. This shares a similar intuition as heuristic teaching (Pintrich and Schunk, 2002) where a teacher who has rich experience and knowledge can impart to students the correct way of thinking and reasoning based on questions with corresponding answers. The students then learn how to deduce their own ways to the correct answers from questions accordingly. Thus we aim to activate explicit reasoning knowledge in LLMs as a teacher model, e.g., contextual and cultural information related to memes, to guide our model to strengthen harmfulness prediction.
+
+Given a meme sample  $M = \{y, \mathcal{I}, \mathcal{T}\}$  from the training data, to prompt large language models in uniform language modality, we first extract the text caption  $\tilde{\mathcal{I}}$  of the image  $\mathcal{I}$  by off-the-shelf captioning models (Mokady et al., 2021). Then we curate a template  $p$  that consists of a triplet  $\{y, \tilde{\mathcal{I}}, \mathcal{T}\}$  as observed attributes, to prompt the LLMs to generate a rationale  $r$  that elicits the reasoning knowledge about how to infer the harmfulness label  $y$  based on the interplay of the meme text  $\mathcal{T}$  and the image caption  $\tilde{\mathcal{I}}$  as illustrated in Figure 2. Specifically, we design  $p$  as:
+
+"Given a Text: [T], which is embedded in an Image: [I]; and a harmfulness label [y], please give me a streamlined rationale associated with the meme, without explicitly indicating the label, for how it is reasoned as [y]."
+
+As we clarify the ground-truth harmfulness label in the observed attributes of the prompt, the hallucination issue (Bang et al., 2023) of LLMs could be effectively alleviated. Because the rich contextual background knowledge could be activated by abductive reasoning based on the ground truth and invalid rationales are naturally filtered out.
+
+# 3.2 Reasoning Distillation with Small LMs
+
+Since we utilize image captions to represent the meme images, we could perform abductive reasoning with large language models pre-trained with language modality. However, only using the captions as opposed to original vision features may suffer from a lack of mutual synergy in the representation space of different modalities in memes due to the inductive bias of possible information loss in the captioning process. On the other hand, LLMs can be used to conduct abductive reasoning only for the training data whose harmfulness label is given in prior but is challenging to be fine-tuned for this task due to the huge amount of model parameters. To facilitate the interactions between the meme text and the image, we propose to fine-tune a smaller language model for the harmful meme detection task, which allows flexibility in adjusting model architectures to incorporate multimodal features and is more lightweight for task-specific fine-tuning.
+
+In this section, we train a small language model as a student model distilled from the LLMs with multimodal reasoning knowledge. Specifically, we leverage generated rationales from LLMs as informative supervision, to fine-tune a smaller pretrained language model to excavate the rich interrelationship between language and vision modali
+
+ties of memes.
+
+Encoding For a meme sample  $M$  from the training data, we first encode the text  $\mathcal{T}$  and the image  $\mathcal{I}$  to obtain their embedding vectors as follows:
+
+$$
+H _ {\mathcal {T}} ^ {0} = \operatorname {T E} (\mathcal {T}), \tag {1}
+$$
+
+$$
+H _ {\mathcal {I}} = \operatorname {V E} (\mathcal {I}),
+$$
+
+where  $\mathrm{TE}(\cdot)$  denotes the text embedding layer of the LM Encoder. And  $H_{\mathcal{T}}^{0}\in \mathbb{R}^{m\times d}$  is the token embeddings in the Transformer encoder (Vaswani et al., 2017) where  $m$  is the text token length and  $d$  is the dimension of the hidden states.  $\mathrm{VE}(\cdot)$  is the Vision Extractor implemented as frozen pre-trained vision Transformers (Radford et al., 2021) to fetch the patch-level features of the image with  $n$  patches, which is projected into the visual representations  $H_{\mathcal{I}}\in \mathbb{R}^{n\times d}$ . Next, to support semantic alignment between the text and the image for better context understanding, we exploit a cross-attention mechanism (Luo et al., 2022) for multimodal fusion of the textual and visual information:
+
+$$
+Q _ {\mathcal {T}} = W _ {Q} ^ {i} H _ {\mathcal {T}} ^ {i} + b _ {Q} ^ {i},
+$$
+
+$$
+K _ {\mathcal {I}} = W _ {K} ^ {i} H _ {\mathcal {I}} + b _ {K} ^ {i},
+$$
+
+$$
+V _ {\mathcal {I}} = W _ {V} ^ {i} H _ {\mathcal {I}} + b _ {V} ^ {i}, \tag {2}
+$$
+
+$$
+H _ {\mathcal {I}} ^ {i} = \mathrm {s o f t m a x} \left(\frac {Q _ {\mathcal {T}} K _ {\mathcal {I}} ^ {\top}}{\sqrt {d _ {k}}}\right) V _ {\mathcal {I}},
+$$
+
+where  $H_{\mathcal{T}}^{i}$  is the input hidden states of each LM Encoder layer and  $H_{\mathcal{I}}^{i}$  is the attended visual features. Then we can fuse  $H_{\mathcal{I}}^{i}$  with  $H_{\mathcal{T}}^{i}$  to attain the interplay representations for a meme:
+
+$$
+H _ {\mathcal {T}} ^ {i + 1} = \operatorname {L M E} ^ {i} \left(H _ {\mathcal {T}} ^ {i}\right) + W _ {O} ^ {i} H _ {\mathcal {I}} ^ {i} + b _ {O} ^ {i}, \tag {3}
+$$
+
+where  $\mathrm{LME}^i (\cdot)$  is the  $i$ -th layer of the LM Encoder,  $W_{*}^{i}$  denotes the linear projection,  $b_{*}^{i}$  is the bias, and  $\hat{H} = H_{T}^{L}$  is the final interplay representations after going through an L-layer LM Encoder fused with the visual features.
+
+Decoding Finally, we feed the interplay representations  $\hat{H} \in \mathbb{R}^{m \times d}$  into the LM Decoder, implemented as a Transformer-based decoder, to generate the reasonable rationale. Overall, the smaller language model  $f$  is trained by minimizing the following distillation loss:
+
+$$
+\mathcal {L} _ {\text {d i s t i l l}} = \operatorname {C E} (f (\mathcal {I}, \mathcal {T}), r), \tag {4}
+$$
+
+where  $\mathrm{CE}(\cdot)$  denotes the cross-entropy loss (Sutskever et al., 2014) between the predicted text and the target rationale  $r$  generated by LLMs. In this way, multimodal reasoning knowledge about the meme could be explicitly distilled from
+
+LLMs and injected into the smaller language model specific to harmful meme detection.
+
+# 3.3 Harmfulness Inference
+
+During the first fine-tuning stage, we conducted explicit deductive reasoning to empower our model with the capability of multimodal reasoning distilled from LLMs. As the goal of this task is to determine whether the meme is harmful or not, we conduct the second fine-tuning stage for Harmfulness Inference, which shares the same model architecture, parameters, and encoding procedure as Sec. 3.2 but differs in the decoding output. To make the output consistent with harmfulness prediction, the smaller model  $f$  is further trained by minimizing the following inference loss:
+
+$$
+\mathcal {L} _ {\text {i n f e r}} = \operatorname {C E} (f (\mathcal {I}, \mathcal {T}), y), \tag {5}
+$$
+
+where the cross-entropy loss is computed between the generated text and ground-truth harmfulness label  $y$ . With the generative objective (Raffel et al., 2020) adapted to the previous Reasoning Distillation stage, the prior reasoning knowledge absorbed in Reasoning Distillation could be well induced for Harmfulness Inference.
+
+Model Training The model training consists of two fine-tuning stages: 1) Reasoning Distillation and 2) Harmfulness Inference, where Reasoning Distillation is the predecessor fine-tuning phase of Harmfulness Inference. Note that for model testing, we directly input the test sample into our fine-tuned language model to predict the meme harmfulness.
+
+# 4 Experiments
+
+# 4.1 Experimental Setup
+
+Datasets We use three publicly available meme datasets for evaluation: (1) Harm-C (Pramanick et al., 2021a), (2) Harm-P (Pramanick et al., 2021b), and (3) FHM (Kiela et al., 2020). Harm-C and Harm-P consist of memes related to COVID-19 and US politics, respectively. FHM was released by Facebook as part of a challenge to crowd-source multimodal harmful meme detection in hate speech solutions. Different from FHM that each meme was labeled as harmful or harmless, Harm-C and Harm-P were originally labeled with three classes: very harmful, partially harmful, and harmless. For a fair comparison, we merge the very harmful and partially harmful memes into harmful ones, following the evaluation setting of recent work (Pramanick et al., 2021b; Cao et al., 2022). We provide
+
+<table><tr><td>Dataset</td><td colspan="2">Harm-C</td><td colspan="2">Harm-P</td><td colspan="2">FHM</td></tr><tr><td>Model</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td></tr><tr><td>Text BERT (Devlin et al., 2019)</td><td>70.17</td><td>66.25</td><td>80.12</td><td>78.35</td><td>57.12</td><td>41.52</td></tr><tr><td>Image-Region (He et al., 2016)</td><td>68.74</td><td>62.97</td><td>73.14</td><td>72.77</td><td>52.34</td><td>34.19</td></tr><tr><td>Late Fusion (Pramanick et al., 2021a)</td><td>73.24</td><td>70.25</td><td>78.26</td><td>78.50</td><td>59.14</td><td>44.81</td></tr><tr><td>MMBT (Kiela et al., 2019)</td><td>73.48</td><td>67.12</td><td>82.54</td><td>80.23</td><td>65.06</td><td>61.93</td></tr><tr><td>VisualBERT COCO (Li et al., 2019)</td><td>81.36</td><td>80.13</td><td>86.80</td><td>86.07</td><td>61.48</td><td>47.26</td></tr><tr><td>ViLBERT CC (Lu et al., 2019)</td><td>78.70</td><td>78.09</td><td>87.25</td><td>86.03</td><td>64.70</td><td>55.78</td></tr><tr><td>MOMENTA (Pramanick et al., 2021b)</td><td>83.82</td><td>82.80</td><td>89.84</td><td>88.26</td><td>61.34</td><td>57.45</td></tr><tr><td>MaskPrompt (Cao et al., 2022)</td><td>84.47</td><td>81.51</td><td>88.17</td><td>87.09</td><td>72.98</td><td>65.24</td></tr><tr><td>MR.HARM</td><td>86.16</td><td>85.43</td><td>89.58</td><td>89.57</td><td>75.40</td><td>75.10</td></tr></table>
+
+Table 1: Harmful meme detection results on three datasets. The accuracy and macro-averaged F1 score  $(\%)$  are reported as the metrics. The best and second results are in bold and underlined.
+
+statistics of the three datasets in the Appendix.
+
+Baselines We compare MR.HARM with several state-of-the-art harmful meme detection systems: 1) Text BERT (Devlin et al., 2019); 2) Image-Region (Ren et al., 2016; He et al., 2016); 3) Late Fusion (Pramanick et al., 2021a); 4) MMBT (Kiela et al., 2019); 5) VisualBERT COCO (Li et al., 2019; Lin et al., 2014); 6) ViL-BERT CC (Lu et al., 2019; Sharma et al., 2018); 7) MOMENTA (Pramanick et al., 2021b); 8) MaskPrompt (Cao et al., 2022). We use the accuracy and macro-averaged F1 score as the evaluation metrics. More implementation details and baseline descriptions are provided in Appendix.
+
+# 4.2 Harmful Meme Detection Performance
+
+Table 1 shows the performance of our proposed method versus all the compared methods on the Harm-C, Harm-P and FHM datasets. It is observed that 1) The performance of the baselines in the first group is obviously poor due to only unimodal features like text-only or image-only being captured. In comparison, the other baselines exploit the multimodal features from both the text and image in memes. 2) The multimodal models in the second group outperform the unimodal ones. The early-fusion models with multimodal pre-training (i.e., VisualBERT COCO and ViLBERT CC) outperform that of the simple fusion with unimodal pretraining (i.e., Late Fusion and MMBT) on Harm-C/P datasets, while MOMENTA performs best in the second group by considering global and local information of memes. 3) However, as the images in FHM dataset are more informative and high-quality, MaskPrompt yields the best performance among all the baselines by incorporating additional
+
+extracted entities and demographic information of the image into the masked language models, besides just captioning the image into the prompt.
+
+Our proposed MR.HARM improves over the best baselines by  $2.63\%$ ,  $1.31\%$ , and  $9.86\%$  in terms of Macro-F1 score on Harm-C, Harm-P, and FHM datasets, respectively. We observe that 1) the improvement on the Harm-P dataset is relatively milder than that on the other two datasets. Meanwhile, all the baselines just have tiny differences among their performances on Harm-P. We speculate the reason falls into the smaller dataset scale of Harm-P which only contains politics-related harmful memes. 2) A similar trend can also be observed in Harm-C and FHM datasets: the more challenging the dataset is, the greater performance improvement MR.HARM achieves. Our model performs flexibly and stably across all datasets with its keen judgment on harmful memes. This is because all the baselines are only designed at the recognition level, but MR.HARM is further empowered with multimodal reasoning knowledge distilled from LLMs to unearth harmful content from the seemingly uncorrelated text and image modalities of memes.
+
+# 4.3 Ablative Study
+
+We perform ablative studies on several variants of MR.HARM: 1) w/o Reasoning Distillation: Simply fine-tune the smaller language models in the stage of Harmfulness Inference without the stage of Reasoning Distillation based on LLMs; 2) w/o Visual Features: Discard the features from the meme image while keeping those from the meme text; 3) w/o Multimodal Fusion: Instead of the fusion mechanism on the multimodal features in our language model, we only append the lingual features from image captioning together with the meme text
+
+<table><tr><td>Dataset</td><td colspan="2">Harm-C</td><td colspan="2">Harm-P</td><td colspan="2">FHM</td></tr><tr><td>Model</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td></tr><tr><td>MR.HARM</td><td>86.16</td><td>85.43</td><td>89.58</td><td>89.57</td><td>75.40</td><td>75.10</td></tr><tr><td>w/o Reasoning Distillation</td><td>83.33</td><td>81.44</td><td>88.17</td><td>88.17</td><td>73.60</td><td>73.41</td></tr><tr><td>w/o Visual Features</td><td>82.48</td><td>80.30</td><td>87.04</td><td>87.03</td><td>58.80</td><td>57.01</td></tr><tr><td>w/o Multimodal Fusion</td><td>79.38</td><td>75.36</td><td>87.46</td><td>87.45</td><td>74.40</td><td>74.25</td></tr><tr><td>w/o Two-stage Training</td><td>83.05</td><td>81.45</td><td>63.32</td><td>63.32</td><td>67.40</td><td>65.77</td></tr><tr><td>w/o Fine-tuning Small LMs</td><td>71.75</td><td>66.86</td><td>61.13</td><td>60.27</td><td>60.00</td><td>57.72</td></tr></table>
+
+Table 2: Ablation studies on our proposed framework.
+
+during encoding; 4) w/o Two-stage Training: Concatenate the rationales generated from LLMs and golden harmfulness label as the target for model training, to replace the two-stage training paradigm; 5) w/o Fine-tuning Small LMs: Directly prompt the representative large language model ChatGPT based on InstructGPT (Ouyang et al., 2022) for harmful meme detection.
+
+As demonstrated in Table 2, the ablative models suffer different degrees of performance degradation, indicating the effectiveness of our proposed components for harmful meme detection with multimodal reasoning distilled from LLMs. Specifically, the performance of MR.HARM significantly decreases in the 'w/o Reasoning Distillation' setting due to the lack of multimodal reasoning knowledge transferred from LLMs about the seemingly uncorrelated modalities in memes. The 'w/o Visual Features' setting also achieves worse performance than MR.HARM, suggesting that the visual representations are complementary to the meme text for harm-indicative pattern extraction in the language model. MR.HARM makes improvements over 'w/o Multimodal Fusion', which implies the promoting role of our fusion mechanism that incorporates original vision features into the language model, hardly compromised when there could be severe information loss in the captioning process. Moreover, the 'w/o Two-stage Training' setting leads to large-margin performance degradation, which verifies the effectiveness of our two-stage training paradigm. This is because this setting causes mutual interference between intermediate reasoning and final prediction, which affects the convergence effect of harmfulness inference and damages the model's performance and stability. Compared with MR.HARM, the performance of 'w/o Fine-tuning Small LMs' also significantly decreases, highlighting the importance of abductive reasoning with LLMs to alleviate the hallucination issue during
+
+![](images/ade8947e9a4920f2c1a432447f0fe1b780fcfe99689fd6c50f51a83e36cb23cf.jpg)
+
+Meme text: DONT WORRY, AMERICA. THE CORONAVIRUS IS NOTHING COMPARED TO WINDMILL CANCER!
+
+Intermediate Reasoning: the meme contains a political message that promotes a political agenda. the text suggests that the coronavirus is nothing compared to windmill cancer, which is a harmful and offensive message. the image of a politician in a suit and tie, possibly related to the inauguration of president trump, further reinforces the harmful message. the use of a political message in this context could be seen as insensitive and disrespectful to those who have been affected by the pandemic.
+
+(a)
+
+![](images/5c084354b8374ddecdd7f8dac87e823659423799984fdd834b4bcc9475db9551.jpg)
+
+Memo text: THE DEMOCRAT PARTY AMERICAS OLDEST HATÉ GROUP
+
+Intermediate Reasoning: based on the text and image it appears that the meme is making an inflammatory statement about the democrat party. the image shows a close up of a confederate flag with stars, which could be interpreted as an implication of the democrat party. the text also mentions the oldest hate group in the united states, which could be interpreted as a reference to the democrat party being associated with racism and white supremacy.
+
+(b)
+
+![](images/fadd66780d7aa1a4fb0a059858e302cdb903fa8160a41e5010c071b4f4934ff9.jpg)  
+Figure 3: Examples of correctly predicted harmful memes in (a) Harm-C, (b) Harm-P, and (c) FHM dataset.
+
+Meme text: it's the goat humper, looks like a bunch of pigs just walked over your grave
+
+Intermediate Reasoning: the text in the meme contains a derogatory and offensive statement "goat humper" towards a particular group of Muslim individuals, associated with an image of a Muslim man praying in front of a mosque, which is highly offensive and discriminatory. The phrase "looks like a bunch of pigs just walked over your grave" can be interpreted as a disrespectful reference to the Muslim practice of abstaining from pork.
+
+(c)
+
+deductive reasoning for harmfulness prediction.
+
+# 4.4 Cognition-view Reasoning Analysis
+
+Note that our smaller language model is explicitly trained in the Reasoning Distillation stage for rationale generation to distill multimodal reasoning knowledge from LLMs. Although intermediate
+
+reasoning is not the final target output for harmful meme detection, after the first fine-tuning stage, we elicit reasonable thoughts from our smaller language model with the test samples as input, to understand the cognition view of our proposed MR.HARM on the test meme samples more transparently and intuitively, as exemplified in Figure 3.
+
+From the visualized intermediate reasoning, we observe that 1) our model could understand the multimodal information related to the meme text (in green) and image (in blue) with commonsense knowledge. For example, in Figure 3(a), the recognized "politician" in the image could be related to "president trump", which could be linked to the "AMERICA" in the text; in Figure 3(b), the recognized "flag" in the image could be cognized to satire "the democrat party" in the text; and in terms of Figure 3(c), the "goat humper" and "pigs" in the text could be associated with the attacks to "a Muslim man" recognized in the image. 2) Furthermore, our model learns to cognize the interplay (in pink) of multimodal information with advanced reasoning. Benefitting from the rich multimodal understanding of the memes, the perpetuates harmful stereotypes could be reasoned over the context to the target like "who affected by the pandemic" in Figure 3(a), "the democrat party" in Figure 3(b), and "the Muslim" in Figure 3(c). In this way, the rich correlation beneath the surface of the meme text and image could be excavated to facilitate harmfulness inference with better reasoning knowledge by harnessing advanced LLMs. Such readable pieces of rationales are also potentially valuable for aiding human checkers to verify the final answer predicted by our model.
+
+# 4.5 Error Analysis
+
+To better understand the behavior of our model and facilitate future studies, we conduct an error analysis on the wrongly predicted memes by our proposed framework. We found that the major error exists in that our framework still cannot fully recognize the images that require rich background knowledge though we exploited the advanced cross-attention mechanism by incorporating visual features into the language model. Figure 4 shows two examples of memes wrongly classified by MR.HARM. For the harmful meme in Figure 4(a), the phrase "and for my next class project!" suggests that the image is being used for an academic or educational purpose, which can be seen as glorifying
+
+![](images/51432446b0d24859200d657aa9b9eb31ad85b7ebfb626cad1840a40db7588e3e.jpg)  
+Meme text: and for my next class project!
+
+# Intermediate Reasoning:
+
+based on the image and text presented in the meme, it appears to be a harmless and relatable scenario. the image shows a group of women in traditional dress walking on a beach, which is a common and harmless activity. the text, "and for my next class project," seems to be a playful and lighthearted comment about the topic of the class project. there is no indication of any harmful or offensive content in the image or text.
+
+![](images/16b02c21d1d2eee790779b0cdc64c6bb4e8d7382a519803a4fbc8997ef02165a.jpg)
+
+![](images/1df81fb7221f06c5f233701cfd2b47b5bee8f1c1ee0c16b95ff4ef4a4885740f.jpg)  
+(a)  
+Memo text: in 1979Jimmy carter created the department of education since then the u.s. has gone from 1st to 17th in education  
+(b)  
+Figure 4: Examples of wrongly predicted memes by our proposed framework with the ground truth (a) harmful and (b) harmless.
+
+or normalizing the behavior depicted in the image. The image features "a group of Ku Klux Klan members walking on a beach", which is a symbol of white supremacy and racism. The combination of the phrase in the text and the use of imagery associated with hate groups can contribute to the glorification of harmful behaviors and the perpetuation of negative stereotypes, which makes the meme harmful. However, due to the lack of related background knowledge about the Ku Klux Klan members and their wear, our framework cannot well recognize the image correctly during the original vision feature extraction, which leads to error propagation for wrongly concluding that the meme is harmless. Also, in terms of the harmless meme in Figure 4(b), the image that "Jimmy Carter with a smile on his face" is mistakenly recognized as "an older man with a funny expression on his face", furthermore, the model hallucinates that the meme text "can be considered harmful and offensive to individuals who identify with the politician", resulting in the wrong prediction that the meme is harmful. Therefore, it is possible to improve MR.HARM by incorporating more informative vision features and improving language-vision interaction to be capable of understanding the images with more complex background knowledge.
+
+<table><tr><td>Version</td><td>Harm-C</td><td>Harm-P</td><td>FHM</td></tr><tr><td>Small</td><td>84.99</td><td>85.33</td><td>72.96</td></tr><tr><td>Base</td><td>85.43</td><td>89.57</td><td>75.10</td></tr><tr><td>Large</td><td>84.02</td><td>90.14</td><td>77.80</td></tr></table>
+
+Table 3: Macro-averaged F1 score  $(\%)$  achieved with different versions of our fine-tuned LMs.
+
+# 4.6 Discussion
+
+As our two-stage training paradigm requires distilling the reasoning knowledge and leveraging original vision features, we utilize the T5 encoder-decoder architecture (Raffel et al., 2020; Chung et al., 2022) to initialize our generative framework. To test the generality of the benefits of our approach to different versions of the backbone, we alter the underlying LMs to other variants in different sizes. As shown in Table 3, one interesting phenomenon is that our model has already achieved outstanding performance on the three benchmarks with the Small (about 60M parameters) or Base ((about 220M parameters)) version as the backbone, which has a smaller size than the state-of-the-art baseline MaskPrompt (over 300M parameters). The Large version of our backbone generally achieved better performance than the other two backbone versions because the larger the fine-tuned LMs, the more it alleviates the hallucination issue (Ji et al., 2023). Overall, the results show that our framework does not rely excessively on the size of the backbone to improve performance and is generally effective with different versions of the backbone model.
+
+# 5 Conclusion and Future Work
+
+In this paper, we propose to capture implicit meaning that is not explicitly conveyed through the surface of the text and image in memes for harmful meme detection. We first conduct abductive reasoning with LLMs. Then we present a novel generative framework to distill multimodal reasoning knowledge from LLMs, which includes two training stages: 1) reasoning distillation and 2) harmfulness inference. Results on three meme benchmarks confirm the advantages of our proposed framework. For future work, since it is harder to judge the quality of the intermediate reasoning, where the evaluation is necessarily qualitative, we plan to do some sort of systematic study towards explainable harmful meme detection to claim explainability through a human subjects study for evaluation.
+
+# Limitations
+
+There are multiple ways to further improve this work:
+
+- Despite this work focusing on performance improvement of harmful meme detection, it is harder to judge the quality of the intermediate reasoning, where the evaluation is necessarily qualitative. Considering that our framework could generate readable snippets for cognition-view reasoning, we plan to do some sort of systematic study to claim explainability for the evaluation, which would be another more targeted research.  
+- New benchmarks to evaluate the reasoning ability of our framework are demanded. We are going to further exploit LLMs toward explainable harmful meme detection from the perspectives like dataset construction and automatic evaluation.  
+- We only use the textual prompt to conduct abductive reasoning with accessible LLMs pretrained with the language modality. We would further update our framework by leveraging visual LLMs if accessible in the future to improve the visual feature extraction for exploring better multimodal reasoning knowledge distillation, and avoid several common deficiencies of existing language models including hallucination and limited generalization as much as possible.
+
+# Acknowledgements
+
+This work was partially supported by Hong Kong RGC ECS (Ref. 22200722) and National Natural Science Foundation of China Young Scientists Fund(No.62206233).
+
+# Broader Impact
+
+The purpose of this work is to prevent the spread of harmful meme information and to ensure that people are not subjected to prejudice, racial and gender discrimination. Nevertheless, we are aware of the potential for malicious users to reverse-engineer and create memes that go undetected or misunderstood by MR.HARM-trained AI systems. This is strongly discouraged and condemned. Intervention with human moderation would be required in order to ensure that this does not occur.
+
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+
+<table><tr><td rowspan="2">Datasets</td><td colspan="2">Train</td><td colspan="2">Test</td></tr><tr><td>#harmful</td><td>#harmless</td><td>#harmful</td><td>#harmless</td></tr><tr><td>Harm-C</td><td>1064</td><td>1949</td><td>124</td><td>230</td></tr><tr><td>Harm-P</td><td>1486</td><td>1534</td><td>173</td><td>182</td></tr><tr><td>FHM</td><td>3050</td><td>5450</td><td>250</td><td>250</td></tr></table>
+
+# A Datasets
+
+The detailed statistics of the three datasets are shown in Table 4.
+
+# B Implementation Details
+
+To separate the text and image in the memes, we first in-paint the memes by combining MMOCR (Kuang et al., 2021) with SAM (Kirillov et al., 2023) to extract the text and pure image. Then during the captioning process, since the focus of this work is primarily on the multimodal reasoning for harmful meme detection from a fresh perspective on harnessing LLMs, we apply a pretrained image captioning model ClipCap (Mokady et al., 2021) used in recent work (Cao et al., 2022), to generate textual descriptions about the dominant objects or events in the memes' image, which is utilized as the inputs into LLMs for abductive reasoning. To generate the rationale for each meme, we employed ChatGPT (Ouyang et al., 2022), a widely used LLM developed by OpenAI, specifically utilizing the "gpt-3.5-turbo" version. To make our results reproducible, we set the temperature as 0 and the maximum length as 256.
+
+For the system prompt to the "gpt-3.5-turbo" model, we design the message as:
+
+"You have been specially designed to perform abductive reasoning for the harmful meme detection task. Your primary function is that, according to a harmfulness label about an image with a text embedded, please provide a streamlined rationale, without explicitly indicating the label, for how it is reasoned as the given harmfulness label. The image and the textual content in the meme are often uncorrelated, but its overall semantics is presented holistically. Thus it is important to note that you are prohibited from relying on your own imagination, as your goal is to provide the most accurate and reliable rationale possible so that people can infer the harmfulness according to your reasoning about the background context and relationship between the given text and image."
+
+Moreover, to prompt the LLMs to generate rea
+
+Table 4: Statistics of Datasets.  
+
+<table><tr><td>Hyper-Parameter</td><td>Harm-C</td><td>Harm-P</td><td>FHM</td></tr><tr><td colspan="4">First-Stage</td></tr><tr><td>epoch</td><td>10</td><td>10</td><td>10</td></tr><tr><td>batch size</td><td>32</td><td>32</td><td>32</td></tr><tr><td>Learning Rate</td><td>5e-5</td><td>5e-5</td><td>5e-5</td></tr><tr><td>Warmup Step</td><td>0.1</td><td>0.1</td><td>0.1</td></tr><tr><td>Warmup Strategy</td><td>Linear</td><td>Linear</td><td>Linear</td></tr><tr><td>Image Size</td><td>224</td><td>224</td><td>224</td></tr><tr><td colspan="4">Second-Stage</td></tr><tr><td>epoch</td><td>30</td><td>30</td><td>30</td></tr><tr><td>batch size</td><td>32</td><td>32</td><td>32</td></tr><tr><td>Learning Rate</td><td>5e-5</td><td>5e-4</td><td>1e-4</td></tr><tr><td>Warmup Step</td><td>0.1</td><td>0.1</td><td>0.1</td></tr><tr><td>Warmup Strategy</td><td>Linear</td><td>Linear</td><td>Linear</td></tr><tr><td>Image Size</td><td>224</td><td>224</td><td>224</td></tr></table>
+
+Table 5: Hyper-parameters.
+
+sonable rationales with the triplet  $\{y,\tilde{\mathcal{I}},\mathcal{T}\}$  as observed attributes, we design the template  $p$  for the user prompt as:
+
+"Given a Text: [T], which is embedded in an Image: [I]; and a harmfulness label [y], please give me a streamlined rationale associated with the meme, without explicitly indicating the label, for how it is reasoned as [y].".
+
+Our MR.HARM model utilizes the T5 encoder-decoder architecture (Raffel et al., 2020; Chung et al., 2022) as its foundational framework, specifically utilizing the "flan-t5-base" version. For the extraction of image features, following previous work (Pramanick et al., 2021b), we adopted the state-of-the-art vision Transformer known as CLIPViT-B/32 (Radford et al., 2021), and this module remains static throughout the training process. To effectively integrate the multi-modal information, we incorporated a simple one-head cross-attention mechanism in each layer of the T5 encoder. During the fusion process, the text features are utilized as the query, while the image features act as the key and value. It is noteworthy that these fusion modules were initialized randomly. For the finetuning phase, we provide a comprehensive list of the hyper-parameters in Table 5. Results are averaged over ten random runs. All experiments were conducted using a single V100 32GiB GPU.
+
+# C Baselines
+
+We compare our model MR.HARM with several state-of-the-art harmful meme detection systems:
+
+<table><tr><td>Dataset</td><td colspan="2">Harm-C</td><td colspan="2">Harm-P</td><td colspan="2">FHM</td></tr><tr><td>Version</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td></tr><tr><td>Small</td><td>85.59</td><td>84.99</td><td>85.35</td><td>85.33</td><td>73.20</td><td>72.96</td></tr><tr><td>Base</td><td>86.16</td><td>85.43</td><td>89.58</td><td>89.57</td><td>75.40</td><td>75.10</td></tr><tr><td>Large</td><td>85.03</td><td>84.02</td><td>90.14</td><td>90.14</td><td>78.20</td><td>77.80</td></tr></table>
+
+Table 6: The detailed results with different sizes of our fine-tuned LMs.  
+
+<table><tr><td>Models</td><td>MOMENTA</td><td>MaskPrompt</td><td>MR.HARM</td></tr><tr><td>Multimodal Fusion</td><td>✓</td><td>✗</td><td>✓</td></tr><tr><td>Prompt Tuning</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Explicit Reasoning</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Leveraging LLMs</td><td>✗</td><td>✗</td><td>✓</td></tr></table>
+
+Table 7: Comparison of characteristics between our MR.HARM with state-of-the-art models for harmful meme detection.
+
+1) Text BERT: BERT (Devlin et al., 2019) is utilized as the unimodal text-only model; 2) ImageRegion: a unimodal visual-only model that processes meme images using Faster R-CNN (Ren et al., 2016) with ResNet-152 (He et al., 2016) to feed into a classification layer; 3) Late Fusion: a multimodal model uses the average prediction scores of BERT and ResNet-152 for harmful meme detection (Pramanick et al., 2021a); 4) MMBT: a multimodal Bi-Transformer (Kiela et al., 2019) that captures the intra-modal and inter-modal dynamics of the two modalities; 5) VisualBERT COCO: Visual BERT (Li et al., 2019) pre-trained on the COCO dataset (Lin et al., 2014); 6) ViLBERT CC: Vision and Language BERT (Lu et al., 2019) trained on an intermediate multimodal objective (Sharma et al., 2018) for task-agnostic joint representations of image and text; 7) MOMENTA: a multimodal harmful meme detection system (Pramanick et al., 2021b) that takes the global and local information in two modalities of memes into account; 8) MaskPrompt: a prompt learning approach (Cao et al., 2022) that converts harmful meme detection as a masked language modeling problem based on RoBERTa-large (Liu et al., 2019). We use accuracy and macro-averaged F1 score as the evaluation metrics, where the macro-averaged F1 is the more important metric owing to the imbalanced class prevalence (see Table 4), to capture competitive performance beyond the majority class.
+
+While LLMs offer strong zero/few-shot performance as shown in the 'w/o Fine-tuning Small
+
+![](images/e6c73a9547dba5ae996866b6d39194914410bc7b5a84aaf7ec102843413c4980.jpg)  
+Figure 5: The details of our Multimodal Fusion module.
+
+LMs' setting in Table 2, they are challenging to serve in practice that requires at least 350GB GPU memory using specialized infrastructure for a single 175 billion LLM. This work presents a novel paradigm to leverage the reasoning ability and rich background knowledge of LLMs for better harmful meme detection, but just need to fine-tune the small language model even with a smaller size than the state-of-the-art baseline. Table 6 illustrates the detailed results on the three meme datasets with different versions of our fine-tuned backbone model. Table 7 illustrates the comparison of characteristics between MR.HARM and the state-of-the-art baselines like MOMENTA and MaskPrompt.
+
+# D Illustration of Multimodal Fusion
+
+Figure 5 illustrates the details of our multimodal fusion module in the encoding phase of MR.HARM.
+
+# E Discussion about One-stage Training
+
+We further investigate the one-stage training to figure out the intrinsic property of the chain-of-thought reasoning. We compare the performance with two proposed variants for the one-stage training: 1) Explanation where the rationale is utilized for explaining the harmfulness inference; 2) Reasoning where harmfulness inference is conditioned
+
+<table><tr><td>Dataset</td><td colspan="2">Harm-C</td><td colspan="2">Harm-P</td><td colspan="2">FHM</td></tr><tr><td>Model</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td><td>Accuracy</td><td>Macro-F1</td></tr><tr><td>Explanation</td><td>83.05</td><td>81.45</td><td>63.32</td><td>63.32</td><td>67.40</td><td>65.77</td></tr><tr><td>Reasoning</td><td>68.93</td><td>56.19</td><td>56.90</td><td>56.67</td><td>63.00</td><td>59.29</td></tr></table>
+
+Table 8: Effects of the one-stage training.
+
+to the rationale. As shown in Tabel 8, the reasoning setting performs worse than the explanation setting with a large margin. We conjecture that this is because the reasoning setting in the one-stage training could lead to error propagation if our small language model generates hallucinated rationales that mislead the harmfulness inference, which however could be well avoided by the two-stage training paradigm. Meanwhile, as there exists mutual interference between rationale generation and harmfulness prediction, the explanation setting could give the harmfulness inference higher priority in the sequence generation so that it performs better than the reasoning setting. We argue that such a one-stage training paradigm could be improved in the future by applying a filtering mechanism, e.g., using only the effective chain-of-thought reasoning to infer the harmfulness of memes and get rid of irrelevant rationales. In summary, both settings in the one-stage training paradigm suffer different degrees of performance degradation, which reaffirms the necessity of our two-stage training paradigm.
+
+# F Future Work
+
+We will explore the following directions in the future:
+
+- Considering that our framework could generate readable snippets for cognition-view reasoning, we plan to do some sort of systematic study to claim explainability (possibly through a human subjects study) for the evaluation.  
+- In this work we target exploring the underlying reasoning process to empower the harmful meme detection model with the ability of explicit reasoning, to arrive at correct harmfulness predictions. We are going to further exploit LLMs toward explainable harmful meme detection from perspectives like dataset construction on social media with propagation structure (Lin et al., 2021; Ma and Gao, 2020;
+
+Ma et al., 2020), automatic evaluation, and human evaluation.
+
+- We would further update our framework by leveraging visual LLMs if accessible in the future to improve the visual feature extraction for better multimodal reasoning, and avoid several common deficiencies of existing language models including hallucination and limited generalization as much as possible.

2023/Beneath the Surface_ Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models/images.zip

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2023/Better Together_ Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information/d96254f1-428d-46b8-a6d6-93195d523d44_content_list.json

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+    {
+        "type": "text",
+        "text": "Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information",
+        "text_level": 1,
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+        "text": "Alla Chepurova<sup>1</sup> Aydar Bulatov<sup>1</sup> Yuri Kuratov<sup>1,2</sup> Mikhail Burtsev<sup>3</sup>",
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+        "text": "$^{1}$ Neural Networks and Deep Learning Lab, MIPT, Dolgoprudny, Russia  $^{2}$ AIRI, Moscow, Russia",
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+        "text": "$^{3}$ London Institute for Mathematical Sciences, London, UK\nchepurova@deeppavlov.ai, {bulatov.as, yurii.kuratov}@phystech.edu, mb@lims.ac.uk",
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+        "type": "text",
+        "text": "Abstract",
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+        "type": "text",
+        "text": "Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential performance. Knowledge Graph Completion (KGC) techniques aim to address this issue. However, traditional KGC methods are computationally intensive and impractical for large-scale KGs, necessitating the learning of dense node embeddings and computing pairwise distances. Generative transformer-based language models (e.g., T5 and recent KGT5) offer a promising solution as they can predict the tail nodes directly. In this study, we propose to include node neighborhoods as additional information to improve KGC methods based on language models. We examine the effects of this imputation and show that, on both inductive and transductive Wikidata subsets, our method outperforms KGT5 and conventional KGC approaches. We also provide an extensive analysis of the impact of neighborhood on model prediction and show its importance. Furthermore, we point the way to significantly improve KGC through more effective neighborhood selection.",
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+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
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+        "text": "Knowledge graphs (KG) represent structured information in the form of a directed multi-relational graph with entities as the graph's nodes and relations between entities as the graph's edges. Entities in such graphs could be both real-world objects or abstract concepts. KGs are represented as a set of triplets with representation entities and a relation between them, i.e. (head entity, relation, tail entity) or (h, r, t) for short. Due to KGs' recent rapid development, they are now widely used in a variety of applications, including data mining, information retrieval, question answering, and recommendation systems. However, most real-world knowledge graphs suffer from incompleteness. Knowledge graph completion (KGC) algorithms attempt to fill in the gaps in the KG in order to solve this problem.",
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+        "image_caption": [
+            "Figure 1: Graph completion pipeline. We extract and verbalize neighborhood of a triplet head node from the Knowledge Graph (KG). Language Model uses verbalized input triplet and neighborhood to make predictions. Predictions can be used for question answering or KG completion."
+        ],
+        "image_footnote": [],
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+        "type": "text",
+        "text": "KG link prediction commonly involves learning knowledge graph embeddings (KGE), which capture structural and semantic information about KG nodes, relations, and their neighborhoods in a way that a simple distance-based function would be able to distinguish real triplets from false ones. However, building a unique embedding for each node and relation leads to a linear increase in model size and inference time as the KG size grows. Another drawback is that KG updating requires full re-training of KGEs, rather than simply learning new embeddings for updated entities or relations.",
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+        "type": "text",
+        "text": "Application of language models (LMs) to KGC tasks is a promising direction (Pan et al., 2023). State-of-the-art performance in most natural language processing (NLP) tasks has recently been demonstrated by pre-trained language models like BERT (Devlin et al., 2019) and more recent large or foundational language models (Brown et al., 2020; Chowdhery et al., 2022; Touvron et al., 2023). Language models-based approaches, such as KG-BERT (Yao et al., 2019), BERTRL (Zha et al., 2022), KGT5 (Saxena et al., 2022) and GenKGC (Xie et al., 2022) consider a triplet in the KG as a text sequence. While distance-based",
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+        "text": "5306",
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+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5306-5316 December 6-10, 2023 ©2023 Association for Computational Linguistics",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "methods incorporate more structural information in dense embeddings, language models tend to capture more semantic information contained in both relations and entities of KG, enabling such models to embed previously unseen entities and operate on new facts based on semantic information contained in triplets textual representation. Thus, KGT5 positions KG link prediction as sequence-to-sequence tasks. KGT5 model trained to generate target node using textually verbalized triplets achieves competitive performance and offers a scalable and simple framework for KGC and QA tasks.",
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+        "type": "text",
+        "text": "Current methods based on language models (KG-BERT, BERTRL, KGT5, and GenKGC) rely only on information that can be extracted from LMs parameters. However, since KG itself is available, we build a method that relies on both internal language model knowledge and external information from KG. For the link prediction task  $(h,r,\\ref{eq:1})$ , we propose to extract nodes and relations adjacent to  $h$  from the KG, i.e. the neighborhood. Node  $h$  with its neighborhood better together represents  $h$  itself: neighborhood may contain clues such as Palo Alto and United States of America for the triplet (Steve Jobs, place of birth, ?) on Figure 1.",
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+        "text": "Though some efforts have been made to incorporate neighborhood into the KGC algorithms (Chen et al., 2020), our proposed pipeline inherits the benefits of generative LLMs compared to KGE approaches: (1) scalability and size of the model, (2) applicability to both transductive and inductive KGs due to an ability to generalize unseen entities, (3) no need to rank all possible candidate triplets due to direct generation of tail entity. Also, a concurrent work by the KGT5 authors, KGT5-context (Kochsiek et al., 2023), proposed a similar idea of integrating node neighborhood in the context of the generative LM model, supporting the main concerns and results of our study.",
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+        "text": "Our contributions are as follows: (1) we propose a generative graph completion method which effectively integrates language models with graph neighborhood information. (2) We show that incorporating graph information into multiple generative language models achieve strong performance improvement of link prediction in both transductive and inductive settings. Moreover, our approach achieves state-of-the-art results on the inductive ILPC dataset. (3) We analyze and evaluate the impact of adding neighborhood information on the performance of the models.",
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+        "text": "2 Methods",
+        "text_level": 1,
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+        "type": "text",
+        "text": "One can represent KG more formally as  $\\mathcal{G} = (\\mathcal{T},\\mathcal{E},\\mathcal{R})$ , where  $\\mathcal{E}$  is a set of entities,  $\\mathcal{R}$  is a set of relations and  $\\mathcal{T}$  is a set of triplets  $\\mathcal{T} \\in \\mathcal{E} \\times \\mathcal{R} \\times \\mathcal{E}$ . Therefore, the formulation of the KGC task is the following: for each triplet  $\\mathcal{T}_i = (h,r,t)$ , where  $h \\in \\mathcal{E}, t \\in \\mathcal{E}, r \\in \\mathcal{R}$ , answer both  $(h,r,?)$  and  $(?,r,t)$  queries on the graph  $\\mathcal{G} / \\mathcal{T}_i$ . The KGC tasks can have different variations depending on the KG type: inductive and transductive. In a transductive setting the KGC task is formulated as follows: given a train KG  $\\mathcal{G}_{train} = (\\mathcal{T}_{train},\\mathcal{E},\\mathcal{R})$  train the model to predict on the inference KG  $\\mathcal{G}_{inference} = (\\mathcal{T}_{inference},\\mathcal{E},\\mathcal{R})$ , therefore in the train and inference KG there is a same set of entities and relations. In inductive KGC the model is trained on the KG  $\\mathcal{G}_{train} = (\\mathcal{T}_{train},\\mathcal{R},\\mathcal{E}_{train})$  and predicts the inference KG  $\\mathcal{G}_{inference} = (\\mathcal{T}_{inference},\\mathcal{R},\\mathcal{E}_{inference})$ , so the trained model observed only on the entities from train subgraph of KG.",
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+        "type": "text",
+        "text": "We followed the KGT5 (Saxena et al., 2022) approach to the KGC task using an encoder-decoder Transformer based on T5 (Raffel et al., 2020). The link prediction task was posed as follows: for each triplet  $(entity_1, relation, entity_2)$  from KG we had to predict the tail in the queries  $(entity_1, relation, ?)$  and  $(entity_2, relation^{-1}, ?)$ . To generate input for the model, we converted such queries into a text form. Apart from the query verbalization only as in KGT5, we formed a neighborhood for the node and relations for each triplet in KG. Thus, verbalized queries and their neighborhoods in the form of text were fed to the input of the model for training (see Figure 1) and later to predict incomplete triplets. We form a neighborhood of nodes that have an edge with the triplet head node (1-hop neighbors). The number of adjacent nodes may be too large to fit into the context size of selected language models. Therefore, we sort them based on relation semantic similarity. We take only those verbalized nodes and relations that fit into the max sequence length. We provide more details on neighborhood formation in Appendix B and verbalization in Appendix C.",
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+        "type": "text",
+        "text": "To evaluate our approach to link prediction tasks we used publically available transductive and inductive KGs. Specifically, we used the Wiki-data5M (Wang et al., 2021) dataset, which is one of the largest publicly available transductive KG up-to-date and large and small versions of ILPC",
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+        "type": "text",
+        "text": "dataset (Galkin et al., 2022), the largest fully inductive benchmark. ILPC inference graph size is challenging for modern GNN methods with inductive capabilities (Galkin et al., 2022). Apart from being challenging to solve by common approaches, the ILPC KGs are much more sparse than the transductive Wikidata5M. The graph size and sparseness of the data make the task hard for existing methods and result in modest performance metrics. Still, there were no existing solutions that beat the baseline provided in the ILPC paper. Both used datasets are the subsets of the real-world KG - Wikidata. Datasets' statistics are provided in the Table 4).",
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+    {
+        "type": "text",
+        "text": "We compared models with and without neighborhoods to evaluate our hypothesis that neighborhoods help to better together represent graph node. In addition, unlike the KGT5, we experimented with different model configurations, both fine-tuning an existing pre-trained model and training from scratch as common knowledge obtained during language models pre-training might be useful on real-world knowledge graphs like Wikidata.",
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+    {
+        "type": "text",
+        "text": "We used the T5-small with 60M parameters as our base model with both pretrained and randomly initialized weights to evaluate the effect of pretraining of the language model on the KG task. We added additional \"[SEP]\" tokens to the tokenizer with 30K tokens vocabulary to separate query and neighborhood. The model was trained using AdamW optimizer (Loshchilov and Hutter, 2019), 1e-5 learning rate,  $10\\%$  dropout, and batch size equals 320. The number of training steps was equal to 5M for the Wikidata5M dataset and 2M for all others. For multi-gpu training, we used the Horovod parallelization framework (Sergeev and Balso, 2018). In cases where a batch size equal to 320 did not fit GPU, we used gradient accumulation steps. We also benchmarked OpenAI gpt-3.5-turbo<sup>1</sup> zero-shot link prediction and evaluate the benefits of a neighborhood on LLM (see Appendix G). The code for all the described experiments is available at the link<sup>2</sup>.",
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+    {
+        "type": "text",
+        "text": "As KGT5, our model produces a probability distribution over all possible tokens in the vocabulary at each step of decoding. Such distribution is penalized for not matching the actual distribution, i.e., for the difference between the real target token at position  $i$  and the generated token at step  $i$  using",
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+    {
+        "type": "text",
+        "text": "CE loss. Such an approach is principally different from distance-based methods as it does not require generating negative samples to make them further from the target prediction for each training query. In this way, training iteration becomes independent of the KG size.",
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+    {
+        "type": "text",
+        "text": "To form predictions, we first verbalize the query (head, relation,?) and pass it as an input to the generative model to produce an output sequence, which will be a verbalization of the target entity id. For inference, we employed two strategies. The first one was sampling  $k$  most probable sequences from the decoder. We follow the KGT5 (Saxena et al., 2022) setting and use a sample size of 50. We apply this approach to Wikidata5M datasets as it is transductive. The second option for inference was to search for the closest entities in KG to the generated one. We apply the second approach to ILPC inductive datasets as language models have to generate previously unseen entities. More details on inference procedure are in Appendix D.",
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+    {
+        "type": "text",
+        "text": "3 Results",
+        "text_level": 1,
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+        "text": "The hypothesis behind our approach was that neighborhood information incorporated in a context fed to the language model should enhance link prediction of generative LM. Tables 1 and 2 demonstrate the performance of our technique on Wikidata5M and ILPC datasets under various conditions, including training a T5 model with and without neighbors, using a pre-trained model or training a model from scratch, and zero-shot with gpt-3.5-turbo.",
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+        "type": "text",
+        "text": "These results support the hypothesis that the quality of link prediction increases with the use of neighbors in the context of the generative model in both transductive and inductive settings. Moreover, our approach demonstrated SOTA performance on the ILPC dataset, while on Wikidata we achieved the best result among generative link prediction models, which is still comparable to scores of SOTA graph and LM methods with many more parameters. Relatively small absolute values of scores on the ILPC dataset can be explained by its fully inductive setup and larger sparseness compared to Wikidata5M. Still, there are no publicly available solutions that exceed the baseline score provided by ILPC authors. Our solution overperforms the existing baseline, is elegantly simple, and more interpretable than GNN-based solutions. Experiments on pre-trained models revealed that pre-training speeds up convergence but degrades",
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+        "type": "page_footnote",
+        "text": "$^{1}$ gpt-3.5-turbo-0613: https://platform.openai.com/docs/models/gpt-3-5  \n $^{2}$ https://github.com/screemix/kgc-t5-with-neighbors",
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+    {
+        "type": "page_number",
+        "text": "5308",
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+    {
+        "type": "table",
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+        "table_caption": [
+            "Table 1: Adding a neighborhood persistently improves results on the link prediction task. We report Hits@k metrics on Wikidata5M, ILPC-small, and ILPC-large test sets. Arrows  $\\uparrow\\downarrow$  show the effect of the neighborhood on language models. Neighbors allow to achieve the best performance considering the number of model parameters."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">MODEL</td><td colspan=\"3\">WIKIDATA5M</td><td colspan=\"3\">ILPC-SMALL</td><td colspan=\"3\">ILPC-LARGE</td><td rowspan=\"2\">#PARAMS</td></tr><tr><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td></tr><tr><td>KGT5 (SAXENA ET AL., 2022)</td><td>0.267</td><td>0.318</td><td>0.365</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>TRANSE (BORDES ET AL., 2013)</td><td>0.170</td><td>0.311</td><td>0.392</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2,400M</td></tr><tr><td>COMPLEX (TROULLON ET AL., 2016)</td><td>0.255</td><td>-</td><td>0.398</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>614M</td></tr><tr><td>KEPLER (WANG ET AL., 2021)</td><td>0.173</td><td>0.224</td><td>0.277</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>125M</td></tr><tr><td>SIMKGC (WANG ET AL., 2022)</td><td>0.313</td><td>0.376</td><td>0.441</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>220M</td></tr><tr><td>INDNODEPIECE (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.007</td><td>0.022</td><td>0.092</td><td>0.037</td><td>0.054</td><td>0.125</td><td>15.5K</td></tr><tr><td>INDNODEPIECEGNN (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.076</td><td>0.140</td><td>0.251</td><td>0.032</td><td>0.073</td><td>0.146</td><td>24K</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED</td><td>0.240</td><td>0.286</td><td>0.323</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED + NEIGHBORS</td><td>0.327↑</td><td>0.363↑</td><td>0.386↑</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED</td><td>0.205</td><td>0.248</td><td>0.282</td><td>0.073</td><td>0.117</td><td>0.177</td><td>0.112</td><td>0.137</td><td>0.194</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED + NEIGHBORS</td><td>0.306↑</td><td>0.342↑</td><td>0.361↑</td><td>0.076↑</td><td>0.137↑</td><td>0.209↑</td><td>0.114↑</td><td>0.139↑</td><td>0.200↑</td><td>60M</td></tr><tr><td>T5-BASE PRE-TRAINED</td><td>-</td><td>-</td><td>-</td><td>0.073</td><td>0.130</td><td>0.199</td><td>0.110</td><td>0.136</td><td>0.198</td><td>220M</td></tr><tr><td>T5-BASE PRE-TRAINED + NEIGHBORS</td><td>-</td><td>-</td><td>-</td><td>0.082↑</td><td>0.142↑</td><td>0.216↑</td><td>0.116↑</td><td>0.141↑</td><td>0.215↑</td><td>220M</td></tr></table>",
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+    {
+        "type": "table",
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+        "table_caption": [
+            "Table 2: Neighbor information improves zero-shot link prediction with OpenAI gpt-3.5-turbo-0613. *ILPC-Large results were obtained on 5000 random samples from the test set."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">DATASET</td><td colspan=\"2\">GPT</td><td colspan=\"2\">GPT+NEIGH</td></tr><tr><td>H@1</td><td>H@3</td><td>H@1</td><td>H@3</td></tr><tr><td>WIKIDATA5M</td><td>0.098</td><td>0.131</td><td>0.113↑</td><td>0.157↑</td></tr><tr><td>ILPC-SMOTE</td><td>0.079</td><td>0.107</td><td>0.084↑</td><td>0.118↑</td></tr><tr><td>ILPC-LARGE*</td><td>0.103</td><td>0.134</td><td>0.114↑</td><td>0.150↑</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "the quality of the model by a few points compared to the one trained from scratch.",
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+        "type": "text",
+        "text": "Also, we demonstrated that neighbors' information enhances performance across different generative models. This includes open-source models such as T5, as well as proprietary models like OpenAI GPTs that are distributed as a model-as-a-service. Although both types of models have their own advantages and limitations, it should be noted that OpenAI GPTs are black box models that cannot be modified or interpreted further. Usage of OpenAI GPTs is restricted in the case of more specialized or proprietary KGs that can store secure or non-public information. In the context of our study, OpenAI's gpt-3.5-turbo has a much larger size and cost, compared to smaller and transparent T5, still our approach improves the performance of both models.",
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+    {
+        "type": "text",
+        "text": "To get an understanding of how proposed models learn to use neighboring triplets we compared predictions of models trained with and without graph information in the input. One possible case when neighbors help in link prediction is when a target entity is somehow mentioned in the model input. Because of neighborhood filtering, the target triplet never appears in the model input, however in some cases target entity can occur with different relations",
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+    {
+        "type": "text",
+        "text": "or as a substring of another entity in a neighborhood or even in the task itself (Table 3). Despite the tail appearing in input only in a small portion of the test set, the neighborhood model learns to make use of such hints, outperforming the baseline T5 in all selected cases. The high solution rate of more than 4 out of 5 samples with hinted targets supports the importance of properly selected neighborhoods.",
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+    {
+        "type": "image",
+        "img_path": "images/5698b3c281989d6aa2353541bb11b453758af5dddfb1167abd98b62546208800.jpg",
+        "image_caption": [
+            "Figure 2: KG neighborhood is critical for the model performance. The plot shows how the quality of link prediction depends on the number of neighborhood triplets removed from the input context Wikidata5M T5-small not pre-trained model. The blue solid line corresponds to removing the most relevant triplets and the dashed red line represents random selection."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To evaluate the impact of neighborhoods, we analyzed triplets' importance by sorting them based on how they affect the probability of the target node prediction. As Figure 2 shows, a performance of the model quickly deteriorates with the removal of important triplets from the context. This implies potential avenues for improvement by prioritizing significant triplets selection prior to model training. Moreover, we analyzed attention maps of the model train on Wikidata5M and found that it indeed pays attention to relevant neighbors, e.g. Appendix F.",
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+    },
+    {
+        "type": "text",
+        "text": "The analysis shows that adding relevant graph",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "5309",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/44aa4c813723da2c78153b8d920bb89837ac5bf36c49d54eb0ac363c450af5da.jpg",
+        "table_caption": [
+            "Table 3: Adding relevant neighbors improves link prediction for T5-small on the Wikidata test set. The table shows the exact match depending on a hint of the target entity's appearance as a part of the model input."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Target position</td><td>T5</td><td>T5 + Neighbors</td><td>frequency</td></tr><tr><td>In task</td><td>0.93</td><td>0.94↑</td><td>5 %</td></tr><tr><td>In neighborhood</td><td>0.34</td><td>0.82↑</td><td>8 %</td></tr><tr><td>Not in input</td><td>0.18</td><td>0.24↑</td><td>87 %</td></tr><tr><td>Total</td><td>0.23</td><td>0.32↑</td><td>100 %</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "context information greatly enhances link prediction. Hence one way of improving performance is increasing neighborhood size by adding k-hop neighbors. Due to the quadratic complexity of attention and the limited input size of pretrained transformers, processing exponentially growing neighborhoods would be computationally expensive. To deal with large input sizes one can use efficient T5 architectures with input sizes up to 32k (Guo et al., 2022) and 64k (Ainslie et al., 2023) tokens or recurrent approaches (Bulatov et al., 2022; Hutchins et al., 2022) that can sequentially process effectively unlimited neighborhood sizes. Another potential way of subgraph selection is to add relevant but more distant knowledge graph information, such as random walks or more sophisticated methods (Das et al., 2018).",
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+    {
+        "type": "text",
+        "text": "4 Conclusions and Future work",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Our study found that incorporating KG neighborhood information into a generative model's context can enhance the quality of the link prediction task. We trained the T5-small model on transductive Wikidata5M and inductive ILPC datasets with various configurations and showed that coupling the node and its neighborhood information together better represents the node. Our approach significantly outperformed baselines on the largest real-world KGCs, with a comparable or fewer number of parameters. We also demonstrated that OpenAI's gpt-3.5-turbo performance on the KGC task also benefits from neighborhood information without prior fine-tuning. In addition to the quality improvement, we analyzed the impact of neighborhood imputation, highlighting the significance of selecting relevant triplets or extending the model's context in future works. Furthermore, future studies could explore the impact of model size, the effect of neighborhood imputation on the datasets from other domains, and neighborhood selection strategies.",
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+    {
+        "type": "text",
+        "text": "Limitations",
+        "text_level": 1,
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "Limitations of this study include the training of only relatively small models, with only T5 being employed. Further study of the benefits of larger models applied to KGC tasks should be performed. Additionally, each knowledge graph in this study was limited to only one model, which may not accurately represent more complex graphs that involve multiple datasets. Another limitation is that language models are better suited for real-world textual data and may not work well for KGC representing more specialized concepts, such as biology or medicine. Also, although neighborhood selection was performed, we cannot guarantee that the selected neighborhoods are optimal. Further works should include the way of extending the ability of models to capture larger parts of a graph or selecting the most relevant neighbors. Moreover, constructing prompts for testing GPT performance may not be optimal, and the model's maximal performance cannot be guaranteed without knowledge of its training data.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Ethics Statement",
+        "text_level": 1,
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "As a field of study in the intersection of natural language processing and knowledge graphs, this work inherits the main ethical considerations of both of these domains. Automated graph completion tasks may produce various types of mistakes, including but not limited to factual and grammatical errors. Consequently, any application based on this work should employ robust error detection mechanisms prior to deployment. Additionally, given that our system employs a generative language model, there exists a risk of generating false or misleading content such as hallucinations and biases existing in pre-training and knowledge graph data. Users of the proposed method or any application based on it should be warned about the potential risks of using generative language models. Potential misuses may include purposeful disinformation and misleading language or factual information. Taking into account the aforementioned considerations, we believe that under ethically responsible usage this work will have a positive impact on artificial intelligence research and applications.",
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+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "We are grateful to Mikhail Galkin for valuable advice on knowledge graphs and introduction to the",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "5310",
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "ILPC dataset. We are thankful to SberDevices for granting us access to additional computational resources. A.C., A.B., and Y.K.'s work was supported by a grant for research centers in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Moscow Institute of Physics and Technology dated November 1, 2021 No. 70-2021-00138.",
+        "bbox": [
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+        "page_idx": 5
+    },
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+        "text": "Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.",
+        "bbox": [
+            114,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International conference on machine learning, pages 2071-2080. PMLR.",
+        "bbox": [
+            114,
+            454,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Liang Wang, Wei Zhao, Zhuoyu Wei, and Jingming Liu. 2022. Simkgc: Simple contrastive knowledge graph completion with pre-trained language models. arXiv preprint arXiv:2203.02167.",
+        "bbox": [
+            114,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. 2021. Kepler: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics, 9:176-194.",
+        "bbox": [
+            114,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Xin Xie, Ningyu Zhang, Zhoubo Li, Shumin Deng, Hui Chen, Feiyu Xiong, Mosha Chen, and Huajun Chen. 2022. From discrimination to generation: Knowledge graph completion with generative transformer. In Companion Proceedings of the Web Conference 2022, pages 162-165.",
+        "bbox": [
+            114,
+            681,
+            489,
+            760
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Kgbert: Bert for knowledge graph completion. arXiv preprint arXiv:1909.03193.",
+        "bbox": [
+            114,
+            770,
+            489,
+            810
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Hanwen Zha, Zhiyu Chen, and Xifeng Yan. 2022. Inductive relation prediction by bert. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 5923-5931.",
+        "bbox": [
+            114,
+            820,
+            489,
+            873
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "A Input data pipeline",
+        "text_level": 1,
+        "bbox": [
+            509,
+            83,
+            714,
+            101
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/e52446d7e7fac270556b26bfb341a8dbd8e2a15c981e3ad93e6742aa342e2d4b.jpg",
+        "image_caption": [
+            "Figure 3: Visualization of the complete pipeline for the preparation of the input data of the language model: 0) pre-calculate FastText embeddings for each relation in KG and form a static similarity matrix out of them; 1) form a neighborhood for the input triplet; 2) sort the neighborhood by relation similarity to the relation in the input query; 3) verbalize and concatenate query triplet and its neighborhood."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            515,
+            126,
+            875,
+            218
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "B Neighborhood formation",
+        "text_level": 1,
+        "bbox": [
+            507,
+            374,
+            761,
+            390
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "To augment the semantic information contained in the entity and relationship names with the structural one, we used triplet neighborhoods. A triplet neighborhood is formed as follows: for a query (head, relation, ?) search for 1-hop neighbors associated with head entity, while excluding from the formed neighborhood target tail entity, which is associated with head by relation; head can be both head or tail entity in triplets that form this 1-hop neighborhood. In this manner, we obtain the context as a neighborhood of the graph, avoiding the leakage of the target entity. The process of neighborhood formation is illustrated on Figure 4.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/60c8842423a94a2ddf5d89c2077e80bb83cd500968069bb1e92ea0909f266f8d.jpg",
+        "image_caption": [
+            "Figure 4: Neighborhood formation - taking the input triplet, search for the triplets in which the head from the input triplet is either head or tail, then exclude the triplet with relation and tail from the input. As a result, there is a list of triplets that form the neighborhood for a given triplet. The represented neighborhood from the Wikidata5m KG is incomplete and textually verbalized for ease of understanding."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/33a5f1a557f19445f767a4ac92e9b1330d2fa15176d3bd1b8b631f0b8ae28dbe.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
+            670,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "5312",
+        "bbox": [
+            480,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "However, some of the triplets from KGs result in extremely large neighborhoods, e.g. hub nodes such as \"human\". Therefore, we needed to tackle the problem of large neighborhoods to fit it into the model's context, which is limited to 512 tokens for most language models including T5. We propose to do it as follows: 1) sort the neighborhood by semantic similarity of relations from its triplets to the relation from the input triplet in order to prioritize more important information in the context; 2) limit the sorted neighborhood to 512 triplets, since this will always be at least as big as the size of the allowed context, and, after verbalization, specify the maximum length of 512 for the model tokenizer to fit the resulting verbalized neighborhood representation into the language model context. Neighborhood sorting by semantic proximity was performed using a pre-calculated matrix of cosine similarity of relations in KG, for similarity calculation the relations were embedded by the fasttext model (Bojanowski et al., 2017). Figure 5 displays a pipeline for neighborhood sorting.",
+        "bbox": [
+            112,
+            84,
+            492,
+            441
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/900ff71c1b5af1e623f3b58f162086b4f508001636b10ee89ab8e698121641b8.jpg",
+        "image_caption": [
+            "Figure 5: Neighborhood sorting - taking the input triplet, its neighborhood, and pre-calculated similarity matrix for verbalized relation embedded by the FastText model, sort the neighborhood by the similarity of relations in its triplets to the relation in the input query."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            137,
+            468,
+            463,
+            596
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Neighborhood composition was a relatively straightforward task for a smaller graph like ILPC, but for Wikidata KG with a large number of triplets in the graph, neighborhood formation took a significant amount of time and RAM. To solve this problem, we used the MongoDB database<sup>3</sup>, which stored knowledge graph triplets in its collection in JSON format and contained indices for 'head' and 'tail' fields to speed up retrieving. In order not to store verbalized data in memory either during the verbalization process or during model training, verbalized representations were outputted to the",
+        "bbox": [
+            112,
+            702,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "MongoDB collection too, and retrieved by batches from the database during training.",
+        "bbox": [
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+            84,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "C Triplets verbalization",
+        "text_level": 1,
+        "bbox": [
+            507,
+            162,
+            736,
+            180
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "To pass the resulting representations of queries to the input of the language model, we had to perform a transformation of these queries and their neighborhoods into textual form. In order to do this, we first needed to generate a mapping of entity and relationship Wikidata IDs into corresponding texts. For all the datasets we performed our experiments on there is such textual mapping. However, often there is no unambiguous encoding in such mappings because many entities have the same name. The problem of non-unique textual representations concerns Wikidata KG entities, not relations, e.g. apple as a fruit of the apple tree and Apple as an American multinational technology company. To tackle this issue we used the same method KGT5 authors proposed: 1) concatenate non-unique entity names with corresponding descriptions if ones exist; 2) in case there is no description or after description, concatenation text representations cannot be disambiguated, add unique numeric ids. For all other smaller datasets we only added a unique numeric identifier at the end of the name of non-unique entities. The full disambiguation pipeline is displayed in Figure 6. No such transformations were required for KG relations, as they were fully distinguishable by their text representation.",
+        "bbox": [
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+            190,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/48977740a01ba71507731a50655fa00925b12280dc5ec8b0655739350e2213b3.jpg",
+        "image_caption": [
+            "Figure 6: Disambiguation schema for wikidata5m entities: 1) concatenate entities' textual representation with corresponding description if there is one in the KG metadata; 2) if there is no description for some entities - concatenate their names with unique numerical id."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            542,
+            637,
+            847,
+            782
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "The next step after obtaining 1to1 mapping of entities and relation IDs to their text representations",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_footnote",
+        "text": "<sup>3</sup>https://www.mongodb.com",
+        "bbox": [
+            136,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "5313",
+        "bbox": [
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+            940
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "was verbalization of the resulting queries and corresponding neighborhoods in the KG. Textual representations of the query  $(head_q, relation_q, ?)$  from KG were formed as follows:",
+        "bbox": [
+            112,
+            84,
+            489,
+            148
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1. first part of query verbalization was formed via concatenation of headq's and relation's text mappings with \"predict\" string, e.g. \"predict Steve Jobs place of birth\";",
+            "2. for each i-th triplet  $(head_q, relation_i, tail_i)$  or  $(head_i, relation_i, head_q)$  from the sorted query's neighborhood we formed a string consisting of  $relation_i$  and entity not belonging to the input query, i.e. for query (Steve Jobs, place of birth,?) the verbalized triplet (Steve Jobs, occupation, entrepreneur) from its neighborhood will be \"occupation entrepreneur\"; in case of so-called inverse triplet, e.g. in triplet  $(head_i, relation_i, head_q)$ , where  $head_q$  is a tail of the neighborhood triplet, we add \"inverse of\" to the textual mapping of  $relation_i$ , i.e. for (Apple, founded by, Steve Jobs) verbalization will be \"inverse of founded by Apple\";",
+            "3. at the end we concatenate the verbalization obtained in stage (1) and all the verbalizations from stage (2) using the\" [SEP] \" string inserted between all the above-mentioned strings."
+        ],
+        "bbox": [
+            127,
+            167,
+            490,
+            624
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "image",
+        "img_path": "images/72647039607e1bbef33364bcaf9700e471cd2be33bd1e3d15073d8246d0ac364.jpg",
+        "image_caption": [
+            "Figure 7: Query verbalization - map each entity and relation id from query triplet and its neighborhood triplets with corresponding textual representations; concatenate \"predict\" string, obtained query verbalization excluding target and the sorted triplets' verbalization without head entity from the query, separating each verbalized triplet by \"[SEP]\" token."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            122,
+            664,
+            480,
+            749
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Example of query and its neighborhood verbalization is represented on a Figure 7.",
+        "bbox": [
+            112,
+            887,
+            489,
+            917
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "D Link prediction inference",
+        "text_level": 1,
+        "bbox": [
+            509,
+            83,
+            766,
+            99
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "As in KGT5 (Saxena et al., 2022), to evaluate top-k possible answers, we sample k most probable sequences from the decoder and assign them their corresponding probabilities, which is a sum of log probabilities of each token in the sequence. Then, we assign  $-\\infty$  score for all the nodes that were not generated by the sampling procedure. In this way, we are producing results comparable with traditional KG models as we obtain plausibility scores for all possible triplets in KG. By such procedure, we can obtain whether a real score of the model (if the target sequence persists in k generated sequences) or the score that will be worse than a real (if the target sequence was not generated as one in the top-k probable sequences), thus not overestimating the resulting metric even though we do not score all the possible triplets. Additionally, to exclude both entities that do not belong to KG and known links from the train and valid graph in a transductive setting, we performed a filtering procedure specific to KGC evaluation protocol as described in the previous studies (Bordes et al., 2013).",
+        "bbox": [
+            505,
+            112,
+            884,
+            482
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "As a main metric for choosing the best model during evaluation, we used Exact Match (EM) or in other words Hits@1, which returns 1 in case the generated and target sequences are exactly the same or 0 otherwise. However, an exact match between the target and the generated sequence can be a rather strict metric for long target entity names, and for previously unseen entities. Thus, we came up with a new approach for scoring entities produced by generative models such as T5 for inductive KGC. In this approach, we first embedded verbalized entities from the inference part of KG by Sbert model (Reimers and Gurevych, 2019) and then indexed the resulted vectors using FAISS index (Johnson et al., 2019). In such a way, we used a simple flat FAISS index to perform a lookup for the k most similar entities from inference KG to the generated one. Using the index for the inductive ILPC dataset, we retrieve entities from the KG that are semantically closest to the generated sequence even if the generated string is imperfect, but contains the right tokens referring to the target entity. In this manner, we could calculate Hits@k retrieving top-k closest to the generated entity names in a similar way as distance-based methods. Overall, in the case of inductive setup, we used a combination of exact match (EM) and FAISS-based met",
+        "bbox": [
+            507,
+            483,
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "5314",
+        "bbox": [
+            480,
+            928,
+            519,
+            940
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "rics Hits@k calculated by the following formula:  $1000EM + Hits@1$ . In this way, we give more weight to the exact match metric, but in the case of equal EM in different checkpoints, the model with the best Hits@1 will be chosen.",
+        "bbox": [
+            112,
+            84,
+            487,
+            162
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "E Datasets statistics",
+        "text_level": 1,
+        "bbox": [
+            114,
+            184,
+            305,
+            200
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "table",
+        "img_path": "images/6903ec33c6b18a63664ba2e2781a8f90019122ee144115d4e1fc5bed1c0b5c92.jpg",
+        "table_caption": [
+            "Table 4: Statistics of used KGs."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dataset</td><td># Entities</td><td># Relations</td><td># Triplets</td></tr><tr><td>Wikidata5M</td><td>4.8M</td><td>828</td><td>21M</td></tr><tr><td>ILPC-small</td><td>10,230</td><td>96</td><td>78,616</td></tr><tr><td>ILPC-large</td><td>46,626</td><td>130</td><td>202,446</td></tr></table>",
+        "bbox": [
+            119,
+            258,
+            484,
+            323
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "F Attention map interpretation",
+        "text_level": 1,
+        "bbox": [
+            114,
+            369,
+            401,
+            386
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/6cf43a2fc0250015cb8fd0d8a8288c49ba80cf20f705226aff9c48fcdb0f7ce5.jpg",
+        "image_caption": [
+            "Figure 8: Example of cross-encoder attention map highlighting the importance of neighborhood triplets from the input"
+        ],
+        "image_footnote": [],
+        "bbox": [
+            117,
+            420,
+            485,
+            491
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Figure 8 demonstrates a cross-encoder attention map highlighting the input neighborhood's significance for the target prediction. The query (Arman Kirakossian, country of citizenship, ?) identifies Yerevan, Armenia's capital, as the birthplace of the target entity, thereby amplifying its role in predicting the right country of citizenship.",
+        "bbox": [
+            112,
+            576,
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+            688
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "G Zero-shot link prediction with gpt-3.5-turbo",
+        "text_level": 1,
+        "bbox": [
+            112,
+            707,
+            413,
+            740
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Since OpenAI API only returns generated text, we cannot get its probabilities. Therefore, we use a slightly different evaluation pipeline from other models. We generate up to three predictions with gpt-3.5-turbo-0613. If any of the randomly selected  $k$  predictions match the target entity after cleaning and normalization, we set that model prediction as correct (Hits@  $k$  in Table 2).",
+        "bbox": [
+            112,
+            756,
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+            883
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "We use the following prompts to control the gpt-3.5-turbo-0613.",
+        "bbox": [
+            112,
+            887,
+            487,
+            917
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "G.1 Zero-shot link prediction without neighborhood",
+        "text_level": 1,
+        "bbox": [
+            507,
+            84,
+            821,
+            115
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer.",
+        "bbox": [
+            505,
+            124,
+            882,
+            235
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "User prompt: Triplet to complete: <verbalized triplet>.",
+        "bbox": [
+            507,
+            237,
+            882,
+            269
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "G.2 Zero-shot link prediction with neighborhood",
+        "text_level": 1,
+        "bbox": [
+            507,
+            285,
+            796,
+            316
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Triplet to complete IS NOT in the list of related nodes, but it may contain helpful clues. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer.",
+        "bbox": [
+            505,
+            324,
+            882,
+            467
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "User prompt: Adjacent relations: <verbalized neighborhood>  $\\backslash n$  Triplet to complete: <verbalized triplet>.",
+        "bbox": [
+            507,
+            470,
+            882,
+            518
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "H Overview of concurrent work",
+        "text_level": 1,
+        "bbox": [
+            507,
+            535,
+            803,
+            550
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "While our work was under review, the concurrent work KGT5-context (Kochsiek et al., 2023) was published. Following the same idea, KGT5-context uses verbalized node neighborhood as additional context for KGC with the KGT5 model. However, KGT5-context explores the applicability of neighborhood information for KGC only in transductive setup. Also, neighborhood sampling in KGT5-context was performed randomly instead of prioritizing the neighbors, which makes the results less reproducible and does not consider the importance of neighbors in the context of a query. Furthermore, we show that this approach generalizes well to other pre-trained language models like gpt-3.5-turbo in a zero-shot setting without pre-training on KGC datasets.",
+        "bbox": [
+            505,
+            564,
+            882,
+            820
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "We have made the effort and tried our best to reproduce the results of the KGT5 model (Saxena et al., 2022) on Wikidata5M. We report the results of T5-small not pre-trained (Table 1), which is our version of the KGT5 model. Therefore, all our other models were trained and evaluated in",
+        "bbox": [
+            507,
+            822,
+            882,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "page_number",
+        "text": "5315",
+        "bbox": [
+            480,
+            927,
+            519,
+            940
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "the same setting and with the same pipeline as for T5-small not pre-trained model.",
+        "bbox": [
+            114,
+            85,
+            487,
+            116
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "5316",
+        "bbox": [
+            480,
+            928,
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+        ],
+        "page_idx": 10
+    }
+]

2023/Better Together_ Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information/d96254f1-428d-46b8-a6d6-93195d523d44_model.json

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+[
+    [
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "Alla Chepurova<sup>1</sup> Aydar Bulatov<sup>1</sup> Yuri Kuratov<sup>1,2</sup> Mikhail Burtsev<sup>3</sup>"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "\\(^{1}\\)Neural Networks and Deep Learning Lab, MIPT, Dolgoprudny, Russia \\(^{2}\\)AIRI, Moscow, Russia"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "content": "\\(^{3}\\)London Institute for Mathematical Sciences, London, UK\nchepurova@deeppavlov.ai, {bulatov.as, yurii.kuratov}@phystech.edu, mb@lims.ac.uk"
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "Abstract"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "content": "Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential performance. Knowledge Graph Completion (KGC) techniques aim to address this issue. However, traditional KGC methods are computationally intensive and impractical for large-scale KGs, necessitating the learning of dense node embeddings and computing pairwise distances. Generative transformer-based language models (e.g., T5 and recent KGT5) offer a promising solution as they can predict the tail nodes directly. In this study, we propose to include node neighborhoods as additional information to improve KGC methods based on language models. We examine the effects of this imputation and show that, on both inductive and transductive Wikidata subsets, our method outperforms KGT5 and conventional KGC approaches. We also provide an extensive analysis of the impact of neighborhood on model prediction and show its importance. Furthermore, we point the way to significantly improve KGC through more effective neighborhood selection."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "content": "Knowledge graphs (KG) represent structured information in the form of a directed multi-relational graph with entities as the graph's nodes and relations between entities as the graph's edges. Entities in such graphs could be both real-world objects or abstract concepts. KGs are represented as a set of triplets with representation entities and a relation between them, i.e. (head entity, relation, tail entity) or (h, r, t) for short. Due to KGs' recent rapid development, they are now widely used in a variety of applications, including data mining, information retrieval, question answering, and recommendation systems. However, most real-world knowledge graphs suffer from incompleteness. Knowledge graph completion (KGC) algorithms attempt to fill in the gaps in the KG in order to solve this problem."
+        },
+        {
+            "type": "image",
+            "bbox": [
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+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
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+            "content": "Figure 1: Graph completion pipeline. We extract and verbalize neighborhood of a triplet head node from the Knowledge Graph (KG). Language Model uses verbalized input triplet and neighborhood to make predictions. Predictions can be used for question answering or KG completion."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "KG link prediction commonly involves learning knowledge graph embeddings (KGE), which capture structural and semantic information about KG nodes, relations, and their neighborhoods in a way that a simple distance-based function would be able to distinguish real triplets from false ones. However, building a unique embedding for each node and relation leads to a linear increase in model size and inference time as the KG size grows. Another drawback is that KG updating requires full re-training of KGEs, rather than simply learning new embeddings for updated entities or relations."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "Application of language models (LMs) to KGC tasks is a promising direction (Pan et al., 2023). State-of-the-art performance in most natural language processing (NLP) tasks has recently been demonstrated by pre-trained language models like BERT (Devlin et al., 2019) and more recent large or foundational language models (Brown et al., 2020; Chowdhery et al., 2022; Touvron et al., 2023). Language models-based approaches, such as KG-BERT (Yao et al., 2019), BERTRL (Zha et al., 2022), KGT5 (Saxena et al., 2022) and GenKGC (Xie et al., 2022) consider a triplet in the KG as a text sequence. While distance-based"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+            "angle": 0,
+            "content": "5306"
+        },
+        {
+            "type": "footer",
+            "bbox": [
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+            "angle": 0,
+            "content": "Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5306-5316 December 6-10, 2023 ©2023 Association for Computational Linguistics"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
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+            "angle": 0,
+            "content": "methods incorporate more structural information in dense embeddings, language models tend to capture more semantic information contained in both relations and entities of KG, enabling such models to embed previously unseen entities and operate on new facts based on semantic information contained in triplets textual representation. Thus, KGT5 positions KG link prediction as sequence-to-sequence tasks. KGT5 model trained to generate target node using textually verbalized triplets achieves competitive performance and offers a scalable and simple framework for KGC and QA tasks."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "Current methods based on language models (KG-BERT, BERTRL, KGT5, and GenKGC) rely only on information that can be extracted from LMs parameters. However, since KG itself is available, we build a method that relies on both internal language model knowledge and external information from KG. For the link prediction task \\((h,r,\\ref{eq:1})\\), we propose to extract nodes and relations adjacent to \\(h\\) from the KG, i.e. the neighborhood. Node \\(h\\) with its neighborhood better together represents \\(h\\) itself: neighborhood may contain clues such as Palo Alto and United States of America for the triplet (Steve Jobs, place of birth, ?) on Figure 1."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+                0.492,
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+            "angle": 0,
+            "content": "Though some efforts have been made to incorporate neighborhood into the KGC algorithms (Chen et al., 2020), our proposed pipeline inherits the benefits of generative LLMs compared to KGE approaches: (1) scalability and size of the model, (2) applicability to both transductive and inductive KGs due to an ability to generalize unseen entities, (3) no need to rank all possible candidate triplets due to direct generation of tail entity. Also, a concurrent work by the KGT5 authors, KGT5-context (Kochsiek et al., 2023), proposed a similar idea of integrating node neighborhood in the context of the generative LM model, supporting the main concerns and results of our study."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.727,
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+            ],
+            "angle": 0,
+            "content": "Our contributions are as follows: (1) we propose a generative graph completion method which effectively integrates language models with graph neighborhood information. (2) We show that incorporating graph information into multiple generative language models achieve strong performance improvement of link prediction in both transductive and inductive settings. Moreover, our approach achieves state-of-the-art results on the inductive ILPC dataset. (3) We analyze and evaluate the impact of adding neighborhood information on the performance of the models."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "2 Methods"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.113,
+                0.887,
+                0.419
+            ],
+            "angle": 0,
+            "content": "One can represent KG more formally as \\(\\mathcal{G} = (\\mathcal{T},\\mathcal{E},\\mathcal{R})\\), where \\(\\mathcal{E}\\) is a set of entities, \\(\\mathcal{R}\\) is a set of relations and \\(\\mathcal{T}\\) is a set of triplets \\(\\mathcal{T} \\in \\mathcal{E} \\times \\mathcal{R} \\times \\mathcal{E}\\). Therefore, the formulation of the KGC task is the following: for each triplet \\(\\mathcal{T}_i = (h,r,t)\\), where \\(h \\in \\mathcal{E}, t \\in \\mathcal{E}, r \\in \\mathcal{R}\\), answer both \\((h,r,?)\\) and \\((?,r,t)\\) queries on the graph \\(\\mathcal{G} / \\mathcal{T}_i\\). The KGC tasks can have different variations depending on the KG type: inductive and transductive. In a transductive setting the KGC task is formulated as follows: given a train KG \\(\\mathcal{G}_{train} = (\\mathcal{T}_{train},\\mathcal{E},\\mathcal{R})\\) train the model to predict on the inference KG \\(\\mathcal{G}_{inference} = (\\mathcal{T}_{inference},\\mathcal{E},\\mathcal{R})\\), therefore in the train and inference KG there is a same set of entities and relations. In inductive KGC the model is trained on the KG \\(\\mathcal{G}_{train} = (\\mathcal{T}_{train},\\mathcal{R},\\mathcal{E}_{train})\\) and predicts the inference KG \\(\\mathcal{G}_{inference} = (\\mathcal{T}_{inference},\\mathcal{R},\\mathcal{E}_{inference})\\), so the trained model observed only on the entities from train subgraph of KG."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.42,
+                0.886,
+                0.821
+            ],
+            "angle": 0,
+            "content": "We followed the KGT5 (Saxena et al., 2022) approach to the KGC task using an encoder-decoder Transformer based on T5 (Raffel et al., 2020). The link prediction task was posed as follows: for each triplet \\((entity_1, relation, entity_2)\\) from KG we had to predict the tail in the queries \\((entity_1, relation, ?)\\) and \\((entity_2, relation^{-1}, ?)\\). To generate input for the model, we converted such queries into a text form. Apart from the query verbalization only as in KGT5, we formed a neighborhood for the node and relations for each triplet in KG. Thus, verbalized queries and their neighborhoods in the form of text were fed to the input of the model for training (see Figure 1) and later to predict incomplete triplets. We form a neighborhood of nodes that have an edge with the triplet head node (1-hop neighbors). The number of adjacent nodes may be too large to fit into the context size of selected language models. Therefore, we sort them based on relation semantic similarity. We take only those verbalized nodes and relations that fit into the max sequence length. We provide more details on neighborhood formation in Appendix B and verbalization in Appendix C."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.823,
+                0.887,
+                0.92
+            ],
+            "angle": 0,
+            "content": "To evaluate our approach to link prediction tasks we used publically available transductive and inductive KGs. Specifically, we used the Wiki-data5M (Wang et al., 2021) dataset, which is one of the largest publicly available transductive KG up-to-date and large and small versions of ILPC"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.522,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5307"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.085,
+                0.493,
+                0.295
+            ],
+            "angle": 0,
+            "content": "dataset (Galkin et al., 2022), the largest fully inductive benchmark. ILPC inference graph size is challenging for modern GNN methods with inductive capabilities (Galkin et al., 2022). Apart from being challenging to solve by common approaches, the ILPC KGs are much more sparse than the transductive Wikidata5M. The graph size and sparseness of the data make the task hard for existing methods and result in modest performance metrics. Still, there were no existing solutions that beat the baseline provided in the ILPC paper. Both used datasets are the subsets of the real-world KG - Wikidata. Datasets' statistics are provided in the Table 4)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.295,
+                0.492,
+                0.44
+            ],
+            "angle": 0,
+            "content": "We compared models with and without neighborhoods to evaluate our hypothesis that neighborhoods help to better together represent graph node. In addition, unlike the KGT5, we experimented with different model configurations, both fine-tuning an existing pre-trained model and training from scratch as common knowledge obtained during language models pre-training might be useful on real-world knowledge graphs like Wikidata."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.44,
+                0.49,
+                0.761
+            ],
+            "angle": 0,
+            "content": "We used the T5-small with 60M parameters as our base model with both pretrained and randomly initialized weights to evaluate the effect of pretraining of the language model on the KG task. We added additional \"[SEP]\" tokens to the tokenizer with 30K tokens vocabulary to separate query and neighborhood. The model was trained using AdamW optimizer (Loshchilov and Hutter, 2019), 1e-5 learning rate, \\(10\\%\\) dropout, and batch size equals 320. The number of training steps was equal to 5M for the Wikidata5M dataset and 2M for all others. For multi-gpu training, we used the Horovod parallelization framework (Sergeev and Balso, 2018). In cases where a batch size equal to 320 did not fit GPU, we used gradient accumulation steps. We also benchmarked OpenAI gpt-3.5-turbo<sup>1</sup> zero-shot link prediction and evaluate the benefits of a neighborhood on LLM (see Appendix G). The code for all the described experiments is available at the link<sup>2</sup>."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.762,
+                0.49,
+                0.859
+            ],
+            "angle": 0,
+            "content": "As KGT5, our model produces a probability distribution over all possible tokens in the vocabulary at each step of decoding. Such distribution is penalized for not matching the actual distribution, i.e., for the difference between the real target token at position \\(i\\) and the generated token at step \\(i\\) using"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.18
+            ],
+            "angle": 0,
+            "content": "CE loss. Such an approach is principally different from distance-based methods as it does not require generating negative samples to make them further from the target prediction for each training query. In this way, training iteration becomes independent of the KG size."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.183,
+                0.885,
+                0.424
+            ],
+            "angle": 0,
+            "content": "To form predictions, we first verbalize the query (head, relation,?) and pass it as an input to the generative model to produce an output sequence, which will be a verbalization of the target entity id. For inference, we employed two strategies. The first one was sampling \\( k \\) most probable sequences from the decoder. We follow the KGT5 (Saxena et al., 2022) setting and use a sample size of 50. We apply this approach to Wikidata5M datasets as it is transductive. The second option for inference was to search for the closest entities in KG to the generated one. We apply the second approach to ILPC inductive datasets as language models have to generate previously unseen entities. More details on inference procedure are in Appendix D."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "3 Results"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.468,
+                0.885,
+                0.612
+            ],
+            "angle": 0,
+            "content": "The hypothesis behind our approach was that neighborhood information incorporated in a context fed to the language model should enhance link prediction of generative LM. Tables 1 and 2 demonstrate the performance of our technique on Wikidata5M and ILPC datasets under various conditions, including training a T5 model with and without neighbors, using a pre-trained model or training a model from scratch, and zero-shot with gpt-3.5-turbo."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.614,
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+            ],
+            "angle": 0,
+            "content": "These results support the hypothesis that the quality of link prediction increases with the use of neighbors in the context of the generative model in both transductive and inductive settings. Moreover, our approach demonstrated SOTA performance on the ILPC dataset, while on Wikidata we achieved the best result among generative link prediction models, which is still comparable to scores of SOTA graph and LM methods with many more parameters. Relatively small absolute values of scores on the ILPC dataset can be explained by its fully inductive setup and larger sparseness compared to Wikidata5M. Still, there are no publicly available solutions that exceed the baseline score provided by ILPC authors. Our solution overperforms the existing baseline, is elegantly simple, and more interpretable than GNN-based solutions. Experiments on pre-trained models revealed that pre-training speeds up convergence but degrades"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
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+            "angle": 0,
+            "content": "\\(^{1}\\)gpt-3.5-turbo-0613: https://platform.openai.com/docs/models/gpt-3-5  \n\\(^{2}\\)https://github.com/screemix/kgc-t5-with-neighbors"
+        },
+        {
+            "type": "page_number",
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+            "angle": 0,
+            "content": "5308"
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+    ],
+    [
+        {
+            "type": "table_caption",
+            "bbox": [
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+            "angle": 0,
+            "content": "Table 1: Adding a neighborhood persistently improves results on the link prediction task. We report Hits@k metrics on Wikidata5M, ILPC-small, and ILPC-large test sets. Arrows \\(\\uparrow\\downarrow\\) show the effect of the neighborhood on language models. Neighbors allow to achieve the best performance considering the number of model parameters."
+        },
+        {
+            "type": "table",
+            "bbox": [
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+            "content": "<table><tr><td rowspan=\"2\">MODEL</td><td colspan=\"3\">WIKIDATA5M</td><td colspan=\"3\">ILPC-SMALL</td><td colspan=\"3\">ILPC-LARGE</td><td rowspan=\"2\">#PARAMS</td></tr><tr><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td></tr><tr><td>KGT5 (SAXENA ET AL., 2022)</td><td>0.267</td><td>0.318</td><td>0.365</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>TRANSE (BORDES ET AL., 2013)</td><td>0.170</td><td>0.311</td><td>0.392</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2,400M</td></tr><tr><td>COMPLEX (TROULLON ET AL., 2016)</td><td>0.255</td><td>-</td><td>0.398</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>614M</td></tr><tr><td>KEPLER (WANG ET AL., 2021)</td><td>0.173</td><td>0.224</td><td>0.277</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>125M</td></tr><tr><td>SIMKGC (WANG ET AL., 2022)</td><td>0.313</td><td>0.376</td><td>0.441</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>220M</td></tr><tr><td>INDNODEPIECE (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.007</td><td>0.022</td><td>0.092</td><td>0.037</td><td>0.054</td><td>0.125</td><td>15.5K</td></tr><tr><td>INDNODEPIECEGNN (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.076</td><td>0.140</td><td>0.251</td><td>0.032</td><td>0.073</td><td>0.146</td><td>24K</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED</td><td>0.240</td><td>0.286</td><td>0.323</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED + NEIGHBORS</td><td>0.327↑</td><td>0.363↑</td><td>0.386↑</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED</td><td>0.205</td><td>0.248</td><td>0.282</td><td>0.073</td><td>0.117</td><td>0.177</td><td>0.112</td><td>0.137</td><td>0.194</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED + NEIGHBORS</td><td>0.306↑</td><td>0.342↑</td><td>0.361↑</td><td>0.076↑</td><td>0.137↑</td><td>0.209↑</td><td>0.114↑</td><td>0.139↑</td><td>0.200↑</td><td>60M</td></tr><tr><td>T5-BASE PRE-TRAINED</td><td>-</td><td>-</td><td>-</td><td>0.073</td><td>0.130</td><td>0.199</td><td>0.110</td><td>0.136</td><td>0.198</td><td>220M</td></tr><tr><td>T5-BASE PRE-TRAINED + NEIGHBORS</td><td>-</td><td>-</td><td>-</td><td>0.082↑</td><td>0.142↑</td><td>0.216↑</td><td>0.116↑</td><td>0.141↑</td><td>0.215↑</td><td>220M</td></tr></table>"
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+            "type": "table_caption",
+            "bbox": [
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+            "content": "Table 2: Neighbor information improves zero-shot link prediction with OpenAI gpt-3.5-turbo-0613. *ILPC-Large results were obtained on 5000 random samples from the test set."
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+        {
+            "type": "table",
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+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\">DATASET</td><td colspan=\"2\">GPT</td><td colspan=\"2\">GPT+NEIGH</td></tr><tr><td>H@1</td><td>H@3</td><td>H@1</td><td>H@3</td></tr><tr><td>WIKIDATA5M</td><td>0.098</td><td>0.131</td><td>0.113↑</td><td>0.157↑</td></tr><tr><td>ILPC-SMOTE</td><td>0.079</td><td>0.107</td><td>0.084↑</td><td>0.118↑</td></tr><tr><td>ILPC-LARGE*</td><td>0.103</td><td>0.134</td><td>0.114↑</td><td>0.150↑</td></tr></table>"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.482,
+                0.489,
+                0.514
+            ],
+            "angle": 0,
+            "content": "the quality of the model by a few points compared to the one trained from scratch."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.516,
+                0.49,
+                0.773
+            ],
+            "angle": 0,
+            "content": "Also, we demonstrated that neighbors' information enhances performance across different generative models. This includes open-source models such as T5, as well as proprietary models like OpenAI GPTs that are distributed as a model-as-a-service. Although both types of models have their own advantages and limitations, it should be noted that OpenAI GPTs are black box models that cannot be modified or interpreted further. Usage of OpenAI GPTs is restricted in the case of more specialized or proprietary KGs that can store secure or non-public information. In the context of our study, OpenAI's gpt-3.5-turbo has a much larger size and cost, compared to smaller and transparent T5, still our approach improves the performance of both models."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.775,
+                0.49,
+                0.92
+            ],
+            "angle": 0,
+            "content": "To get an understanding of how proposed models learn to use neighboring triplets we compared predictions of models trained with and without graph information in the input. One possible case when neighbors help in link prediction is when a target entity is somehow mentioned in the model input. Because of neighborhood filtering, the target triplet never appears in the model input, however in some cases target entity can occur with different relations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.32,
+                0.885,
+                0.45
+            ],
+            "angle": 0,
+            "content": "or as a substring of another entity in a neighborhood or even in the task itself (Table 3). Despite the tail appearing in input only in a small portion of the test set, the neighborhood model learns to make use of such hints, outperforming the baseline T5 in all selected cases. The high solution rate of more than 4 out of 5 samples with hinted targets supports the importance of properly selected neighborhoods."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.514,
+                0.462,
+                0.88,
+                0.607
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.508,
+                0.618,
+                0.884,
+                0.719
+            ],
+            "angle": 0,
+            "content": "Figure 2: KG neighborhood is critical for the model performance. The plot shows how the quality of link prediction depends on the number of neighborhood triplets removed from the input context Wikidata5M T5-small not pre-trained model. The blue solid line corresponds to removing the most relevant triplets and the dashed red line represents random selection."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.726,
+                0.885,
+                0.903
+            ],
+            "angle": 0,
+            "content": "To evaluate the impact of neighborhoods, we analyzed triplets' importance by sorting them based on how they affect the probability of the target node prediction. As Figure 2 shows, a performance of the model quickly deteriorates with the removal of important triplets from the context. This implies potential avenues for improvement by prioritizing significant triplets selection prior to model training. Moreover, we analyzed attention maps of the model train on Wikidata5M and found that it indeed pays attention to relevant neighbors, e.g. Appendix F."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.527,
+                0.904,
+                0.883,
+                0.92
+            ],
+            "angle": 0,
+            "content": "The analysis shows that adding relevant graph"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.521,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5309"
+        }
+    ],
+    [
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.114,
+                0.083,
+                0.488,
+                0.141
+            ],
+            "angle": 0,
+            "content": "Table 3: Adding relevant neighbors improves link prediction for T5-small on the Wikidata test set. The table shows the exact match depending on a hint of the target entity's appearance as a part of the model input."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.116,
+                0.15,
+                0.484,
+                0.228
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Target position</td><td>T5</td><td>T5 + Neighbors</td><td>frequency</td></tr><tr><td>In task</td><td>0.93</td><td>0.94↑</td><td>5 %</td></tr><tr><td>In neighborhood</td><td>0.34</td><td>0.82↑</td><td>8 %</td></tr><tr><td>Not in input</td><td>0.18</td><td>0.24↑</td><td>87 %</td></tr><tr><td>Total</td><td>0.23</td><td>0.32↑</td><td>100 %</td></tr></table>"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.246,
+                0.49,
+                0.519
+            ],
+            "angle": 0,
+            "content": "context information greatly enhances link prediction. Hence one way of improving performance is increasing neighborhood size by adding k-hop neighbors. Due to the quadratic complexity of attention and the limited input size of pretrained transformers, processing exponentially growing neighborhoods would be computationally expensive. To deal with large input sizes one can use efficient T5 architectures with input sizes up to 32k (Guo et al., 2022) and 64k (Ainslie et al., 2023) tokens or recurrent approaches (Bulatov et al., 2022; Hutchins et al., 2022) that can sequentially process effectively unlimited neighborhood sizes. Another potential way of subgraph selection is to add relevant but more distant knowledge graph information, such as random walks or more sophisticated methods (Das et al., 2018)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.537,
+                0.406,
+                0.552
+            ],
+            "angle": 0,
+            "content": "4 Conclusions and Future work"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.566,
+                0.49,
+                0.919
+            ],
+            "angle": 0,
+            "content": "Our study found that incorporating KG neighborhood information into a generative model's context can enhance the quality of the link prediction task. We trained the T5-small model on transductive Wikidata5M and inductive ILPC datasets with various configurations and showed that coupling the node and its neighborhood information together better represents the node. Our approach significantly outperformed baselines on the largest real-world KGCs, with a comparable or fewer number of parameters. We also demonstrated that OpenAI's gpt-3.5-turbo performance on the KGC task also benefits from neighborhood information without prior fine-tuning. In addition to the quality improvement, we analyzed the impact of neighborhood imputation, highlighting the significance of selecting relevant triplets or extending the model's context in future works. Furthermore, future studies could explore the impact of model size, the effect of neighborhood imputation on the datasets from other domains, and neighborhood selection strategies."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.084,
+                0.616,
+                0.099
+            ],
+            "angle": 0,
+            "content": "Limitations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.112,
+                0.885,
+                0.45
+            ],
+            "angle": 0,
+            "content": "Limitations of this study include the training of only relatively small models, with only T5 being employed. Further study of the benefits of larger models applied to KGC tasks should be performed. Additionally, each knowledge graph in this study was limited to only one model, which may not accurately represent more complex graphs that involve multiple datasets. Another limitation is that language models are better suited for real-world textual data and may not work well for KGC representing more specialized concepts, such as biology or medicine. Also, although neighborhood selection was performed, we cannot guarantee that the selected neighborhoods are optimal. Further works should include the way of extending the ability of models to capture larger parts of a graph or selecting the most relevant neighbors. Moreover, constructing prompts for testing GPT performance may not be optimal, and the model's maximal performance cannot be guaranteed without knowledge of its training data."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.464,
+                0.662,
+                0.48
+            ],
+            "angle": 0,
+            "content": "Ethics Statement"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.492,
+                0.885,
+                0.846
+            ],
+            "angle": 0,
+            "content": "As a field of study in the intersection of natural language processing and knowledge graphs, this work inherits the main ethical considerations of both of these domains. Automated graph completion tasks may produce various types of mistakes, including but not limited to factual and grammatical errors. Consequently, any application based on this work should employ robust error detection mechanisms prior to deployment. Additionally, given that our system employs a generative language model, there exists a risk of generating false or misleading content such as hallucinations and biases existing in pre-training and knowledge graph data. Users of the proposed method or any application based on it should be warned about the potential risks of using generative language models. Potential misuses may include purposeful disinformation and misleading language or factual information. Taking into account the aforementioned considerations, we believe that under ethically responsible usage this work will have a positive impact on artificial intelligence research and applications."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.861,
+                0.682,
+                0.877
+            ],
+            "angle": 0,
+            "content": "Acknowledgements"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.888,
+                0.884,
+                0.919
+            ],
+            "angle": 0,
+            "content": "We are grateful to Mikhail Galkin for valuable advice on knowledge graphs and introduction to the"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.522,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5310"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.493,
+                0.262
+            ],
+            "angle": 0,
+            "content": "ILPC dataset. We are thankful to SberDevices for granting us access to additional computational resources. A.C., A.B., and Y.K.'s work was supported by a grant for research centers in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Moscow Institute of Physics and Technology dated November 1, 2021 No. 70-2021-00138."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+                0.304
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+            "angle": 0,
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+            ],
+            "angle": 0,
+            "content": "Hanwen Zha, Zhiyu Chen, and Xifeng Yan. 2022. Inductive relation prediction by bert. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 5923-5931."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.084,
+                0.715,
+                0.102
+            ],
+            "angle": 0,
+            "content": "A Input data pipeline"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.517,
+                0.127,
+                0.877,
+                0.219
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.508,
+                0.232,
+                0.885,
+                0.347
+            ],
+            "angle": 0,
+            "content": "Figure 3: Visualization of the complete pipeline for the preparation of the input data of the language model: 0) pre-calculate FastText embeddings for each relation in KG and form a static similarity matrix out of them; 1) form a neighborhood for the input triplet; 2) sort the neighborhood by relation similarity to the relation in the input query; 3) verbalize and concatenate query triplet and its neighborhood."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.375,
+                0.762,
+                0.391
+            ],
+            "angle": 0,
+            "content": "B Neighborhood formation"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.4,
+                0.885,
+                0.626
+            ],
+            "angle": 0,
+            "content": "To augment the semantic information contained in the entity and relationship names with the structural one, we used triplet neighborhoods. A triplet neighborhood is formed as follows: for a query (head, relation, ?) search for 1-hop neighbors associated with head entity, while excluding from the formed neighborhood target tail entity, which is associated with head by relation; head can be both head or tail entity in triplets that form this 1-hop neighborhood. In this manner, we obtain the context as a neighborhood of the graph, avoiding the leakage of the target entity. The process of neighborhood formation is illustrated on Figure 4."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.52,
+                0.652,
+                0.67,
+                0.789
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.671,
+                0.652,
+                0.876,
+                0.789
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.508,
+                0.801,
+                0.885,
+                0.915
+            ],
+            "angle": 0,
+            "content": "Figure 4: Neighborhood formation - taking the input triplet, search for the triplets in which the head from the input triplet is either head or tail, then exclude the triplet with relation and tail from the input. As a result, there is a list of triplets that form the neighborhood for a given triplet. The represented neighborhood from the Wikidata5m KG is incomplete and textually verbalized for ease of understanding."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.522,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5312"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.493,
+                0.442
+            ],
+            "angle": 0,
+            "content": "However, some of the triplets from KGs result in extremely large neighborhoods, e.g. hub nodes such as \"human\". Therefore, we needed to tackle the problem of large neighborhoods to fit it into the model's context, which is limited to 512 tokens for most language models including T5. We propose to do it as follows: 1) sort the neighborhood by semantic similarity of relations from its triplets to the relation from the input triplet in order to prioritize more important information in the context; 2) limit the sorted neighborhood to 512 triplets, since this will always be at least as big as the size of the allowed context, and, after verbalization, specify the maximum length of 512 for the model tokenizer to fit the resulting verbalized neighborhood representation into the language model context. Neighborhood sorting by semantic proximity was performed using a pre-calculated matrix of cosine similarity of relations in KG, for similarity calculation the relations were embedded by the fasttext model (Bojanowski et al., 2017). Figure 5 displays a pipeline for neighborhood sorting."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.139,
+                0.469,
+                0.465,
+                0.598
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.612,
+                0.49,
+                0.687
+            ],
+            "angle": 0,
+            "content": "Figure 5: Neighborhood sorting - taking the input triplet, its neighborhood, and pre-calculated similarity matrix for verbalized relation embedded by the FastText model, sort the neighborhood by the similarity of relations in its triplets to the relation in the input query."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.703,
+                0.49,
+                0.898
+            ],
+            "angle": 0,
+            "content": "Neighborhood composition was a relatively straightforward task for a smaller graph like ILPC, but for Wikidata KG with a large number of triplets in the graph, neighborhood formation took a significant amount of time and RAM. To solve this problem, we used the MongoDB database<sup>3</sup>, which stored knowledge graph triplets in its collection in JSON format and contained indices for 'head' and 'tail' fields to speed up retrieving. In order not to store verbalized data in memory either during the verbalization process or during model training, verbalized representations were outputted to the"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.085,
+                0.883,
+                0.119
+            ],
+            "angle": 0,
+            "content": "MongoDB collection too, and retrieved by batches from the database during training."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.163,
+                0.737,
+                0.181
+            ],
+            "angle": 0,
+            "content": "C Triplets verbalization"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.191,
+                0.886,
+                0.61
+            ],
+            "angle": 0,
+            "content": "To pass the resulting representations of queries to the input of the language model, we had to perform a transformation of these queries and their neighborhoods into textual form. In order to do this, we first needed to generate a mapping of entity and relationship Wikidata IDs into corresponding texts. For all the datasets we performed our experiments on there is such textual mapping. However, often there is no unambiguous encoding in such mappings because many entities have the same name. The problem of non-unique textual representations concerns Wikidata KG entities, not relations, e.g. apple as a fruit of the apple tree and Apple as an American multinational technology company. To tackle this issue we used the same method KGT5 authors proposed: 1) concatenate non-unique entity names with corresponding descriptions if ones exist; 2) in case there is no description or after description, concatenation text representations cannot be disambiguated, add unique numeric ids. For all other smaller datasets we only added a unique numeric identifier at the end of the name of non-unique entities. The full disambiguation pipeline is displayed in Figure 6. No such transformations were required for KG relations, as they were fully distinguishable by their text representation."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.543,
+                0.638,
+                0.848,
+                0.783
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.508,
+                0.795,
+                0.887,
+                0.869
+            ],
+            "angle": 0,
+            "content": "Figure 6: Disambiguation schema for wikidata5m entities: 1) concatenate entities' textual representation with corresponding description if there is one in the KG metadata; 2) if there is no description for some entities - concatenate their names with unique numerical id."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.888,
+                0.886,
+                0.92
+            ],
+            "angle": 0,
+            "content": "The next step after obtaining 1to1 mapping of entities and relation IDs to their text representations"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.137,
+                0.904,
+                0.313,
+                0.919
+            ],
+            "angle": 0,
+            "content": "<sup>3</sup>https://www.mongodb.com"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.522,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5313"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.085,
+                0.49,
+                0.149
+            ],
+            "angle": 0,
+            "content": "was verbalization of the resulting queries and corresponding neighborhoods in the KG. Textual representations of the query \\((head_q, relation_q, ?)\\) from KG were formed as follows:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.13,
+                0.168,
+                0.49,
+                0.232
+            ],
+            "angle": 0,
+            "content": "1. first part of query verbalization was formed via concatenation of headq's and relation's text mappings with \"predict\" string, e.g. \"predict Steve Jobs place of birth\";"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.129,
+                0.251,
+                0.492,
+                0.526
+            ],
+            "angle": 0,
+            "content": "2. for each i-th triplet \\((head_q, relation_i, tail_i)\\) or \\((head_i, relation_i, head_q)\\) from the sorted query's neighborhood we formed a string consisting of \\(relation_i\\) and entity not belonging to the input query, i.e. for query (Steve Jobs, place of birth,?) the verbalized triplet (Steve Jobs, occupation, entrepreneur) from its neighborhood will be \"occupation entrepreneur\"; in case of so-called inverse triplet, e.g. in triplet \\((head_i, relation_i, head_q)\\), where \\(head_q\\) is a tail of the neighborhood triplet, we add \"inverse of\" to the textual mapping of \\(relation_i\\), i.e. for (Apple, founded by, Steve Jobs) verbalization will be \"inverse of founded by Apple\";"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.129,
+                0.544,
+                0.49,
+                0.625
+            ],
+            "angle": 0,
+            "content": "3. at the end we concatenate the verbalization obtained in stage (1) and all the verbalizations from stage (2) using the\" [SEP] \" string inserted between all the above-mentioned strings."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.129,
+                0.168,
+                0.492,
+                0.625
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.123,
+                0.665,
+                0.481,
+                0.75
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.767,
+                0.49,
+                0.868
+            ],
+            "angle": 0,
+            "content": "Figure 7: Query verbalization - map each entity and relation id from query triplet and its neighborhood triplets with corresponding textual representations; concatenate \"predict\" string, obtained query verbalization excluding target and the sorted triplets' verbalization without head entity from the query, separating each verbalized triplet by \"[SEP]\" token."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.888,
+                0.49,
+                0.919
+            ],
+            "angle": 0,
+            "content": "Example of query and its neighborhood verbalization is represented on a Figure 7."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.084,
+                0.768,
+                0.101
+            ],
+            "angle": 0,
+            "content": "D Link prediction inference"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.113,
+                0.885,
+                0.483
+            ],
+            "angle": 0,
+            "content": "As in KGT5 (Saxena et al., 2022), to evaluate top-k possible answers, we sample k most probable sequences from the decoder and assign them their corresponding probabilities, which is a sum of log probabilities of each token in the sequence. Then, we assign \\(-\\infty\\) score for all the nodes that were not generated by the sampling procedure. In this way, we are producing results comparable with traditional KG models as we obtain plausibility scores for all possible triplets in KG. By such procedure, we can obtain whether a real score of the model (if the target sequence persists in k generated sequences) or the score that will be worse than a real (if the target sequence was not generated as one in the top-k probable sequences), thus not overestimating the resulting metric even though we do not score all the possible triplets. Additionally, to exclude both entities that do not belong to KG and known links from the train and valid graph in a transductive setting, we performed a filtering procedure specific to KGC evaluation protocol as described in the previous studies (Bordes et al., 2013)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.485,
+                0.885,
+                0.919
+            ],
+            "angle": 0,
+            "content": "As a main metric for choosing the best model during evaluation, we used Exact Match (EM) or in other words Hits@1, which returns 1 in case the generated and target sequences are exactly the same or 0 otherwise. However, an exact match between the target and the generated sequence can be a rather strict metric for long target entity names, and for previously unseen entities. Thus, we came up with a new approach for scoring entities produced by generative models such as T5 for inductive KGC. In this approach, we first embedded verbalized entities from the inference part of KG by Sbert model (Reimers and Gurevych, 2019) and then indexed the resulted vectors using FAISS index (Johnson et al., 2019). In such a way, we used a simple flat FAISS index to perform a lookup for the k most similar entities from inference KG to the generated one. Using the index for the inductive ILPC dataset, we retrieve entities from the KG that are semantically closest to the generated sequence even if the generated string is imperfect, but contains the right tokens referring to the target entity. In this manner, we could calculate Hits@k retrieving top-k closest to the generated entity names in a similar way as distance-based methods. Overall, in the case of inductive setup, we used a combination of exact match (EM) and FAISS-based met"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.929,
+                0.521,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5314"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.488,
+                0.164
+            ],
+            "angle": 0,
+            "content": "rics Hits@k calculated by the following formula: \\(1000EM + Hits@1\\). In this way, we give more weight to the exact match metric, but in the case of equal EM in different checkpoints, the model with the best Hits@1 will be chosen."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.185,
+                0.307,
+                0.201
+            ],
+            "angle": 0,
+            "content": "E Datasets statistics"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.192,
+                0.227,
+                0.409,
+                0.241
+            ],
+            "angle": 0,
+            "content": "Table 4: Statistics of used KGs."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.12,
+                0.259,
+                0.485,
+                0.324
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Dataset</td><td># Entities</td><td># Relations</td><td># Triplets</td></tr><tr><td>Wikidata5M</td><td>4.8M</td><td>828</td><td>21M</td></tr><tr><td>ILPC-small</td><td>10,230</td><td>96</td><td>78,616</td></tr><tr><td>ILPC-large</td><td>46,626</td><td>130</td><td>202,446</td></tr></table>"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.37,
+                0.402,
+                0.387
+            ],
+            "angle": 0,
+            "content": "F Attention map interpretation"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.118,
+                0.421,
+                0.486,
+                0.492
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.512,
+                0.487,
+                0.556
+            ],
+            "angle": 0,
+            "content": "Figure 8: Example of cross-encoder attention map highlighting the importance of neighborhood triplets from the input"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.577,
+                0.489,
+                0.689
+            ],
+            "angle": 0,
+            "content": "Figure 8 demonstrates a cross-encoder attention map highlighting the input neighborhood's significance for the target prediction. The query (Arman Kirakossian, country of citizenship, ?) identifies Yerevan, Armenia's capital, as the birthplace of the target entity, thereby amplifying its role in predicting the right country of citizenship."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.708,
+                0.414,
+                0.741
+            ],
+            "angle": 0,
+            "content": "G Zero-shot link prediction with gpt-3.5-turbo"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.757,
+                0.489,
+                0.884
+            ],
+            "angle": 0,
+            "content": "Since OpenAI API only returns generated text, we cannot get its probabilities. Therefore, we use a slightly different evaluation pipeline from other models. We generate up to three predictions with gpt-3.5-turbo-0613. If any of the randomly selected \\(k\\) predictions match the target entity after cleaning and normalization, we set that model prediction as correct (Hits@ \\(k\\) in Table 2)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.888,
+                0.488,
+                0.919
+            ],
+            "angle": 0,
+            "content": "We use the following prompts to control the gpt-3.5-turbo-0613."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.085,
+                0.822,
+                0.116
+            ],
+            "angle": 0,
+            "content": "G.1 Zero-shot link prediction without neighborhood"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.125,
+                0.884,
+                0.236
+            ],
+            "angle": 0,
+            "content": "System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.239,
+                0.883,
+                0.27
+            ],
+            "angle": 0,
+            "content": "User prompt: Triplet to complete: <verbalized triplet>."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.286,
+                0.797,
+                0.317
+            ],
+            "angle": 0,
+            "content": "G.2 Zero-shot link prediction with neighborhood"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.325,
+                0.884,
+                0.468
+            ],
+            "angle": 0,
+            "content": "System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Triplet to complete IS NOT in the list of related nodes, but it may contain helpful clues. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.472,
+                0.884,
+                0.519
+            ],
+            "angle": 0,
+            "content": "User prompt: Adjacent relations: <verbalized neighborhood> \\(\\backslash n\\) Triplet to complete: <verbalized triplet>."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.536,
+                0.804,
+                0.551
+            ],
+            "angle": 0,
+            "content": "H Overview of concurrent work"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.565,
+                0.884,
+                0.821
+            ],
+            "angle": 0,
+            "content": "While our work was under review, the concurrent work KGT5-context (Kochsiek et al., 2023) was published. Following the same idea, KGT5-context uses verbalized node neighborhood as additional context for KGC with the KGT5 model. However, KGT5-context explores the applicability of neighborhood information for KGC only in transductive setup. Also, neighborhood sampling in KGT5-context was performed randomly instead of prioritizing the neighbors, which makes the results less reproducible and does not consider the importance of neighbors in the context of a query. Furthermore, we show that this approach generalizes well to other pre-trained language models like gpt-3.5-turbo in a zero-shot setting without pre-training on KGC datasets."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.824,
+                0.883,
+                0.918
+            ],
+            "angle": 0,
+            "content": "We have made the effort and tried our best to reproduce the results of the KGT5 model (Saxena et al., 2022) on Wikidata5M. We report the results of T5-small not pre-trained (Table 1), which is our version of the KGT5 model. Therefore, all our other models were trained and evaluated in"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.521,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5315"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.115,
+                0.086,
+                0.489,
+                0.117
+            ],
+            "angle": 0,
+            "content": "the same setting and with the same pipeline as for T5-small not pre-trained model."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.929,
+                0.522,
+                0.941
+            ],
+            "angle": 0,
+            "content": "5316"
+        }
+    ]
+]

2023/Better Together_ Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information/d96254f1-428d-46b8-a6d6-93195d523d44_origin.pdf

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2023/Better Together_ Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information/full.md

@@ -0,0 +1,236 @@
+# Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information
+
+Alla Chepurova<sup>1</sup> Aydar Bulatov<sup>1</sup> Yuri Kuratov<sup>1,2</sup> Mikhail Burtsev<sup>3</sup>
+
+$^{1}$ Neural Networks and Deep Learning Lab, MIPT, Dolgoprudny, Russia  $^{2}$ AIRI, Moscow, Russia
+
+$^{3}$ London Institute for Mathematical Sciences, London, UK
+chepurova@deeppavlov.ai, {bulatov.as, yurii.kuratov}@phystech.edu, mb@lims.ac.uk
+
+# Abstract
+
+Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential performance. Knowledge Graph Completion (KGC) techniques aim to address this issue. However, traditional KGC methods are computationally intensive and impractical for large-scale KGs, necessitating the learning of dense node embeddings and computing pairwise distances. Generative transformer-based language models (e.g., T5 and recent KGT5) offer a promising solution as they can predict the tail nodes directly. In this study, we propose to include node neighborhoods as additional information to improve KGC methods based on language models. We examine the effects of this imputation and show that, on both inductive and transductive Wikidata subsets, our method outperforms KGT5 and conventional KGC approaches. We also provide an extensive analysis of the impact of neighborhood on model prediction and show its importance. Furthermore, we point the way to significantly improve KGC through more effective neighborhood selection.
+
+# 1 Introduction
+
+Knowledge graphs (KG) represent structured information in the form of a directed multi-relational graph with entities as the graph's nodes and relations between entities as the graph's edges. Entities in such graphs could be both real-world objects or abstract concepts. KGs are represented as a set of triplets with representation entities and a relation between them, i.e. (head entity, relation, tail entity) or (h, r, t) for short. Due to KGs' recent rapid development, they are now widely used in a variety of applications, including data mining, information retrieval, question answering, and recommendation systems. However, most real-world knowledge graphs suffer from incompleteness. Knowledge graph completion (KGC) algorithms attempt to fill in the gaps in the KG in order to solve this problem.
+
+![](images/6c44ba12738f48bf1c00f3a91a33c87e489458c8b3a0b7bde9195a8c4a966177.jpg)  
+Figure 1: Graph completion pipeline. We extract and verbalize neighborhood of a triplet head node from the Knowledge Graph (KG). Language Model uses verbalized input triplet and neighborhood to make predictions. Predictions can be used for question answering or KG completion.
+
+KG link prediction commonly involves learning knowledge graph embeddings (KGE), which capture structural and semantic information about KG nodes, relations, and their neighborhoods in a way that a simple distance-based function would be able to distinguish real triplets from false ones. However, building a unique embedding for each node and relation leads to a linear increase in model size and inference time as the KG size grows. Another drawback is that KG updating requires full re-training of KGEs, rather than simply learning new embeddings for updated entities or relations.
+
+Application of language models (LMs) to KGC tasks is a promising direction (Pan et al., 2023). State-of-the-art performance in most natural language processing (NLP) tasks has recently been demonstrated by pre-trained language models like BERT (Devlin et al., 2019) and more recent large or foundational language models (Brown et al., 2020; Chowdhery et al., 2022; Touvron et al., 2023). Language models-based approaches, such as KG-BERT (Yao et al., 2019), BERTRL (Zha et al., 2022), KGT5 (Saxena et al., 2022) and GenKGC (Xie et al., 2022) consider a triplet in the KG as a text sequence. While distance-based
+
+methods incorporate more structural information in dense embeddings, language models tend to capture more semantic information contained in both relations and entities of KG, enabling such models to embed previously unseen entities and operate on new facts based on semantic information contained in triplets textual representation. Thus, KGT5 positions KG link prediction as sequence-to-sequence tasks. KGT5 model trained to generate target node using textually verbalized triplets achieves competitive performance and offers a scalable and simple framework for KGC and QA tasks.
+
+Current methods based on language models (KG-BERT, BERTRL, KGT5, and GenKGC) rely only on information that can be extracted from LMs parameters. However, since KG itself is available, we build a method that relies on both internal language model knowledge and external information from KG. For the link prediction task  $(h,r,\ref{eq:1})$ , we propose to extract nodes and relations adjacent to  $h$  from the KG, i.e. the neighborhood. Node  $h$  with its neighborhood better together represents  $h$  itself: neighborhood may contain clues such as Palo Alto and United States of America for the triplet (Steve Jobs, place of birth, ?) on Figure 1.
+
+Though some efforts have been made to incorporate neighborhood into the KGC algorithms (Chen et al., 2020), our proposed pipeline inherits the benefits of generative LLMs compared to KGE approaches: (1) scalability and size of the model, (2) applicability to both transductive and inductive KGs due to an ability to generalize unseen entities, (3) no need to rank all possible candidate triplets due to direct generation of tail entity. Also, a concurrent work by the KGT5 authors, KGT5-context (Kochsiek et al., 2023), proposed a similar idea of integrating node neighborhood in the context of the generative LM model, supporting the main concerns and results of our study.
+
+Our contributions are as follows: (1) we propose a generative graph completion method which effectively integrates language models with graph neighborhood information. (2) We show that incorporating graph information into multiple generative language models achieve strong performance improvement of link prediction in both transductive and inductive settings. Moreover, our approach achieves state-of-the-art results on the inductive ILPC dataset. (3) We analyze and evaluate the impact of adding neighborhood information on the performance of the models.
+
+# 2 Methods
+
+One can represent KG more formally as  $\mathcal{G} = (\mathcal{T},\mathcal{E},\mathcal{R})$ , where  $\mathcal{E}$  is a set of entities,  $\mathcal{R}$  is a set of relations and  $\mathcal{T}$  is a set of triplets  $\mathcal{T} \in \mathcal{E} \times \mathcal{R} \times \mathcal{E}$ . Therefore, the formulation of the KGC task is the following: for each triplet  $\mathcal{T}_i = (h,r,t)$ , where  $h \in \mathcal{E}, t \in \mathcal{E}, r \in \mathcal{R}$ , answer both  $(h,r,?)$  and  $(?,r,t)$  queries on the graph  $\mathcal{G} / \mathcal{T}_i$ . The KGC tasks can have different variations depending on the KG type: inductive and transductive. In a transductive setting the KGC task is formulated as follows: given a train KG  $\mathcal{G}_{train} = (\mathcal{T}_{train},\mathcal{E},\mathcal{R})$  train the model to predict on the inference KG  $\mathcal{G}_{inference} = (\mathcal{T}_{inference},\mathcal{E},\mathcal{R})$ , therefore in the train and inference KG there is a same set of entities and relations. In inductive KGC the model is trained on the KG  $\mathcal{G}_{train} = (\mathcal{T}_{train},\mathcal{R},\mathcal{E}_{train})$  and predicts the inference KG  $\mathcal{G}_{inference} = (\mathcal{T}_{inference},\mathcal{R},\mathcal{E}_{inference})$ , so the trained model observed only on the entities from train subgraph of KG.
+
+We followed the KGT5 (Saxena et al., 2022) approach to the KGC task using an encoder-decoder Transformer based on T5 (Raffel et al., 2020). The link prediction task was posed as follows: for each triplet  $(entity_1, relation, entity_2)$  from KG we had to predict the tail in the queries  $(entity_1, relation, ?)$  and  $(entity_2, relation^{-1}, ?)$ . To generate input for the model, we converted such queries into a text form. Apart from the query verbalization only as in KGT5, we formed a neighborhood for the node and relations for each triplet in KG. Thus, verbalized queries and their neighborhoods in the form of text were fed to the input of the model for training (see Figure 1) and later to predict incomplete triplets. We form a neighborhood of nodes that have an edge with the triplet head node (1-hop neighbors). The number of adjacent nodes may be too large to fit into the context size of selected language models. Therefore, we sort them based on relation semantic similarity. We take only those verbalized nodes and relations that fit into the max sequence length. We provide more details on neighborhood formation in Appendix B and verbalization in Appendix C.
+
+To evaluate our approach to link prediction tasks we used publically available transductive and inductive KGs. Specifically, we used the Wiki-data5M (Wang et al., 2021) dataset, which is one of the largest publicly available transductive KG up-to-date and large and small versions of ILPC
+
+dataset (Galkin et al., 2022), the largest fully inductive benchmark. ILPC inference graph size is challenging for modern GNN methods with inductive capabilities (Galkin et al., 2022). Apart from being challenging to solve by common approaches, the ILPC KGs are much more sparse than the transductive Wikidata5M. The graph size and sparseness of the data make the task hard for existing methods and result in modest performance metrics. Still, there were no existing solutions that beat the baseline provided in the ILPC paper. Both used datasets are the subsets of the real-world KG - Wikidata. Datasets' statistics are provided in the Table 4).
+
+We compared models with and without neighborhoods to evaluate our hypothesis that neighborhoods help to better together represent graph node. In addition, unlike the KGT5, we experimented with different model configurations, both fine-tuning an existing pre-trained model and training from scratch as common knowledge obtained during language models pre-training might be useful on real-world knowledge graphs like Wikidata.
+
+We used the T5-small with 60M parameters as our base model with both pretrained and randomly initialized weights to evaluate the effect of pretraining of the language model on the KG task. We added additional "[SEP]" tokens to the tokenizer with 30K tokens vocabulary to separate query and neighborhood. The model was trained using AdamW optimizer (Loshchilov and Hutter, 2019), 1e-5 learning rate,  $10\%$  dropout, and batch size equals 320. The number of training steps was equal to 5M for the Wikidata5M dataset and 2M for all others. For multi-gpu training, we used the Horovod parallelization framework (Sergeev and Balso, 2018). In cases where a batch size equal to 320 did not fit GPU, we used gradient accumulation steps. We also benchmarked OpenAI gpt-3.5-turbo<sup>1</sup> zero-shot link prediction and evaluate the benefits of a neighborhood on LLM (see Appendix G). The code for all the described experiments is available at the link<sup>2</sup>.
+
+As KGT5, our model produces a probability distribution over all possible tokens in the vocabulary at each step of decoding. Such distribution is penalized for not matching the actual distribution, i.e., for the difference between the real target token at position  $i$  and the generated token at step  $i$  using
+
+CE loss. Such an approach is principally different from distance-based methods as it does not require generating negative samples to make them further from the target prediction for each training query. In this way, training iteration becomes independent of the KG size.
+
+To form predictions, we first verbalize the query (head, relation,?) and pass it as an input to the generative model to produce an output sequence, which will be a verbalization of the target entity id. For inference, we employed two strategies. The first one was sampling  $k$  most probable sequences from the decoder. We follow the KGT5 (Saxena et al., 2022) setting and use a sample size of 50. We apply this approach to Wikidata5M datasets as it is transductive. The second option for inference was to search for the closest entities in KG to the generated one. We apply the second approach to ILPC inductive datasets as language models have to generate previously unseen entities. More details on inference procedure are in Appendix D.
+
+# 3 Results
+
+The hypothesis behind our approach was that neighborhood information incorporated in a context fed to the language model should enhance link prediction of generative LM. Tables 1 and 2 demonstrate the performance of our technique on Wikidata5M and ILPC datasets under various conditions, including training a T5 model with and without neighbors, using a pre-trained model or training a model from scratch, and zero-shot with gpt-3.5-turbo.
+
+These results support the hypothesis that the quality of link prediction increases with the use of neighbors in the context of the generative model in both transductive and inductive settings. Moreover, our approach demonstrated SOTA performance on the ILPC dataset, while on Wikidata we achieved the best result among generative link prediction models, which is still comparable to scores of SOTA graph and LM methods with many more parameters. Relatively small absolute values of scores on the ILPC dataset can be explained by its fully inductive setup and larger sparseness compared to Wikidata5M. Still, there are no publicly available solutions that exceed the baseline score provided by ILPC authors. Our solution overperforms the existing baseline, is elegantly simple, and more interpretable than GNN-based solutions. Experiments on pre-trained models revealed that pre-training speeds up convergence but degrades
+
+Table 1: Adding a neighborhood persistently improves results on the link prediction task. We report Hits@k metrics on Wikidata5M, ILPC-small, and ILPC-large test sets. Arrows  $\uparrow\downarrow$  show the effect of the neighborhood on language models. Neighbors allow to achieve the best performance considering the number of model parameters.  
+
+<table><tr><td rowspan="2">MODEL</td><td colspan="3">WIKIDATA5M</td><td colspan="3">ILPC-SMALL</td><td colspan="3">ILPC-LARGE</td><td rowspan="2">#PARAMS</td></tr><tr><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td><td>H@1</td><td>H@3</td><td>H@10</td></tr><tr><td>KGT5 (SAXENA ET AL., 2022)</td><td>0.267</td><td>0.318</td><td>0.365</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>TRANSE (BORDES ET AL., 2013)</td><td>0.170</td><td>0.311</td><td>0.392</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2,400M</td></tr><tr><td>COMPLEX (TROULLON ET AL., 2016)</td><td>0.255</td><td>-</td><td>0.398</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>614M</td></tr><tr><td>KEPLER (WANG ET AL., 2021)</td><td>0.173</td><td>0.224</td><td>0.277</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>125M</td></tr><tr><td>SIMKGC (WANG ET AL., 2022)</td><td>0.313</td><td>0.376</td><td>0.441</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>220M</td></tr><tr><td>INDNODEPIECE (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.007</td><td>0.022</td><td>0.092</td><td>0.037</td><td>0.054</td><td>0.125</td><td>15.5K</td></tr><tr><td>INDNODEPIECEGNN (GALKIN ET AL., 2022)</td><td>-</td><td>-</td><td>-</td><td>0.076</td><td>0.140</td><td>0.251</td><td>0.032</td><td>0.073</td><td>0.146</td><td>24K</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED</td><td>0.240</td><td>0.286</td><td>0.323</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL NOT PRE-TRAINED + NEIGHBORS</td><td>0.327↑</td><td>0.363↑</td><td>0.386↑</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED</td><td>0.205</td><td>0.248</td><td>0.282</td><td>0.073</td><td>0.117</td><td>0.177</td><td>0.112</td><td>0.137</td><td>0.194</td><td>60M</td></tr><tr><td>T5-SMALL PRE-TRAINED + NEIGHBORS</td><td>0.306↑</td><td>0.342↑</td><td>0.361↑</td><td>0.076↑</td><td>0.137↑</td><td>0.209↑</td><td>0.114↑</td><td>0.139↑</td><td>0.200↑</td><td>60M</td></tr><tr><td>T5-BASE PRE-TRAINED</td><td>-</td><td>-</td><td>-</td><td>0.073</td><td>0.130</td><td>0.199</td><td>0.110</td><td>0.136</td><td>0.198</td><td>220M</td></tr><tr><td>T5-BASE PRE-TRAINED + NEIGHBORS</td><td>-</td><td>-</td><td>-</td><td>0.082↑</td><td>0.142↑</td><td>0.216↑</td><td>0.116↑</td><td>0.141↑</td><td>0.215↑</td><td>220M</td></tr></table>
+
+Table 2: Neighbor information improves zero-shot link prediction with OpenAI gpt-3.5-turbo-0613. *ILPC-Large results were obtained on 5000 random samples from the test set.  
+
+<table><tr><td rowspan="2">DATASET</td><td colspan="2">GPT</td><td colspan="2">GPT+NEIGH</td></tr><tr><td>H@1</td><td>H@3</td><td>H@1</td><td>H@3</td></tr><tr><td>WIKIDATA5M</td><td>0.098</td><td>0.131</td><td>0.113↑</td><td>0.157↑</td></tr><tr><td>ILPC-SMOTE</td><td>0.079</td><td>0.107</td><td>0.084↑</td><td>0.118↑</td></tr><tr><td>ILPC-LARGE*</td><td>0.103</td><td>0.134</td><td>0.114↑</td><td>0.150↑</td></tr></table>
+
+the quality of the model by a few points compared to the one trained from scratch.
+
+Also, we demonstrated that neighbors' information enhances performance across different generative models. This includes open-source models such as T5, as well as proprietary models like OpenAI GPTs that are distributed as a model-as-a-service. Although both types of models have their own advantages and limitations, it should be noted that OpenAI GPTs are black box models that cannot be modified or interpreted further. Usage of OpenAI GPTs is restricted in the case of more specialized or proprietary KGs that can store secure or non-public information. In the context of our study, OpenAI's gpt-3.5-turbo has a much larger size and cost, compared to smaller and transparent T5, still our approach improves the performance of both models.
+
+To get an understanding of how proposed models learn to use neighboring triplets we compared predictions of models trained with and without graph information in the input. One possible case when neighbors help in link prediction is when a target entity is somehow mentioned in the model input. Because of neighborhood filtering, the target triplet never appears in the model input, however in some cases target entity can occur with different relations
+
+or as a substring of another entity in a neighborhood or even in the task itself (Table 3). Despite the tail appearing in input only in a small portion of the test set, the neighborhood model learns to make use of such hints, outperforming the baseline T5 in all selected cases. The high solution rate of more than 4 out of 5 samples with hinted targets supports the importance of properly selected neighborhoods.
+
+![](images/5698b3c281989d6aa2353541bb11b453758af5dddfb1167abd98b62546208800.jpg)  
+Figure 2: KG neighborhood is critical for the model performance. The plot shows how the quality of link prediction depends on the number of neighborhood triplets removed from the input context Wikidata5M T5-small not pre-trained model. The blue solid line corresponds to removing the most relevant triplets and the dashed red line represents random selection.
+
+To evaluate the impact of neighborhoods, we analyzed triplets' importance by sorting them based on how they affect the probability of the target node prediction. As Figure 2 shows, a performance of the model quickly deteriorates with the removal of important triplets from the context. This implies potential avenues for improvement by prioritizing significant triplets selection prior to model training. Moreover, we analyzed attention maps of the model train on Wikidata5M and found that it indeed pays attention to relevant neighbors, e.g. Appendix F.
+
+The analysis shows that adding relevant graph
+
+Table 3: Adding relevant neighbors improves link prediction for T5-small on the Wikidata test set. The table shows the exact match depending on a hint of the target entity's appearance as a part of the model input.  
+
+<table><tr><td>Target position</td><td>T5</td><td>T5 + Neighbors</td><td>frequency</td></tr><tr><td>In task</td><td>0.93</td><td>0.94↑</td><td>5 %</td></tr><tr><td>In neighborhood</td><td>0.34</td><td>0.82↑</td><td>8 %</td></tr><tr><td>Not in input</td><td>0.18</td><td>0.24↑</td><td>87 %</td></tr><tr><td>Total</td><td>0.23</td><td>0.32↑</td><td>100 %</td></tr></table>
+
+context information greatly enhances link prediction. Hence one way of improving performance is increasing neighborhood size by adding k-hop neighbors. Due to the quadratic complexity of attention and the limited input size of pretrained transformers, processing exponentially growing neighborhoods would be computationally expensive. To deal with large input sizes one can use efficient T5 architectures with input sizes up to 32k (Guo et al., 2022) and 64k (Ainslie et al., 2023) tokens or recurrent approaches (Bulatov et al., 2022; Hutchins et al., 2022) that can sequentially process effectively unlimited neighborhood sizes. Another potential way of subgraph selection is to add relevant but more distant knowledge graph information, such as random walks or more sophisticated methods (Das et al., 2018).
+
+# 4 Conclusions and Future work
+
+Our study found that incorporating KG neighborhood information into a generative model's context can enhance the quality of the link prediction task. We trained the T5-small model on transductive Wikidata5M and inductive ILPC datasets with various configurations and showed that coupling the node and its neighborhood information together better represents the node. Our approach significantly outperformed baselines on the largest real-world KGCs, with a comparable or fewer number of parameters. We also demonstrated that OpenAI's gpt-3.5-turbo performance on the KGC task also benefits from neighborhood information without prior fine-tuning. In addition to the quality improvement, we analyzed the impact of neighborhood imputation, highlighting the significance of selecting relevant triplets or extending the model's context in future works. Furthermore, future studies could explore the impact of model size, the effect of neighborhood imputation on the datasets from other domains, and neighborhood selection strategies.
+
+# Limitations
+
+Limitations of this study include the training of only relatively small models, with only T5 being employed. Further study of the benefits of larger models applied to KGC tasks should be performed. Additionally, each knowledge graph in this study was limited to only one model, which may not accurately represent more complex graphs that involve multiple datasets. Another limitation is that language models are better suited for real-world textual data and may not work well for KGC representing more specialized concepts, such as biology or medicine. Also, although neighborhood selection was performed, we cannot guarantee that the selected neighborhoods are optimal. Further works should include the way of extending the ability of models to capture larger parts of a graph or selecting the most relevant neighbors. Moreover, constructing prompts for testing GPT performance may not be optimal, and the model's maximal performance cannot be guaranteed without knowledge of its training data.
+
+# Ethics Statement
+
+As a field of study in the intersection of natural language processing and knowledge graphs, this work inherits the main ethical considerations of both of these domains. Automated graph completion tasks may produce various types of mistakes, including but not limited to factual and grammatical errors. Consequently, any application based on this work should employ robust error detection mechanisms prior to deployment. Additionally, given that our system employs a generative language model, there exists a risk of generating false or misleading content such as hallucinations and biases existing in pre-training and knowledge graph data. Users of the proposed method or any application based on it should be warned about the potential risks of using generative language models. Potential misuses may include purposeful disinformation and misleading language or factual information. Taking into account the aforementioned considerations, we believe that under ethically responsible usage this work will have a positive impact on artificial intelligence research and applications.
+
+# Acknowledgements
+
+We are grateful to Mikhail Galkin for valuable advice on knowledge graphs and introduction to the
+
+ILPC dataset. We are thankful to SberDevices for granting us access to additional computational resources. A.C., A.B., and Y.K.'s work was supported by a grant for research centers in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Moscow Institute of Physics and Technology dated November 1, 2021 No. 70-2021-00138.
+
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+
+Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
+
+Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International conference on machine learning, pages 2071-2080. PMLR.
+
+Liang Wang, Wei Zhao, Zhuoyu Wei, and Jingming Liu. 2022. Simkgc: Simple contrastive knowledge graph completion with pre-trained language models. arXiv preprint arXiv:2203.02167.
+
+Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. 2021. Kepler: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics, 9:176-194.
+
+Xin Xie, Ningyu Zhang, Zhoubo Li, Shumin Deng, Hui Chen, Feiyu Xiong, Mosha Chen, and Huajun Chen. 2022. From discrimination to generation: Knowledge graph completion with generative transformer. In Companion Proceedings of the Web Conference 2022, pages 162-165.
+
+Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Kgbert: Bert for knowledge graph completion. arXiv preprint arXiv:1909.03193.
+
+Hanwen Zha, Zhiyu Chen, and Xifeng Yan. 2022. Inductive relation prediction by bert. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 5923-5931.
+
+# A Input data pipeline
+
+![](images/e52446d7e7fac270556b26bfb341a8dbd8e2a15c981e3ad93e6742aa342e2d4b.jpg)  
+Figure 3: Visualization of the complete pipeline for the preparation of the input data of the language model: 0) pre-calculate FastText embeddings for each relation in KG and form a static similarity matrix out of them; 1) form a neighborhood for the input triplet; 2) sort the neighborhood by relation similarity to the relation in the input query; 3) verbalize and concatenate query triplet and its neighborhood.
+
+# B Neighborhood formation
+
+To augment the semantic information contained in the entity and relationship names with the structural one, we used triplet neighborhoods. A triplet neighborhood is formed as follows: for a query (head, relation, ?) search for 1-hop neighbors associated with head entity, while excluding from the formed neighborhood target tail entity, which is associated with head by relation; head can be both head or tail entity in triplets that form this 1-hop neighborhood. In this manner, we obtain the context as a neighborhood of the graph, avoiding the leakage of the target entity. The process of neighborhood formation is illustrated on Figure 4.
+
+![](images/60c8842423a94a2ddf5d89c2077e80bb83cd500968069bb1e92ea0909f266f8d.jpg)  
+Figure 4: Neighborhood formation - taking the input triplet, search for the triplets in which the head from the input triplet is either head or tail, then exclude the triplet with relation and tail from the input. As a result, there is a list of triplets that form the neighborhood for a given triplet. The represented neighborhood from the Wikidata5m KG is incomplete and textually verbalized for ease of understanding.
+
+![](images/33a5f1a557f19445f767a4ac92e9b1330d2fa15176d3bd1b8b631f0b8ae28dbe.jpg)
+
+However, some of the triplets from KGs result in extremely large neighborhoods, e.g. hub nodes such as "human". Therefore, we needed to tackle the problem of large neighborhoods to fit it into the model's context, which is limited to 512 tokens for most language models including T5. We propose to do it as follows: 1) sort the neighborhood by semantic similarity of relations from its triplets to the relation from the input triplet in order to prioritize more important information in the context; 2) limit the sorted neighborhood to 512 triplets, since this will always be at least as big as the size of the allowed context, and, after verbalization, specify the maximum length of 512 for the model tokenizer to fit the resulting verbalized neighborhood representation into the language model context. Neighborhood sorting by semantic proximity was performed using a pre-calculated matrix of cosine similarity of relations in KG, for similarity calculation the relations were embedded by the fasttext model (Bojanowski et al., 2017). Figure 5 displays a pipeline for neighborhood sorting.
+
+![](images/900ff71c1b5af1e623f3b58f162086b4f508001636b10ee89ab8e698121641b8.jpg)  
+Figure 5: Neighborhood sorting - taking the input triplet, its neighborhood, and pre-calculated similarity matrix for verbalized relation embedded by the FastText model, sort the neighborhood by the similarity of relations in its triplets to the relation in the input query.
+
+Neighborhood composition was a relatively straightforward task for a smaller graph like ILPC, but for Wikidata KG with a large number of triplets in the graph, neighborhood formation took a significant amount of time and RAM. To solve this problem, we used the MongoDB database<sup>3</sup>, which stored knowledge graph triplets in its collection in JSON format and contained indices for 'head' and 'tail' fields to speed up retrieving. In order not to store verbalized data in memory either during the verbalization process or during model training, verbalized representations were outputted to the
+
+MongoDB collection too, and retrieved by batches from the database during training.
+
+# C Triplets verbalization
+
+To pass the resulting representations of queries to the input of the language model, we had to perform a transformation of these queries and their neighborhoods into textual form. In order to do this, we first needed to generate a mapping of entity and relationship Wikidata IDs into corresponding texts. For all the datasets we performed our experiments on there is such textual mapping. However, often there is no unambiguous encoding in such mappings because many entities have the same name. The problem of non-unique textual representations concerns Wikidata KG entities, not relations, e.g. apple as a fruit of the apple tree and Apple as an American multinational technology company. To tackle this issue we used the same method KGT5 authors proposed: 1) concatenate non-unique entity names with corresponding descriptions if ones exist; 2) in case there is no description or after description, concatenation text representations cannot be disambiguated, add unique numeric ids. For all other smaller datasets we only added a unique numeric identifier at the end of the name of non-unique entities. The full disambiguation pipeline is displayed in Figure 6. No such transformations were required for KG relations, as they were fully distinguishable by their text representation.
+
+![](images/48977740a01ba71507731a50655fa00925b12280dc5ec8b0655739350e2213b3.jpg)  
+Figure 6: Disambiguation schema for wikidata5m entities: 1) concatenate entities' textual representation with corresponding description if there is one in the KG metadata; 2) if there is no description for some entities - concatenate their names with unique numerical id.
+
+The next step after obtaining 1to1 mapping of entities and relation IDs to their text representations
+
+was verbalization of the resulting queries and corresponding neighborhoods in the KG. Textual representations of the query  $(head_q, relation_q, ?)$  from KG were formed as follows:
+
+1. first part of query verbalization was formed via concatenation of headq's and relation's text mappings with "predict" string, e.g. "predict Steve Jobs place of birth";  
+2. for each i-th triplet  $(head_q, relation_i, tail_i)$  or  $(head_i, relation_i, head_q)$  from the sorted query's neighborhood we formed a string consisting of  $relation_i$  and entity not belonging to the input query, i.e. for query (Steve Jobs, place of birth,?) the verbalized triplet (Steve Jobs, occupation, entrepreneur) from its neighborhood will be "occupation entrepreneur"; in case of so-called inverse triplet, e.g. in triplet  $(head_i, relation_i, head_q)$ , where  $head_q$  is a tail of the neighborhood triplet, we add "inverse of" to the textual mapping of  $relation_i$ , i.e. for (Apple, founded by, Steve Jobs) verbalization will be "inverse of founded by Apple";  
+3. at the end we concatenate the verbalization obtained in stage (1) and all the verbalizations from stage (2) using the" [SEP] " string inserted between all the above-mentioned strings.
+
+![](images/72647039607e1bbef33364bcaf9700e471cd2be33bd1e3d15073d8246d0ac364.jpg)  
+Figure 7: Query verbalization - map each entity and relation id from query triplet and its neighborhood triplets with corresponding textual representations; concatenate "predict" string, obtained query verbalization excluding target and the sorted triplets' verbalization without head entity from the query, separating each verbalized triplet by "[SEP]" token.
+
+Example of query and its neighborhood verbalization is represented on a Figure 7.
+
+# D Link prediction inference
+
+As in KGT5 (Saxena et al., 2022), to evaluate top-k possible answers, we sample k most probable sequences from the decoder and assign them their corresponding probabilities, which is a sum of log probabilities of each token in the sequence. Then, we assign  $-\infty$  score for all the nodes that were not generated by the sampling procedure. In this way, we are producing results comparable with traditional KG models as we obtain plausibility scores for all possible triplets in KG. By such procedure, we can obtain whether a real score of the model (if the target sequence persists in k generated sequences) or the score that will be worse than a real (if the target sequence was not generated as one in the top-k probable sequences), thus not overestimating the resulting metric even though we do not score all the possible triplets. Additionally, to exclude both entities that do not belong to KG and known links from the train and valid graph in a transductive setting, we performed a filtering procedure specific to KGC evaluation protocol as described in the previous studies (Bordes et al., 2013).
+
+As a main metric for choosing the best model during evaluation, we used Exact Match (EM) or in other words Hits@1, which returns 1 in case the generated and target sequences are exactly the same or 0 otherwise. However, an exact match between the target and the generated sequence can be a rather strict metric for long target entity names, and for previously unseen entities. Thus, we came up with a new approach for scoring entities produced by generative models such as T5 for inductive KGC. In this approach, we first embedded verbalized entities from the inference part of KG by Sbert model (Reimers and Gurevych, 2019) and then indexed the resulted vectors using FAISS index (Johnson et al., 2019). In such a way, we used a simple flat FAISS index to perform a lookup for the k most similar entities from inference KG to the generated one. Using the index for the inductive ILPC dataset, we retrieve entities from the KG that are semantically closest to the generated sequence even if the generated string is imperfect, but contains the right tokens referring to the target entity. In this manner, we could calculate Hits@k retrieving top-k closest to the generated entity names in a similar way as distance-based methods. Overall, in the case of inductive setup, we used a combination of exact match (EM) and FAISS-based met
+
+rics Hits@k calculated by the following formula:  $1000EM + Hits@1$ . In this way, we give more weight to the exact match metric, but in the case of equal EM in different checkpoints, the model with the best Hits@1 will be chosen.
+
+# E Datasets statistics
+
+Table 4: Statistics of used KGs.  
+
+<table><tr><td>Dataset</td><td># Entities</td><td># Relations</td><td># Triplets</td></tr><tr><td>Wikidata5M</td><td>4.8M</td><td>828</td><td>21M</td></tr><tr><td>ILPC-small</td><td>10,230</td><td>96</td><td>78,616</td></tr><tr><td>ILPC-large</td><td>46,626</td><td>130</td><td>202,446</td></tr></table>
+
+# F Attention map interpretation
+
+![](images/6cf43a2fc0250015cb8fd0d8a8288c49ba80cf20f705226aff9c48fcdb0f7ce5.jpg)  
+Figure 8: Example of cross-encoder attention map highlighting the importance of neighborhood triplets from the input
+
+Figure 8 demonstrates a cross-encoder attention map highlighting the input neighborhood's significance for the target prediction. The query (Arman Kirakossian, country of citizenship, ?) identifies Yerevan, Armenia's capital, as the birthplace of the target entity, thereby amplifying its role in predicting the right country of citizenship.
+
+# G Zero-shot link prediction with gpt-3.5-turbo
+
+Since OpenAI API only returns generated text, we cannot get its probabilities. Therefore, we use a slightly different evaluation pipeline from other models. We generate up to three predictions with gpt-3.5-turbo-0613. If any of the randomly selected  $k$  predictions match the target entity after cleaning and normalization, we set that model prediction as correct (Hits@  $k$  in Table 2).
+
+We use the following prompts to control the gpt-3.5-turbo-0613.
+
+# G.1 Zero-shot link prediction without neighborhood
+
+System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer.
+
+User prompt: Triplet to complete: <verbalized triplet>.
+
+# G.2 Zero-shot link prediction with neighborhood
+
+System prompt: You will be provided with a incomplete triplet from the Wikidata knowledge graph. Your task is to complete the triplet with a tail (subject) based on the given triplet head (object) and relation. Triplet to complete IS NOT in the list of related nodes, but it may contain helpful clues. Your answer must only include the tail of the triplet with prefix 'Tail':'. Do not include relation into the answer.
+
+User prompt: Adjacent relations: <verbalized neighborhood>  $\backslash n$  Triplet to complete: <verbalized triplet>.
+
+# H Overview of concurrent work
+
+While our work was under review, the concurrent work KGT5-context (Kochsiek et al., 2023) was published. Following the same idea, KGT5-context uses verbalized node neighborhood as additional context for KGC with the KGT5 model. However, KGT5-context explores the applicability of neighborhood information for KGC only in transductive setup. Also, neighborhood sampling in KGT5-context was performed randomly instead of prioritizing the neighbors, which makes the results less reproducible and does not consider the importance of neighbors in the context of a query. Furthermore, we show that this approach generalizes well to other pre-trained language models like gpt-3.5-turbo in a zero-shot setting without pre-training on KGC datasets.
+
+We have made the effort and tried our best to reproduce the results of the KGT5 model (Saxena et al., 2022) on Wikidata5M. We report the results of T5-small not pre-trained (Table 1), which is our version of the KGT5 model. Therefore, all our other models were trained and evaluated in
+
+the same setting and with the same pipeline as for T5-small not pre-trained model.

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2023/Beyond Candidates _ Adaptive Dialogue Agent Utilizing Persona and Knowledge/full.md

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+# Beyond Candidates
+
+# : Adaptive Dialogue Agent Utilizing Persona and Knowledge
+
+# Jungwoo Lim*, Myunghoon Kang*, Jinsung Kim*, Jeongwook Kim, Yuna Hur, Heuiseok Lim†
+
+Department of Computer Science and Engineering, Korea University {wjddn803,chaos8527,jin62304,k0s1k0s1k0,yj72722,limhseok}@korea.ac.kr
+
+# Abstract
+
+To build ultimate dialogue agents, previous studies suggest models that ground both persona and knowledge. However, applying the dialogue system directly to the usual conversation is still limited because the system requires a complete sentence-formed persona and knowledge candidate sets from the given dataset. In contrast to the dialogue setting in the dataset, humans utilize semantic concepts in their minds rather than a set of predefined candidate sentences. Following this manner of human dialogue, we suggest an adaptive dialogue system that is applicable to situations where complete sentence-formed candidates are not given. Our model generates consistent and relevant persona descriptions and identifies relevant knowledge for engaging and knowledgeable responses, even with fragmentary information. We show that our model outperforms previous baselines that utilize persona and knowledge candidate sentences and conduct the human evaluation on the machine-generated responses. In addition, we conduct ablation studies to demonstrate the effectiveness of each component of our model. Furthermore, we apply our model to other dialogue datasets that only ground knowledge or persona to showcase its adaptability. Our code is available at https://github.com/dlawjddn803/BeCand.
+
+# 1 Introduction
+
+In usual conversations, humans utilize the semantic concept in their minds in terms of the dialogue topic and the preference of the interlocutor. With the semantic-level of concepts, humans communicate each other by aggregating the concepts to convey knowledgeable and empathetic responses (Collins and Quillian, 1969). It implies that people converse by adaptively reorganizing and retrieving additional information with their
+
+*These authors contributed equally to this work.
+
+† Corresponding author.
+
+# (a) Candidate-Free Conversational Setting
+
+![](images/dbaa901768fa6a78f607bfcdb3ca0c7f5382f2da3c1a7eb3da2dddbbdcf74386.jpg)
+
+Human: Ah, it must look very similar to somewhere I have been before. What is this studio known for?
+
+Machine's Answer (Ours):
+
+As you are interested in filmmaking, you might have heard of this studio before. This studio is known for the first American studio of its kind.
+
+# (b) Previous Conversational Setting
+
+Topic : Norman Studios
+
+Persona Candidates :
+
+1) I am interested in filmmaking.  
+2) I am a dancer.  
+3) I enjoy visiting museums.
+
+Knowledge Candidates :
+
+1) Norman Studios was an American film studio in Jacksonville, Florida. Founded by Richard Edward Norman, the studio produced silent films featuring African-American casts from 1919 to 1928.  
+2) The roof of this temple was of marble. Many diagrams and reconstructions of this structure show a door in the western side-wall; ...  
+3) Since its opening day, the Staples Center has hosted seven NBA Finals series with the Lakers, the 2012 and 2014 Stanley Cup Finals, three WNBA Finals ...
+
+Human: Ah, it must look very similar to somewhere I have been before. What is this studio known for?
+
+Machine's Answer (BART):
+
+The studio produced silent films from the 1920s to the present.
+
+The studio is considered one of the most important silent film studios in the world.
+
+Figure 1: Comparison of conversational settings. (a) is a candidate-free conversational setting and the machine's answer in (a) is a generated response from our model. (b) is a previous conversational setting and the machine's answer in (b) is the response from the BART-large model trained on FoCus dataset.
+
+semantic concepts, encompassing knowledge and persona, not by relying on pre-defined sources (Young et al., 2018; Zou et al., 2021; Li et al., 2023).
+
+It seems that Jang et al. (2022a) and Lim et al. (2022) adhere to this human-like approach
+
+on the conversation by referring to persona and knowledge. However, it neglects the humans' semantic concept reconstruction and retrieval capability by requiring pre-defined candidate sets to ground as in Figure 1 (b). As knowledge and persona candidates for the agents are not given in usual conversation, the dependency on the candidates eventually limits their applicability to candidate-free situations as depicted in Figure 1 (a).
+
+To build the dialogue agents adaptive to the candidate-agnostic situation, two branches of studies are conducted. In knowledge-grounded conversation, the knowledgeable agents employ the non-parametric memory-based retrieval to overcome candidate-agnostic situations (Lewis et al., 2020b; Paranjape et al.). Similarly, persona-aware dialogue agents consider the out-of-persona situations by extending persona sentences from a few persona concept (Xu et al., 2020; Liu et al., 2022; Li et al., 2023). Even though both streams of research focus on the candidate-agnostic conversational situation, they only leverage a single source for grounding, rather than utilizing both persona and knowledge, simultaneously.
+
+In this paper, we propose a dialogue agent utilizing persona and knowledge that is adaptive to the candidate-free situation. To this end, our method consists of 1) a knowledge-retriever 2) a concept-based persona generator, 3) a dialogue-persona aligner, and 4) a response generator. When the knowledge concept is given, a knowledge retriever finds the relevant knowledge from the knowledge base. Our concept-based persona generator then produces complete sentences with fragmentary persona concepts. The generated persona descriptions are then validated based on the persona aligner regarding both consistency and relevancy. The validated persona descriptions are used as the input of the response generator.
+
+Experimental results show that our candidate-free model outperforms other baselines. Also, we show that the concept-based persona generator and persona aligner boost the performance of the dialogue agents with the ablation studies. We conduct the human evaluation of our model's responses, and the result implies that our method is effective in building a persona-knowledge dialogue agent without candidate sentences. Moreover, we demonstrate that our method is capable of utilizing other dialogue datasets grounding single source,
+
+such as PersonaChat (Zhang et al., 2018) or Wizard-of-Wikipedia (WoW) (Dinan et al., 2018), and shows the adaptiveness of our proposed model. In qualitative results, it is shown that the generated responses are comparable to the ground truth answers without the given candidates.
+
+# 2 Related Works
+
+# 2.1 Knowledge-grounded Dialogue System
+
+For the informative dialogue generation, Dinan et al. (2018) and Zhou et al. (2018) introduce open-domain dialogue datasets. Various works directly exploit external knowledge to obtain informative responses (Karpukhin et al., 2020; Lee et al., 2021; Wu et al., 2022a) in knowledge-grounded conversation. Other studies exploit augmenting knowledge base to the language model with non-parametric memory-based retriever (Lewis et al., 2020b; Guu et al., 2020; Izacard and Grave, 2021). It is found that a retrieval-augmented generator also reduces hallucination in knowledge-grounded conversation as well (Shuster et al., 2021), and a similar approach recently achieves comparable performance in knowledge-grounded conversation (Paranjape et al., 2021).
+
+# 2.2 Persona-grounded Dialogue System
+
+Also, persona-concentrated datasets have been proposed for constructing persona-engaging dialogue agents (Zhang et al., 2018; Rashkin et al., 2019; Dinan et al., 2020; Smith et al., 2020). While Song et al. (2020) and Wu et al. (2021) focus on injecting persona with utterance post-editing, Zheng et al. (2020) devises the attention routing mechanism for handling persona dialogue. Furthermore, another research takes into account the consistency and relevancy of the persona by employing natural language inference-based critic with a consistency score in reinforcement learning. Moreover, to maintain a consistent persona perceived by the dialogue agent, Bae et al. (2022) use iterative feedback between pre-trained language models (PLMs) and human annotators.
+
+Along with the previous studies, research that attempts to expand the persona sentences to cover candidate-free conversational settings has appeared. In other words, when given fragmentary information on the user's persona, research to complete the insufficient information is conducted using retrieval (Liu et al., 2022; Majumder et al.,
+
+2021; Han et al., 2022) or generation (Zhou et al., 2021; Lu et al., 2022). In addition, the persona extension approach leveraging commonsense is introduced. Majumder et al. (2020) expands the given persona sentences by fetching additional information from a commonsense knowledge graph.
+
+# 2.3 Persona and Knowledge Grounded Dialogue System
+
+In recent studies, there has been research on fusing persona and knowledge to generate engaging and knowledgeable responses. Fu et al. (2022) suggest a persona memory in knowledge selection for persona-consistent response generation. In addition, Jang et al. (2022b) and Lim et al. (2022) propose a model that explicitly grounds both persona and knowledge simultaneously. Along with the studies, Wu et al. (2022b) ground persona, knowledge, and commonsense by fusing separate encoder-decoder structures. Furthermore, Blenderbot 3 (Shuster et al., 2022) models the persona as the long-term memory of both users and chatbot. However, none of these systems cannot be adopted to the candidate-free conversation setting due to the dependency on the given sentence-formed candidates.
+
+# 3 Method
+
+We propose adaptive dialogue agents that generate the responses without the persona and knowledge candidates. To this end, we assume that the knowledge and persona concepts are only given to the agent for knowledgeable and engaging responses. First, 1) knowledge retriever retrieves the relevant paragraphs with the knowledge concept, and 2) concept-based persona generator produces the persona descriptions with the given short persona concepts. Then, 3) persona aligner decides whether the generated persona descriptions are relevant to the dialogue history and whether the sentences are consistent with the previous dialogue history. Afterward, 4) response generator provides knowledgeable and engaging responses with the predicted knowledge paragraphs and persona descriptions.
+
+# 3.1 Notation
+
+The given dialogue  $D$  is notated as  $\{(u_1^{hm}, u_1^{mc}), \ldots, (u_n^{hm}, u_n^{mc})\}$  and where  $n$  is the number of rounds.  $u^{hm}$  and  $u^{mc}$  denote
+
+the utterances of human and machines, respectively. The dialogue history  $H$  is  $\{(u_{n - w}^{hm}, u_{n - w}^{mc}), \dots, (u_{n - 1}^{hm}, u_{n - 1}^{mc}), (u_n^{hm})\}$
+
+where  $w$  is the window size. The set of given persona sentences  $P = \{p_1, p_2 \dots p_{|P|}\}$  and  $|P|$  is the number of persona sentences. Also,  $C^P = \{c_1^p, c_2^p, \dots, c_{|P|}^p\}$  indicates the persona concepts whereas  $C^K$  is a knowledge concept which is a title of the knowledge.
+
+# 3.2 Knowledge Retriever
+
+To let the model adapt to the situation where the knowledge candidates are absent, we use a nonparametric memory-based retrieval. We combine the query encoder and dense vector index, which is obtained from a pre-trained dense passage retriever (DPR) (Karpukhin et al., 2020) for enhanced semantic search. The retriever refers to the knowledge index from the Wikipedia knowledge which is leveraged with FAISS (Johnson et al., 2019) library. Therefore, our retriever  $R(\cdot)$  finds the relevant knowledge from the index with the knowledge concept  $C^K$  by using maximum inner-product search (MIPS) following Lewis et al. (2020b). The predicted top-k relevant paragraphs are then used as the input for the model and denoted as  $\hat{K}$ .
+
+$$
+R (\hat {K} | C ^ {K}) \propto e x p (\mathbf {e} (\hat {K}) ^ {\top} \mathbf {q} (C ^ {K})), \tag {1}
+$$
+
+where  $\mathbf{e}(\cdot)$  is an embedding from a context encoder, and  $\mathbf{q}(\cdot)$  is a representation from a query encoder, both implemented with BERT (Kenton and Toutanova, 2019) pre-trained on natural-question dataset (Kwiatkowski et al., 2019).
+
+# 3.3 Concept-based Persona Generator
+
+To let the model exploit the semantic concept from the candidate-free situation, we propose a concept-based persona generator to provide complete persona descriptions only with the persona concepts. In detail, our persona generator is pre-trained to generate plausible full persona descriptions with only persona concepts in a retrieve-and-generate manner, following Hashimoto et al. (2018). Then, we freeze the persona generator for the response generation.
+
+For the pre-training process, we first build the persona pool with the collections of unique persona sentences from FoCus (Jang et al., 2022a) and PersonaChat (Zhang et al., 2018).
+
+![](images/b4ea123b18a5967388ddb0a2c53e7643a5aef6692e120854909ce6f66262e8a8.jpg)  
+Figure 2: Overall architecture of our model.
+
+Then, we pre-train the persona retriever using DPR (Karpukhin et al., 2020) and regard highly ranked persona sentences from BM25 (Robertson and Zaragoza, 2009) as negative samples. We then train the generator by considering top  $k$  relevant persona sentences  $P_{k}^{\prime} = \{p_{1}^{\prime}, p_{2}^{\prime}, \dots, p_{k}^{\prime}\}$  as positive samples with BART (Lewis et al., 2020a). Our concept-based persona generator provides complete persona sentences  $G^{P}$ . The training details are presented in Appendix A.
+
+# 3.4 Persona Aligner
+
+The persona aligner consists of two modules, i.e., the persona consistency (PC) module and the persona relevancy module (PR). When the generated persona sentences  $G^{P}$  are obtained, the persona consistency module predicts whether the generated persona sentences contradict the previous dialogue history  $H$ . However, collecting the labels of generated personas' consistency is time-consuming and labor-intensive. Therefore, we distill ChatGPT (OpenAI-Blog, 2022) as model annotators with the BERT-base model (Kenton and Toutanova, 2019) inspired by the high reasoning capability on natural language inference of the ChatGPT (Laskar et al., 2023). We asked the ChatGPT to predict whether the single persona sentence contradicts the given dialogue or not. The prompt for an alignment check is illustrated in Appendix 9. Then, the consistency module trains on the label that ChatGPT provided in a
+
+binary classification manner. The trained persona consistency module is then frozen and predicts whether the sentence is consistent with the dialogue history in the inference stage.
+
+$$
+\hat {G} _ {i} ^ {P C} = \operatorname {P C} ([ H; G _ {i} ^ {P} ]) \tag {2}
+$$
+
+Different from the persona consistency module, the persona relevancy module takes charge of the relevancy by selecting proper persona sentences that are relevant to dialogue. Even though the persona descriptions do not conflict with the dialogue history, it is still unrevealed the level of relevancy of the persona sentence. For enhanced relevancy prediction, we first separately encode the dialogue and generated persona sentences with the question encoder with DPR, and obtain each hidden state from the last layer. Then, we concatenate the embeddings and pass them into the two linear layers to predict the relevancy of the persona sentences to the dialogue.
+
+$$
+\hat {G} _ {i} ^ {P R} = \operatorname {P R} ([ H; G _ {i} ^ {P} ]) \tag {3}
+$$
+
+If the two modules both predict the sentence as relevant and consistent, we assume the sentences are aligned with the given dialogue.
+
+$$
+\hat {G} _ {i} ^ {P C R} = \left\{\hat {G} _ {i} ^ {P C} \cap \hat {G} _ {i} ^ {P R} \right\}, \tag {4}
+$$
+
+We compute the loss as Equation 5.
+
+$$
+\begin{array}{l} \mathcal {L} _ {P} = \\ - \sum_ {j} l _ {j} \cdot \log \hat {l _ {j}} + (1 - l _ {j}) \cdot \log (1 - \hat {l _ {j}}) \tag {5} \\ \end{array}
+$$
+
+<table><tr><td>Model</td><td>Backbone</td><td>Candidate Usage</td><td>BLEU</td><td>chrF++</td><td>R-1</td><td>R-2</td><td>R-L</td></tr><tr><td colspan="8">Baselines (w/ Candidates)</td></tr><tr><td rowspan="4">Jang et al. (2022a)</td><td>GPT2-small</td><td>O</td><td>11.43</td><td>28.73</td><td>36.58</td><td>19.44</td><td>32.62</td></tr><tr><td>GPT2-medium</td><td>O</td><td>12.31</td><td>30.12</td><td>38.29</td><td>21.17</td><td>34.12</td></tr><tr><td>BART-base</td><td>O</td><td>11.99</td><td>29.77</td><td>36.24</td><td>19.73</td><td>32.13</td></tr><tr><td>BART-large</td><td>O</td><td>11.91</td><td>30.69</td><td>36.57</td><td>19.83</td><td>32.05</td></tr><tr><td>INFO (Lim et al. (2022))</td><td>RAG</td><td>O</td><td>31.46</td><td>53.29</td><td>58.26</td><td>42.35</td><td>53.06</td></tr><tr><td colspan="8">w/o Candidates</td></tr><tr><td>Lewis et al. (2020a)</td><td>BART-large</td><td>X</td><td>13.14</td><td>31.45</td><td>38.67</td><td>19.93</td><td>34.13</td></tr><tr><td>Lewis et al. (2020b)</td><td>RAG</td><td>X</td><td>15.90</td><td>35.50</td><td>41.21</td><td>22.80</td><td>36.45</td></tr><tr><td rowspan="2">Ours</td><td>BART-large</td><td>X</td><td>20.86</td><td>40.12</td><td>45.89</td><td>28.06</td><td>40.97</td></tr><tr><td>RAG</td><td>X</td><td>20.30</td><td>39.53</td><td>45.17</td><td>28.05</td><td>40.51</td></tr></table>
+
+Table 1: Focus Results. Main results on the official validation set. The models are evaluated by generation metrics, including BLEU,chrF++,ROUGE-1 (R-1),ROUGE-2 (R-2),ROUGE-L (R-L).
+
+Note that  $l_{j}$  is the ground-truth label of the  $j$ -th example.
+
+# 3.5 Response Generator
+
+With the predicted relevant knowledge passages and persona descriptions, we concatenate them into one sequence along with the concept of the knowledge and dialogue as  $I = [C^K; H; \hat{G}^{PCR}; \hat{K}]$ . Then, we pass into the generative language model to obtain the responses, and the language modeling loss is computed as Equation 6.
+
+$$
+\mathcal {L} _ {L M} = - \sum_ {i = 1} ^ {T} \log \operatorname {P r o b} \left(t _ {i} \mid t _ {1}, \dots , t _ {i - 1}\right), \tag {6}
+$$
+
+where  $\operatorname{Prob}(\cdot)$  denotes a probability of the generative language model,  $t_i$  is  $i$ -th token of target sentence, and  $T$  is the number of tokens. The final loss function  $\mathcal{L}_{Final}$  is computed as Equation 7 and  $\lambda_P$  and  $\lambda_{LM}$  are hyperparameters.
+
+$$
+\mathcal {L} _ {\text {F i n a l}} = \lambda_ {P} \mathcal {L} _ {P} + \lambda_ {L M} \mathcal {L} _ {L M} \tag {7}
+$$
+
+# 4 Experimental Setup
+
+# 4.1 Dataset
+
+FoCus (Jang et al., 2022a) is a dataset designed for dialogue models that utilize knowledge and persona simultaneously. It comprises 12,484 dialogues, 5,152 Wikipedia knowledge, and 32,855 persona sentences. We conduct the experiments on the official FoCus validation set since the official test set can only be confirmed via the leaderboard on the Codalab platform<sup>1</sup>.
+
+Wizard of Wikipedia (WoW) WoW (Dinan et al., 2018) contains 22,311 dialogues with 201,999 turns that utilize Wikipedia articles, primarily aimed at facilitating knowledge-based dialogue. In WoW datasets, the wizard responds to the apprentice based on selected knowledge. We utilized the test splits for Wizard of Wikipedia (WoW) for the experiments.
+
+PersonaChat PersonaChat (Zhang et al., 2018) consists of 162,064 utterances between two interlocutors, each with 3-5 different persona sentences. The interlocutors converse with their persona, attempting to figure out the persona of others. We used the revised validation set for PersonaChat experiments.
+
+# 4.2 Baselines
+
+BART (Lewis et al., 2020a) is a language model that integrates concepts from both autoregressive and denoising autoencoder models, making it highly effective for text generation. For the experiments, we only use the BARTlarge model.
+
+RAG Retrieval-augmented generator (RAG) (Lewis et al., 2020b) is a model which combines pre-trained parametric and non-parametric memory for language generation. It shows comparable performances on open-domain question-answering and conversation.
+
+BART+PG+KG Jang et al. (2022a) introduce the dialogue system that can provide personalized responses by considering the interlocutor's persona and external knowledge. The model finds relevant paragraphs by considering the last utterance and selects proper sources from the persona (PG) and
+
+<table><tr><td>Dataset</td><td>Model</td><td>Backbone</td><td>Info. ↑</td><td>Hal. (K) ↓</td><td>Hal. (P) ↓</td><td>Rel. (P) ↑</td><td>Con. (P) ↑</td><td>Flu. ↑</td></tr><tr><td rowspan="4">FoCus</td><td>Lewis et al. (2020a)</td><td rowspan="2">BART-large</td><td>2.33</td><td>1.97</td><td>1.57</td><td>2.06</td><td>2.11</td><td>2.55</td></tr><tr><td>Ours</td><td>2.42</td><td>1.74</td><td>1.44</td><td>2.32</td><td>2.39</td><td>2.61</td></tr><tr><td>Lewis et al. (2020b)</td><td rowspan="2">RAG</td><td>2.15</td><td>1.98</td><td>1.46</td><td>1.86</td><td>1.99</td><td>2.29</td></tr><tr><td>Ours</td><td>2.52</td><td>1.64</td><td>1.38</td><td>2.48</td><td>2.46</td><td>2.60</td></tr></table>
+
+Table 2: Human evaluation results. Info.: informativeness, Hal.(K): hallucination in knowledge and Hal.(P): hallucination in persona, Rel. (P): relevancy of persona usage, Con. (P): consistency of persona usage, Flu.: fluency.
+
+knowledge candidates (KG). After obtaining useful sources, the model generates the responses in an auto-regressive manner.
+
+INFO Lim et al. (2022) propose a conversational model that grounds both persona and knowledge. The model consists of a knowledge selector, a persona selector, and a responses generator. Each selector is implemented with polyencoder (Humeau et al., 2020) and the output is used as the query for the response generator based on RAG (Lewis et al., 2020b).
+
+# 4.3 Evaluation Metrics
+
+The official automatic evaluation metrics for the FoCus benchmark include BLEU (Papineni et al., 2002),  $\mathrm{chrF} + +$  (Popovic, 2017), ROUGE-1, ROUGE-2, and ROUGE-L (Lin, 2004). These metrics are frequently employed to compare machine-generated responses to gold responses in generation tasks. In our experiments on PersonaChat, we also report the performance of the unigram F1 metrics.
+
+For human evaluation, we adopt six metrics on response generation. 1) Informativeness measures the extent of the information conveyed within a response and denotes the degree of providing new, valuable, or relevant details, insights, or facts to the conversation. We also have two criteria for hallucination regarding persona and knowledge. 2) Knowledge hallucination is the metric that shows the level of hallucination of generated output that contradicts reality. Similarly, 3) Persona hallucination is the metric that indicates the hallucination level based on the given persona descriptions. Along with the persona-related metrics, 4) Persona relevancy metric denotes how much the given persona directly relates to the ongoing conversation. Moreover, 5) Persona consistency refers to how consistently the persona is maintained in a given dialogue. Lastly, 6) Fluency measures the ability to communicate
+
+smoothly, effortlessly, and coherently. Details of our experiments are provided in Appendix B.
+
+# 5 Results and Discussion
+
+# 5.1 Automatic Evaluation
+
+We conduct the experiments on FoCUS dataset to show our method's effectiveness without the given candidates. Table 1 demonstrates that our method achieves the second-highest score while the first-ranked model directly exploits the persona and knowledge candidate set. In addition, our method outperforms the performance of Jang et al. (2022a) even though it utilizes the candidates from the dataset. Furthermore, all models incorporating our method outperform their vanilla backbone models significantly.
+
+# 5.2 Human Evaluation
+
+We also demonstrate the effectiveness of our method through human evaluations. We recruit nine human workers who have at least bachelor's degree and are proficient in English. We randomly chose 30 dialogues from each datasets. We asked the workers to evaluate the machine-generated responses according to six criteria described earlier. The score is scaled from 1 to 3, and the results are indicated in Table 2. The results indicate that our method is effective in achieving both persona consistency and persona relevancy. Moreover, our method shows comparable performance in decreasing both knowledge hallucination and persona hallucination in the FoCUS dataset.
+
+# 5.3 Ablation Studies
+
+We also conduct ablation studies on our methods with respect to the knowledge retriever, concept-based persona generator, and persona aligner.
+
+Knowledge Retriever To demonstrate the effectiveness of our knowledge retriever in our model, we compare vanilla backbone which is fine-tuned on the FoCUS dataset, and our model.
+
+As shown in Table 3, incorporating relevant knowledge into the input of the vanilla generative language models enhances the performance of the response generation regardless of the backbone language models. Also, the knowledge retriever enhances the performances consistently, even when the persona generator and aligner are combined in our method. The performance decrease of the models without the knowledge retriever<sup>2</sup> suggests that our knowledge retriever is effective.
+
+<table><tr><td>Model</td><td>Backbone</td><td>K-Retr.</td><td>BLEU</td><td>chrF++</td><td>R-1</td><td>R-2</td><td>R-L</td></tr><tr><td rowspan="2">Lewis et al. (2020a)</td><td rowspan="2">BART</td><td>X</td><td>13.14</td><td>31.45</td><td>38.67</td><td>19.93</td><td>34.13</td></tr><tr><td>O</td><td>14.83</td><td>33.86</td><td>39.90</td><td>21.07</td><td>34.95</td></tr><tr><td rowspan="2">Lewis et al. (2020b)</td><td rowspan="2">RAG</td><td>X</td><td>15.90</td><td>35.50</td><td>41.21</td><td>22.80</td><td>36.45</td></tr><tr><td>O</td><td>14.66</td><td>33.90</td><td>38.79</td><td>20.42</td><td>33.87</td></tr><tr><td rowspan="4">Ours</td><td rowspan="2">BART</td><td>X</td><td>12.66</td><td>34.22</td><td>38.73</td><td>19.69</td><td>32.96</td></tr><tr><td>O</td><td>20.86</td><td>40.12</td><td>45.89</td><td>28.06</td><td>40.97</td></tr><tr><td rowspan="2">RAG</td><td>X</td><td>19.46</td><td>38.21</td><td>43.58</td><td>25.98</td><td>38.87</td></tr><tr><td>O</td><td>20.30</td><td>39.53</td><td>45.17</td><td>28.05</td><td>40.51</td></tr></table>
+
+Persona Generator We also compare the performance of models by ablating the type of persona descriptions. "GT" refers to our model utilizing ground-truth persona sentences, while "random" indicates the models with five random persona descriptions from the persona pool. As shown in Table 4, the models in random settings exhibit a decrease in performance, regardless of the backbone models. However, the proposed method based on the RAG model outperforms the model that utilizes ground-truth persona descriptions. This suggests that the generated persona descriptions from our concept-based persona generator are comparable to the ground-truth persona sentences and that our concept-based persona generator can replace the labor-intensive human annotating process.
+
+Table 3: Ablation study on knowledge retriever. K-Retr. denotes the knowledge retriever.  
+
+<table><tr><td>Model</td><td>Backbone</td><td>Persona Desc.</td><td>BLEU</td><td>chrF++</td><td>R-1</td><td>R-2</td><td>R-L</td></tr><tr><td rowspan="6">Ours</td><td rowspan="3">BART</td><td>GT.</td><td>20.86</td><td>40.23</td><td>46.31</td><td>28.42</td><td>41.37</td></tr><tr><td>Random</td><td>19.76</td><td>38.85</td><td>45.40</td><td>27.45</td><td>40.55</td></tr><tr><td>Ours</td><td>20.86</td><td>40.12</td><td>45.89</td><td>28.06</td><td>40.97</td></tr><tr><td rowspan="3">RAG</td><td>GT.</td><td>19.55</td><td>38.77</td><td>44.65</td><td>27.02</td><td>39.9</td></tr><tr><td>Random</td><td>17.50</td><td>36.51</td><td>42.50</td><td>24.72</td><td>37.8</td></tr><tr><td>Ours</td><td>20.30</td><td>39.53</td><td>45.17</td><td>28.05</td><td>40.51</td></tr></table>
+
+Table 4: Ablation study on concept-based persona generator. GT. denotes the ground truth persona sentences which are given from the dataset.  
+
+<table><tr><td>Model</td><td>Backbone</td><td>Persona Aligner</td><td>BLEU</td><td>chrF++</td><td>R-1</td><td>R-2</td><td>R-L</td></tr><tr><td rowspan="6">Ours</td><td rowspan="3">BART</td><td>GT.</td><td>20.96</td><td>40.45</td><td>48.08</td><td>29.24</td><td>42.67</td></tr><tr><td>Random</td><td>3.79</td><td>14.79</td><td>19.41</td><td>3.94</td><td>16.69</td></tr><tr><td>Ours</td><td>20.86</td><td>40.12</td><td>45.89</td><td>28.06</td><td>40.97</td></tr><tr><td rowspan="3">RAG</td><td>GT.</td><td>20.74</td><td>40.77</td><td>47.01</td><td>28.29</td><td>41.55</td></tr><tr><td>Random</td><td>17.54</td><td>36.81</td><td>42.08</td><td>23.89</td><td>37.16</td></tr><tr><td>Ours</td><td>20.30</td><td>39.53</td><td>45.17</td><td>28.05</td><td>40.51</td></tr></table>
+
+Table 5: Ablation study on persona aligner. GT. indicates the ground truth label of persona selection from the dataset.  
+
+<table><tr><td>Dataset</td><td>Model</td><td>F1</td><td>BLEU</td><td>chrF++</td><td>RL</td></tr><tr><td rowspan="6">WoW</td><td>RAG† (2020b)</td><td>-</td><td>-</td><td>-</td><td>11.57</td></tr><tr><td>FiD (2021)</td><td>-</td><td>-</td><td>-</td><td>16.06</td></tr><tr><td>Hindsight (2021)</td><td>-</td><td>-</td><td>-</td><td>17.06</td></tr><tr><td>QKConv (2022)</td><td>-</td><td>-</td><td>-</td><td>17.72</td></tr><tr><td>BART (Ours)</td><td>17.11</td><td>8.31</td><td>18.59</td><td>16.35</td></tr><tr><td>RAG (Ours)</td><td>16.39</td><td>8.07</td><td>18.33</td><td>15.91</td></tr><tr><td rowspan="6">PersonaChat</td><td>KV Profile Memory (2018)</td><td>13.65</td><td>-</td><td>-</td><td>-</td></tr><tr><td>TransferTransfo (2019)</td><td>15.71</td><td>-</td><td>-</td><td>-</td></tr><tr><td>P2Bot (2020)</td><td>19.08</td><td>-</td><td>-</td><td>-</td></tr><tr><td>LMEDR (2023)</td><td>21.99</td><td>-</td><td>-</td><td>-</td></tr><tr><td>BART (Ours)</td><td>19.46</td><td>12.78</td><td>15.7</td><td>18.44</td></tr><tr><td>RAG (Ours)</td><td>18.67</td><td>11.26</td><td>16.73</td><td>17.48</td></tr></table>
+
+Table 6: Automatic evaluation results on the other dialogue datasets.† denotes the vanilla model and the scores of other models in WoW are imported from KILT benchmark (Petroni et al., 2021).
+
+Persona Aligner We evaluate the impact of our consistency and relevancy module by employing the persona relevancy label. "GT" refers to utilizing the ground-truth persona relevancy label in the FoCus dataset, while "random" indicates the model with randomly assigned persona relevancy labels. As shown in Table 5, our method performs comparably to the GT performance when trained on both BART and RAG. However, performance significantly decreases in random settings. This indicates that our persona aligner effectively captures the consistency and relevancy of the generated persona with respect to the dialogue context.
+
+# 5.4 Adaptation to Other Dialogue Datasets
+
+To evaluate the adaptiveness of our method, we conduct experiments on the other dialogue datasets, Wizard of Wikipedia (WoW) and PersonaChat. Since both datasets consist of dialogues grounding a single source, there are no candidates for the other source. In other words, there are no persona candidates in WoW dataset, and knowledge candidates are absent in PersonaChat. Therefore, we report the results of applying our method to these single-source datasets. Table 6 demonstrates that the models with our method show comparable
+
+performances in ROUGE-L. Also, our method based on BART exceeds FiD (Izacard and Grave, 2021) which shows remarkable performances in knowledge-grounded conversation. Also, our BART-based model in PersonaChat also surpasses the  $\mathcal{P}^2$  Bot (Liu et al., 2020) according to the unigram-F1 metric.
+
+# 5.5 Qualitative Results
+
+# Knowledge Concepts
+
+Cardiff Bay Barrage
+
+# Human's Persona Concepts
+
+love, area.
+
+have, Wales, been
+
+engineering, am, fan
+
+Europe, like, visit
+
+Kingdom, not, United, am
+
+# Dialogue
+
+Human: Where is it located?
+
+Machine: You've never been there before, but this can be found in Wales
+
+between the Queen Alexandra Dock and Penarth Head.
+
+Human: Was this a large project?
+
+# Generated Persona
+
+I love the downtown area.
+
+I have never been to Wales.
+
+I am a fan of engineering.
+
+I would like to visit Europe.
+
+I am from the United Kingdom.
+
+# Predicted Responses
+
+<table><tr><td>BART‡</td><td>Oh yes. You’d be interested in this barrage, which was around 60 million deep and was the largest civil engineering project in the world.</td></tr><tr><td>RAG‡</td><td>Oh yes. You’ll be interested in this barrage, with its 4.75 mile long effect. It was the largest civil engineering project in Europe.</td></tr><tr><td>Ours (BART)</td><td>Oh yes, it was very large. You’d be a fan of this engineering work, which was considered one of the largest civil engineering projects ever undertaken in the country.</td></tr><tr><td>Ours (RAG)</td><td>It was very large in scale, and you’d be a fan of this engineering, as it was one of the largest civil engineering projects in the country.</td></tr></table>
+
+# Ground Truth Knowledge
+
+It was one of the largest civil engineering projects in Europe during construction in the 1990s.
+
+# Ground Truth Persona
+
+I love the bay area.
+
+I have never been to Wales.
+
+I am a fan of engineering.
+
+I would like to visit Europe.
+
+I am not from the United Kingdom.
+
+# Ground Truth Response
+
+Oh yes, very large. With you being a fan of engineering, you'd be
+
+interested to hear that this was one of the largest civil engineering projects
+
+in Europe during the time.
+
+Table 7: Qualitative result.  $\ddagger$  denotes the vanilla models, and red and blue each indicate the parts utilized by Ours in the persona and the knowledge. All the predicted results are from our model.
+
+Table 7 demonstrates the prediction results from the baselines and our models on the FoCus dataset. It is noteworthy that the vanilla BART and RAG
+
+models tend to generate shallow responses that revolve around the topic of Cardiff Bay Barrage. Furthermore, the models tend to provide numerical information that lacks factual support regarding the knowledge concept. To sum up, these models fail to achieve a deep understanding of human preferences based on the provided persona concepts, leading to less engaging responses that lack any personal-related expressions.
+
+Furthermore, our proposed models generate informative and empathetic responses, striking a balance between incorporating external information relevant to the knowledge concept and avoiding any distortion. For instance, our models generate expressions such as "it was one of the largest civil engineering projects in the country," which provide sufficient information. These results suggest that our method is well-suited for scenarios where the sentence-formed knowledge and persona candidates are absent.
+
+# 6 Conclusions
+
+In this paper, we introduced an adaptive dialogue agent utilizing persona and knowledge without the given candidates from the dataset. Due to the absence of knowledge candidates, the knowledge retriever retrieves the relevant paragraphs with the knowledge concept from the knowledge base. Also, the concept-based persona generator outputs the persona descriptions with the fragmentary persona concepts from retrieve-and-generate architecture. The generated persona descriptions are then validated through a persona aligner regarding relevancy and consistency. From experiments, we showed that our method is effective even though the persona concept and knowledge concept are given with the dialogue. We also presented the ablation studies on each component of our model. Moreover, we conducted the human evaluation to show the improved quality of the responses of our models and it is also shown in qualitative results. To show its applicability and adaptiveness, we denoted the experimental results of our method on FoCUS, WoW, and PersonaChat datasets.
+
+# 7 Acknowledgement
+
+This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2023-2018-0-01405) supervised by the IITP(Institute for
+
+Information & Communications Technology Planning & Evaluation). This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques). This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2022R1A2C1007616)
+
+# Limitations
+
+Our model deal with response generation in the candidate-agnostic conversation setting, which is the limitation of INFO (Lim et al., 2022) model, proving the possibility of application in the real world. Still, hallucinations regarding personas and knowledge are observed occasionally in the generated responses. However, since the case of hallucinations is a severe problem even in large language models with enormous parameter sizes, it is required for our NLP communities to continue to solve the challenge. Also, although we conducted a human evaluation to validate the diverse capabilities of the proposed model, such as hallucination, consistency, and informativeness in dialogue generation, the number of cases is relatively small for evaluating the entire aspects of the capabilities. Finally, our model demands high GPU computation resources as it marginalizes loss at the token level.
+
+We plan to improve our model for future work by conducting human evaluations with more cases and enhancing the way of qualitative analysis for the model's hallucinated answers. Improving the model's generator with more computationally efficient components is also a desirable direction for the GPU resource issues.
+
+# Ethics Statement
+
+We discuss the main ethical considerations of the model we proposed: (1) Privacy. the datasets adopted to construct our model provide factual knowledge and fictional person's preferences, and our model does not contain privacy issues. (2) Human evaluation. During the human evaluation process, we paid human workers the legal wage determined by the average time of evaluation and local labor compensation standards. We also guided them to take a rest when they are in a state
+
+of fatigue during work. (3) Potential problems. Although we take conscientious steps to ensure the quality of our models, there can still be potential problems with the generated responses' quality, which can lead to incorrect predictions in applications that leverage factual information and human preferences. (4) Model deployment. Our approach employs the pre-trained language models (PLMs) for the downstream tasks, which have the risk of reflecting the bias of the training data. It is a well-known threat in tasks using PLMs, and we should be careful about social impact when using this method since our model aims to handle factual knowledge.
+
+# References
+
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+
+# A Training details for Concept-based Persona Generator
+
+# A.1 Persona Retriever
+
+For training persona retriever, we first construct persona pool. The statistics for persona pool is shown in Table 8. To train the persona retriever, we created a training and validation dataset using the persona pool. To construct a DPR-style dataset, we set the query as the persona-concept, the positive sample as the complete persona sentence, and the negative samples as lexically similar persona sentences retrieved from BM25 (Robertson and Zaragoza, 2009). We use  $\mathrm{haystack}^3$  and Hugging Face (Wolf et al., 2020) to implement persona retriever. The hyperparameters utilized for training persona retriever are batch size 32, learning rate  $1 \times 10^{-6}$ , AdamW optimizer (Loshchilov and Hutter, 2019) ( $\beta_{1} = 0.9$ ,  $\beta_{2} = 0.999$ , and  $\epsilon = 1e - 8$ ). We initialized the query and context encoder with bert-base-uncased model and the training is carried out 5 epochs.
+
+# A.2 Concept-based Persona Generator
+
+For training the concept-based persona generator, we set top  $k$  as 10 to augment ten relevant persona sentences to the input for training BART (Lewis et al., 2020a). The hyperparameters utilized for the training the BART are batch size 64, learning rate  $5 \times 10^{-5}$ , and the AdamW optimizer (Loshchilov and Hutter, 2019)  $(\beta_{1} = 0.9, \beta_{2} = 0.999$ , and  $\epsilon = 1e - 8$ ). We initialized BART with facebook/bart-base from the Hugging Face and the training is carried out 5 epochs.
+
+<table><tr><td>Model</td><td># Sentences</td></tr><tr><td>FoCus (Jang et al., 2022b)</td><td>42,105</td></tr><tr><td>PersonaChat (Zhang et al., 2018)</td><td>4,795</td></tr><tr><td>Total</td><td>46,900</td></tr></table>
+
+Table 8: Statistics for the persona pool used in persona retriever
+
+# B Experimental Details
+
+For the dialogue generation, we implement our method with Hugging Face (Wolf et al., 2020). We set the learning rate  $6.25 \times 10^{-5}$  with the AdamW optimizer (Loshchilov and Hutter, 2019). We employ a BARTlarge model with 12 encoder and decoder layers and RAG-Token that have the same number of layers for a fair comparison. The batch size is set as 128 for BART and 16 for RAG.  $\lambda_{P}$  and  $\lambda_{LM}$  are 0.5 and founded by manual search. The number of dialogue history window sizes is 2 and the beam size is 4. The whole model was trained for three epochs on RTX A6000 GPU and took 8 hours per epoch.
+
+# C Prompt for Building Aligner
+
+# Prompt
+
+"Check for aligning" is whether the "Persona" is explicitly or implicitly reflected in "Dialogue". Classify if a given "Dialogue" aligns with "Persona". You must answer with Yes or No.
+
+New Sample
+
+Persona: I would like to visit the Nazareth House again.
+
+Dialogue:
+
+A: I think I've been there before but I don't remember the name of this place.
+
+B: This place is the Nazareth House, which you would like to visit again.
+
+A: Can you describe this house to me?
+
+B: You have curiosity about the description of Nazareth House and I will tell you. Nazareth House is prominently located on an elevation along Wynnum North Road. The complex consists of a number of buildings including the original building, the Convent and Chapel and two more recent additions, St Joseph's Hostel and the nursing home known as Larmeniere.
+
+A: Does this house look old to me, when it was built?
+
+B: This house is relatively old, but since you would like to know when it was built, I will explain it to you. Nazareth House was built from 1924 to 1939.
+
+A: What is the history of this house?
+
+B: The history of the house you are interested in began in 1925 when it was opened by Archbishop James Duhig as part of the charity established on the site by the Poor Sisters of Nazareth. The Nazareth House, located in Tingal Hill, Wynnum, was designed by the Brisbane architecture firm, Hennessy, Hennessy, Keesing Co and JP Donoghue and built by George Turner.
+
+Check for aligning:
+
+# expected answer
+
+Yes or No
+
+Table 9: Prompt used for alignment on consistency by referring to persona and dialogue. Our persona consistency module is trained by gathering the responses.

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+# Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
+
+Warning: content in this paper may be upsetting or offensive.
+
+Jimin Mun $^{\text{♥}}$  Laura Vianna
+
+Carnegie Mellon University
+
+Emily Allaway\* Sarah-Jane Leslie
+
+$\spadesuit$  Columbia University
+
+Akhila Yerukola Maarten Sap
+
+†University of Washington
+
+$\diamond$  Princeton University
+
+*Allen Institute for AI
+
+# Abstract
+
+Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype's origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine-generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.
+
+# 1 Introduction
+
+Counterspeech, i.e., responses that counteract hateful or dangerous content (Benesch et al., 2016), has emerged as a widely supported solution to address hateful online speech without censorship risks of deletion-based content moderation (Myers West, 2018; Sap et al., 2019). However, as the scale of hateful content increases online (Leetaru, 2019; Baggs, 2021), effectively responding with counterspeech is infeasible for humans to do alone. As
+
+![](images/b738ceb25b58c58704fdaa23d242bb3bd4b2686fac91f0345d79948f3ebc99d4.jpg)  
+Figure 1: Responses to same implication where humans are able use appropriate countering strategies to target the implication by presenting alternate groups, counterexamples, and external factors while LLMs focus on denouncing.
+
+such, NLP systems can provide a promising opportunity to further understand counterspeech and assist content moderators and other stake holders in generating responses (Parker and Ruths, 2023).
+
+One major challenge towards effectively combatting hateful and prejudiced content is that counter-speech should dispel the implied stereotypical beliefs about social groups (Buerger, 2021b), beyond simply denouncing the speech. For instance, in Figure 1, the human-written counterspeech targets the stereotype that "gay men are promiscuous" implied in the original post. If left unchallenged, such prejudiced implications can perpetuate and maintain existing stereotypes (Beukeboom and Burgers, 2019) and exacerbate discrimination and intergroup conflict (Fiske, 1998; Macrae and Bodenhausen, 2000). However, exactly how to convincingly combat stereotypical implications with counterspeech
+
+remains an open question (Buerger, 2022).
+
+To investigate this question, we analyze the usage and convincingness of psychologically inspired strategies for countering implied biases and stereotypes, in both human- and machine-generated counterspeech. In contrast to prior work, which has examined tone- or style-based effectiveness (Hangartner et al., 2021; Han et al., 2018), we design and analyze six stereotype-targeting strategies drawing from literature in social psychology, social cognition, and philosophy of language.
+
+Our strategies aim to directly combat the stereotypical association between a group and an attribute (e.g., "gay men", "promiscuous"): reducing the association between the attribute and the group by suggesting alternate groups that exhibit similar attributes, or focusing on different attributes of the targeted group, providing counterexamples to the stereotype, reducing the intrinsic implication of the stereotype through external factors about stereotype origins, broadening the group boundaries to emphasize individual variation, and generally labeling the stereotype as negative.
+
+We first investigate how prevalent and convincing these strategies are in human-generated counterspeech. Specifically, we conduct a crowdsourced user study to examine which strategies workers prefer, and analyze two corpora of human-written counterspeech. Our results show that humans perceive countering strategies that present different qualities of the targeted group as most persuasive, and generally prefer strategies that require deeper reasoning about the implied stereotype (e.g., presenting external factors or counterexamples).
+
+Furthermore, when comparing human-written and machine-generated counterspeech, we find that machines often generate nonspecific strategies (e.g., general denouncing) whereas humans use more nuanced and specific strategies, like counterexamples. Our findings highlight the potential for understanding counterspeech strategies that persuasively challenge stereotypical beliefs and reveal the inherent limitations of current NLP models in generating effective stereotype-targeting counterspeech.
+
+# 2 Background: Language, Stereotypes, and Counterspeech
+
+Our goal is to investigate stereotype-targeting counterspeech strategies that most convincingly challenge the underlying implications of hate speech. Our analyses and strategies are based on the fact
+
+that prejudiced language about social groups can often convey and exacerbate stereotypes through subtle implicature (Fiske, 1998; Sap et al., 2020; Perez Gomez, 2021, §2.1). Properly combating such language requires convincingly dispelling and fact-checking the stereotypical implications beyond simple deflection or norm-setting (Lepoutre, 2019, §2.2). Additionally, our investigations aim to shed light on how NLP systems can help humans better analyze and produce counterspeech (§2.3).
+
+# 2.1 Language and Stereotypes
+
+Prejudiced language reflects and perpetuates social inequalities and stereotypical beliefs (Beukeboom and Burgers, 2019). As argued by Perez Gomez (2021), when accepting subtly prejudiced statements as true, we inherently accept the underlying stereotype as true. Therefore, the harmful implications of hate speech must be challenged to mitigate the spread of such beliefs.
+
+These underlying stereotypes are often in the form of generics, i.e., generalizing statements such as "girls wear pink", (Rhodes et al., 2012) which are difficult to counter due to their unquantified structure (Leslie, 2014). Generics show especially strong association to the group when the characteristics are striking and negative (Leslie, 2017), and once formed, these beliefs become extremely difficult to change (Scheffer et al., 2022). Thus, countering hate speech is important yet challenging because it requires extensive reasoning about the underlying implications.
+
+# 2.2 Countering Hate vs. Implied Stereotypes
+
+Counterspeech can have many intended effects to a diverse audience including original speakers and bystanders (Buerger, 2021a, 2022). Therefore, counterspeech, and more broadly countering hate, has been studied from a number of different angles. Observing Twitter interactions, Hangartner et al. (2021) found that empathy resulted in better outcomes (e.g., deletion, decrease in number of hate tweets) compared to using humor or warnings of consequences. Benesch et al. (2016) suggests various strategies for countering hate such as empathy, shaming, humor, warning of consequences, and fact-checking. Moreover, studies show the contagion effect (Buerger, 2021a) where social feedback (Berry and Taylor, 2017) and the quality of the comments (e.g., civility) impact subsequent comments and discussions (Friess et al., 2021; Han et al., 2018). These works provide insights into
+
+preferences for strategies to counter the explicit content of hate speech. In contrast, our work studies strategies for persuasive arguments against hate conveyed through implied stereotypes.
+
+To specifically target stereotypes and group perception, previous works in psychology have studied humanization of targeted groups through reminding people that individuals belong in multiple categories (Prati et al., 2016), exposure to positive outgroup examples (Cernat, 2011), and anti stereotypes (Finnegan et al., 2015) and have shown promising results. Moreover, the positive impacts on belief change of fact-checking (Porter and Wood, 2021) and counter narratives (Lewandowsky et al., 2012) call for counterspeech beyond just rejecting the original speech.
+
+# 2.3 NLP for Counterspeech
+
+With the ability to analyze and generate text at a large scale, NLP is an invaluable asset for counterspeech and number of works in NLP have collected and generated counterspeech datasets (Mathew et al., 2019; Garland et al., 2020; Qian et al., 2019; Chung et al., 2019; Bonaldi et al., 2022; Fanton et al., 2021) for the detection, analysis, and generation of counterspeech. Yu et al. (2022) observed that question marks and words related to awareness and problem solving are frequently used in counterspeech. Similarly, Lasser et al. (2023) noted that responding with simple opinions or sarcasm reduced extreme hate. Generative approaches to counterspeech have incorporate stylistic strategies (e.g., politeness, joyfulness, detoxification; Saha et al. 2022) and relevance (Zhu and Bhat, 2021). Although previous works have made progress towards observing and generating using different counterspeech strategies, the focus has been on stylistic differences and while promising, do not explicitly target the underlying stereotypes in hate speech.
+
+Fraser et al. (2021) and Allaway et al. (2023) have more closely investigated computational methods to counter stereotypes. Fraser et al. (2021) investigated stereotypes and counter stereotypes using warmth and competence of the groups and associated words and noted the difficulty in automatically generating anti-stereotypes. Moreover, Allaway et al. (2023) have studied automatic generation of counterstatements to implied stereotypes and provides a proof-of-concept for countering strategies. Our work, on the other hand, adds upon the proposed strategies and analyzes their usage by
+
+Post: Do girls think guys flex muscles n stroke their c\*\*ks before taking pics Bc that's basically what pathetic girls do in every pic Implied Stereotype: Women are shallow.
+
+ALT-GRP This statement implies an inaccurate stereotype because many men can also be shallow.
+
+ALT-QUAL This statement implies an inaccurate stereotype because many women are intelligent, profound, and multi-dimensional.
+
+CNTR-EXS This statement implies an inaccurate stereotype because a lot of women are profound, including women philosophers, women scientists, and women writers.
+
+EXTERNAL This statement implies an inaccurate stereotype; historical contexts such as gender inequality and sexism have perpetuated this false stereotype that women are shallow.
+
+BROAD This statement implies an inaccurate stereotype because women have all sorts of personality types and characteristics, just like all groups of people; we cannot characterize them all as shallow.
+
+GEN-DEN This statement's implication is an inaccurate and unnecessarily hurtful generalization about women that they are shallow.
+
+Table 1: Example statements of each countering strategy. These statements were also used as counter statements for user study in §4.
+
+humans and machines to understand these strategies and improve counterspeech. Building on these prior works, we aim to provide insights into how language models can help counter stereotypes and mitigate the harmful effects of hate speech.
+
+# 3 Stereotype-Targeting Strategies
+
+To investigate how counterspeech can address stereotypical speech, we examine stereotypes through the lens of generics about social groups (Rhodes et al., 2012; Sap et al., 2020), i.e., short unquantified statements that link a social group with a quality:
+
+$$
+\mathrm {G R O U P} + \text {r e l a t i o n} + \mathrm {Q U A L I T Y}.
+$$
+
+For example, the stereotype in Table 1 "Women are shallow" can be separated into three components: "Women" (GROUP), "are" (relation) and "shallow" (QUALITY). The GROUP typically refers to demographic groups such as black people, gay men, etc. The relation is typically a stative verb like "are" or a verb indicating a lack of ability, such as "can't". A QUALITY usually has negative meaning or connotation (e.g., shallow, terrorist), but can sometimes be positive (e.g., for positive stereotypes; Cheryan and Bodenhausen, 2000).
+
+Through this formulation, we introduce six countering strategies that aim to challenge the acceptability, validity, or truthfulness of stereotypes ex
+
+pressed as generics, crafted based on prior work on combating stereotypes and essentialism in social psychology and finding exceptions to generics in philosophy of language.
+
+Alternate Groups (ALT-GRP) People often accept generics of the form 'G relation  $q$ ' as true based on relatively weak evidence (Cimpian et al., 2010). However, for unfamiliar groups, we often attribute qualities more assertively, claiming that (almost) 'all G relation  $q$ ' even with scarce supporting evidence (Rooij and Schulz, 2020). To counter such inferences, we can remind readers that alternate groups' relation to  $q$  is often higher than commonly recognized.
+
+We define the ALT-GRP strategy as follows:
+
+$$
+A l t (\text {G R O U P}) + r e l a t i o n + \text {Q U A L I T Y}
+$$
+
+Here,  $Alt(G)$  represents a relevant set of alternative groups to  $G$  with the same set of separating characteristic (e.g., gender). For instance, if  $G = \text{"women" }$ ,  $Alt(G)$  would consist of a group like "men" rather than a set of unrelated groups.
+
+Alternate Qualities (ALT-QUAL) Similarly, we construct a strategy that highlights alternate distinctive or defining qualities of  $G$ . We consider values of  $Alt(q)$  to promote a more nuanced understanding and challenge preconceived notions about the group. Thus, we formulate the ALT-QUAL strategy as the following:
+
+$$
+\text {G R O U P} + \text {r e l a t i o n} + \text {A l t (Q U A L I T Y)}
+$$
+
+In this case,  $Alt(q)$  represents alternate contextually relevant qualities. For example, if  $S =$  Women are shallow.,  $Alt(q)$  would include qualities like "intelligent" and "profound" but not positive and unrelated quality (e.g., fun). This aims to combat the definitional aspect of a stereotype.
+
+Counterexamples (CNTR-Exs) Counterexamples aim to point out exceptions to the stereotype and thereby challenge the implication that the stereotype holds for all members of the GROUP.
+
+$$
+x + \left\{ \begin{array}{l} r e l a t i o n + A l t (\text {Q U A L I T Y}) \\ \neg r e l a t i o n + \text {Q U A L I T Y} \end{array} \right.
+$$
+
+where  $x$  is a specific individual member of GROUP or a subgroup of GROUP<sup>2</sup>.
+
+External Factors (EXTERNAL) When readers are reminded that stereotypical characteristics of a group  $G$  exist due to external conditions (i.e. 'G relation  $q$  is due to external factor  $f$  ), they have been shown to exhibit more flexible and dispensable thinking towards the group  $G$  (Vasilyeva and Lombrozo, 2020). This style of unconfounding is referred to as structural construal (Berkowitz, 1984) in social psychology.
+
+Thus, we propose the EXTERNAL strategy, in which counterspeech employs external causes or factors to demonstrate why a stereotype might have been formed (e.g., historical context such as gender inequality). Note that it differs from CNTR-EXS by focusing on external factors or details for refutation, rather than specific instances within the group.
+
+Broadening (BROAD) The broadening strategy questions the validity of a GROUP in a generic or stereotype by employing humanizing techniques. Essentially, broadening emphasizes either the uniqueness of a certain quality to the group or highlights the complexity of individual group members, suggesting that they cannot be defined by merely one quality.
+
+An example of broadening can be formalized as:
+
+$$
+\text {A l l p e o p l e} + \text {r e l a t i o n} + \left\{ \begin{array}{l} A l t (\text {Q U A L I T Y}) \\ \text {Q U A L I T Y} \end{array} \right.
+$$
+
+General Denouncing (GEN-DEN) Another commonly observed countering strategy in NLP is denouncing (Mathew et al., 2019; Qian et al., 2019). Denouncing involves expressing general disapproval for the hate speech (e.g., "This is just wrong"). This approach has the potential to establish discursive norms and preemptively dismiss similar instances of hate speech (Lepoutre, 2019). Here, counterspeech assigns general negative values to the stereotypical statement, using terms like "hurtful", "unhelpful" or "not okay" to express disapproval and challenge the stereotype.
+
+# 4 Human Evaluation of Stereotype-Targeting Strategies
+
+In order to determine which strategies are persuasive in countering implied stereotypical beliefs, we first conduct a user study on Amazon Mechanical Turk (MTurk) to measure the convincingness of expert-crafted counter-statements, i.e., short sentences that embody a specific countering strategy.
+
+![](images/33c690a6e7f946ad8bfad932ca03d90c3a19ba3f7bd5a91a2fb4da3f00d7f885.jpg)  
+Figure 2: First and second choices of countering strategies sorted by the sum of the two responses. Percentage is calculated separately for first and second choice.
+
+# 4.1 Experimental Setup
+
+We first collected social media posts with their corresponding implied stereotype for 10 demographic groups from the Social Bias Inference Corpus (SBIC; Sap et al., 2020). The groups were selected from the top 25 most targeted groups in  $\mathrm{SBIC}^3$ , and the stereotypes were selected from the top 10 most frequent stereotypes for each group. The selected groups and stereotypes are shown in Figure 7.
+
+Counter-statements For each stereotype, six counter-statements (one for each strategy) were crafted collaboratively by the authors, to ensure maximal faithfulness to the strategy definitions. This resulted in a total of 60 counter-statements; see Table 1 for examples.
+
+Task Description Each participant is presented with a post and its implied stereotype, along with six counter-statements in random order. Annotators are asked to select the first and second most convincing counter-statements according to them (i.e., following a descriptive data labeling paradigm; Röttger et al., 2022). We recruited annotators from a pool of pre-qualified participants who were paid 0.27 USD for each text and stereotype pair. Please refer to Appendix G for further details.
+
+# 4.2 Results
+
+Our results in Figure 2 suggest that there is a clear distinction and ordering of convincing countering strategies. Although the order of preference for the first choice and the second choices are different, the cumulative top choices remain consistent:
+
+ALT-QUAL, BROAD, CNTR-EXS, and EXTERNAL. These results are mostly consistent with findings from social psychology that highlight the value of strategies similar to ALT-QUAL (Leslie, 2008, 2017), BROAD (Foster-Hanson et al., 2022), and EXTERNAL (Vasilyeva and Lombrozo, 2020).
+
+We also observe that less complex strategies are not necessarily the most convincing. In particular, while GEN-DEN and BROAD require the least amount of reasoning about the stereotypical quality or group to understand, they have very different levels of convincingness: BROAD is the most preferred first choice while GEN-DEN is the least preferred. Furthermore, strategies that require a similar level of involved reasoning can have drastically different levels of convincingness (e.g., ALT-QUAL is chosen as convincing twice as frequently as ALT-GRP, cumulatively). This suggests that differences in strategy complexity are not sufficient to account for convincingness.
+
+There are also clear differences in strategy preferences depending on the group and stereotype (see Figure 7). For example, BROAD is not chosen at all for the stereotype about feminists (Fig. 7, fourth from the bottom), despite its popularity in countering other stereotypes such as "women are shallow".
+
+# 5 Countering Strategies in Human vs. Machine-generated Data
+
+The results from the previous section suggest that some strategies are more convincing than others. To further investigate this, we examine the usage of these six strategies in human-written counterspeech. Additionally, we compare human and machine-generated counterspeech to understand whether machines use these strategies. In this section, we discuss the methods and datasets used for this analysis, while our findings are presented in §6.
+
+# 5.1 Datasets
+
+We consider two datasets that contain posts paired with counterspeech comments: Multitag-CONAN (Fanton et al., 2021), a dataset of hate speech and counterspeech pairs generated using human-machine collaboration, and the Winning Arguments (ChangeMyView) Corpus (Tan et al., 2016) extended with more recent posts from the same subreddit, r/ChangeMyView (hence forth CMV) for the period 2016-2022.4 Appendix A
+
+shows details on datasets and their collection and filtering process.
+
+These two datasets offer insights into how countering strategies are employed in two distinct types of online interactions: in CONAN, users share short comment replies without necessarily seeking change, while in CMV, users actively engage via long comment posts to challenge or potentially alter their opinions.
+
+# 5.2 Selecting Data
+
+For both MTCONAN and CMV datasets, we are interested in analyzing the implied stereotype of the original post as well as the strategies used in the counter-statements (counterspeech in MTCONAN;  $\Delta$  and non- $\Delta$  comments in CMV). We use the process outlined below to filter and select data that contain specific implied stereotypes.
+
+Implied Stereotype Extraction To analyze strategies for combating stereotypical implications, we filter our two datasets to include only posts that target or reference a specific stereotype about a demographic group. Based on evidence of its labeling abilities (Ziems et al., 2023), we use GPT-4 to identify such posts and generate the target group and implied stereotype, by prompting it with three in-context examples (see Appendix B for details). Table 3 shows the number of unique harmful posts in each dataset.
+
+# 5.3 Counterspeech Generation
+
+To investigate machine-generated counterspeech, we prompt two large language models (LLMs) to respond to posts with counterspeech: GPT-4 (OpenAI, 2023) and Alpaca (Taori et al., 2023). We instruct the LLMs to assist a user in responding to harmful posts without providing any specific counter strategy definitions, to avoid biasing the model's output towards predetermined approaches.
+
+# 5.4 Counter Strategy Labeling
+
+To detect the usage of any our six stereotype-targeting strategies within a reply to a post, we leverage the labeling abilities of two LLMs, namely GPT-3.5 $^{6}$  and GPT-4. We prompt these models
+
+with the strategy definition and two in-context examples (one using the strategy and one without it), and instruct them to provide a binary label that indicates the presence of the strategy. We also prompt them to output the text span that contains it. Please refer to Appendix D.2 for detailed prompts used for analysis.
+
+Test Set Creation To validate the reliability of using LLMs for detecting these nuanced counter- ing strategies, we randomly sampled 100 examples from each dataset. These samples were assigned gold labels through expert annotation conducted by three of the authors. The authors manually reviewed the examples independently, resolving disagreements through discussions. This process led to the creation of a test set comprising of 200 expert-annotated samples.
+
+Evaluating Strategy Detection We utilize these gold labels to assess the performance of each LLM in detecting strategies, as shown in Table 2. Although performance is only moderate, both models are outperform the majority class classifiers, which include both the 0 and 1 majority classes. This finding is particularly notable given that the majority class of the entire dataset is unknown. It suggests that these models have recognized some meaningful signals within the data. GPT-4 shows similar or improved performance on strategy detection compared to GPT-3.5 for all strategies and is thus used in subsequent experiments for strategy labeling. However, akin to expert-annotators, determining whether a reply contains a specific strategy is not trivial for LLMs. For example, we observed during a qualitative inspection that models occasionally confuse simple mention of alternative qualities or groups for the presence of ALT-QUAL or ALT-GRP, respectively. See Appendix F for further analysis.
+
+# 6 Usage and Convincingness Results
+
+In this section, we discuss the results of our investigations regarding the usage and persuasive power of our six stereotype-targeting strategies, for both human and machine-written counterspeech.
+
+# 6.1 CMV: Persuasive vs. Non-persuasive Comments
+
+The comparison of strategy usage between  $\Delta$  and non- $\Delta$  comments in CMV dataset is shown in Figure 3. Overall, the most frequently used strategies were ALT-QUAL (84.8%) followed by EXTERNAL
+
+<table><tr><td rowspan="2">Strategy\Method</td><td colspan="3">GPT-4</td><td colspan="3">GPT-3.5</td><td colspan="3">Majority Class (0)</td><td colspan="3">Majority Class (1)</td></tr><tr><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>ALT-GRP</td><td>0.64</td><td>0.75</td><td>0.60</td><td>0.62</td><td>0.71</td><td>0.59</td><td>0.42</td><td>0.5</td><td>0.45</td><td>0.09</td><td>0.5</td><td>0.15</td></tr><tr><td>ALT-QUAL</td><td>0.68</td><td>0.66</td><td>0.61</td><td>0.66</td><td>0.64</td><td>0.58</td><td>0.31</td><td>0.5</td><td>0.38</td><td>0.20</td><td>0.5</td><td>0.28</td></tr><tr><td>CCTR-EXS</td><td>0.74</td><td>0.80</td><td>0.73</td><td>0.73</td><td>0.78</td><td>0.72</td><td>0.36</td><td>0.5</td><td>0.42</td><td>0.14</td><td>0.5</td><td>0.22</td></tr><tr><td>EXTERNAL</td><td>0.69</td><td>0.72</td><td>0.68</td><td>0.70</td><td>0.72</td><td>0.68</td><td>0.34</td><td>0.5</td><td>0.40</td><td>0.16</td><td>0.5</td><td>0.25</td></tr><tr><td>BROAD</td><td>0.60</td><td>0.66</td><td>0.52</td><td>0.61</td><td>0.67</td><td>0.52</td><td>0.41</td><td>0.5</td><td>0.45</td><td>0.10</td><td>0.5</td><td>0.16</td></tr><tr><td>GEN-DEN</td><td>0.55</td><td>0.57</td><td>0.46</td><td>0.55</td><td>0.57</td><td>0.47</td><td>0.40</td><td>0.5</td><td>0.44</td><td>0.11</td><td>0.5</td><td>0.17</td></tr></table>
+
+Table 2: GPT-4 and GPT-3.5 strategy labeling performance over two datasets using expert-annotations shown with majority class classifiers. While performance is moderate, both models significantly outperform majority class classifiers, and GPT-4 outperforms or shows similar performance to GPT-3.5 for all strategies.  
+
+<table><tr><td>Dataset</td><td>Harmful Posts</td><td>Counterspeech</td></tr><tr><td>CMV</td><td>1586</td><td>5191</td></tr><tr><td>MTCONAN</td><td>876</td><td>1000</td></tr></table>
+
+Table 3: Number of unique harmful posts and counter-speech for each dataset.
+
+![](images/2cbf514e8c294484e51f497bd597396028e9d1b4f1364cd3d1217b4c695f0677.jpg)  
+Figure 3: Delta and non-delta comments comparisons of strategy usage annotated with independent  $t$ -test  $P$  values. The legends used in this graph are ns ( $p < .00$ ), * ( $p < .05$ ), ** ( $p < .01$ ), and *** ( $p < .001$ ). Error bars indicate standard error.
+
+(66.7%), BROAD (66.5%), ALT-GRP (61.4%), CNTR-EXS (42.3%), and GEN-DEN (34.3%). On average, each comment used 3.56 strategies.
+
+Using an independent  $t$ -test, we find that the most significant difference in strategy usage is for the BROAD strategy, with  $68.8\%$  and  $64.1\%$  for  $\Delta$  and non- $\Delta$  comments respectively ( $p < 0.001$ ). We also see that GEN-DEN strategy usage is less persuasive (32.6% in  $\Delta$  comments vs  $36.1\%$  in non- $\Delta$  comments;  $p < 0.01$ ). Additionally, CNTR-EXS and BROAD were found to be more persuasive.
+
+Strategies by Demographic Identity Types The analysis of results categorized by target group demographic types is shown in Figure 5. The keywords corresponding to each demographic group can be found at Appendix H. Among the persuasive
+
+![](images/53467b72c3feff832d9cd14ee31718f319f98a5ed429f974cb0eed1f1b8f6bf5.jpg)  
+Figure 4: Delta comments and machine-generated counterspeech comparisons of strategy usage annotated with independent  $t$ -test  $P$  values. The legends indicate ns ( $p < 1.00$ ), ** ( $p < .01$ ), and **** ( $p < .0001$ ). Error bars indicate standard error.
+
+$\Delta$  comments, the highest proportion of targeted demographic types were related to gender identity, accounting for  $42.5\%$  of the total. This was followed by race/ethnicity  $(10.9\%)$ , sexual orientation  $(8.0\%)$ , appearance  $(7.7\%)$ , socio-economic status  $(4.8\%)$ , and religion  $(4.8\%)$ . We notice that the most distinguishing strategies for addressing various demographic types were EXTERNAL and GEN-DEN. Further, GPT-4 showed a higher usage of GEN-DEN when countering stereotypes concerning race/ethnicity, appearance, and religion.
+
+# 6.2 CMV:Persuasive Comments vs.
+
+# Machine-generated Counterstatements
+
+The comparison between persuasive human responses ( $\Delta$  comments) and machine-generated responses in the CMV dataset is shown in Figure 4. Both GPT-4 and Alpaca generated counterspeech showed significant differences in strategy usage compared to persuasive human responses. Alpaca-generated counterspeech contained an average of
+
+![](images/ee6692bf5e132d34a114035d1e62c520bf21324460ac9d2f0446aa90055f2598.jpg)  
+Figure 5: Delta comments and machine-generated counterspeech strategy usage categorized by target group demographic identity types. GPT-4 shows much higher denouncing for certain demographic groups than others.
+
+![](images/9b56a82ac4928a16ac91f9374ba1942c113cde7d35352447bb8fdd709a672657.jpg)  
+Figure 6: Delta comments and CONAN counterspeech comparisons of strategy usage annotated with independent  $t$ -test  $P$  values. The legends indicate ns ( $p < .00$ ), ** ( $p < .01$ ), and **** ( $p < .0001$ ).
+
+2.30 strategies per counterstatement, while GPT-4-generated ones used the most out of the three variations, averaging 4.05 per counterstatement.
+
+Most notably, GPT-4-generated counterspeech contained GEN-DEN more frequently (55.6%) compared to persuasive human (32.6%) and Alpaca-generated (32.3%) responses. Further, both GPT-4 and Alpaca generated counterspeech used CNTR-EXS significantly lesser than human responses (43.6% vs. 31.5% and 11.4% for GPT-4 and Alpaca respectively). Also, GPT-4-generated counterspeech used lower ALT-GRP (56.6%) than human responses (61.4%). The comparison between non-persuasive human responses (non- $\Delta$  comments) and machine-generated responses shown in Appendix C show a similar pattern where GPT-4 uses more GEN-DEN and machines use less CNTR-EXS.
+
+# 6.3 MTCONAN: Counter Narratives vs Machine-generated Counterstatements
+
+The usage of countering strategies in the MTCO-NAN dataset, compared to machine responses, is presented in Figure 6. In contrast to CMV  $\Delta$  comments, the observed trends of counter narra
+
+tives in MTCONAN indicate a higher use of general denoucing and lower usage of all other strategies. The most frequent strategies are ALT-QUAL (54.0%) and GEN-DEN (48.3%), while the least frequent ones are EXTERNAL (16.9%), and CNTR-EXS (12.6%). This analysis reveals that uncivil stereotypical speech is countered differently from civil engagements.
+
+In contrast, machine-generated responses to MTCONAN posts show high usage of GEN-DEN and ALT-QUAL at  $96.9\%$  and  $94.4\%$  respectively, significantly differing from MTCONAN counter narratives using  $48.3\%$  and  $54\%$  respectively. GPT-4-generated responses demonstrate higher usage of all the strategies averaging 4.24 strategies per comment, followed by alpaca (2.91) and MTCONAN counter narratives (1.87).
+
+# 7 Conclusion & Discussion
+
+In this work, we investigated stereotype-targeting strategies that convincingly counter the underlying implication of harmful speech. We crafted six different strategies (§3) and observed human preferences under a controlled setting (§4) and in two datasets (§5). We compared the strategy usage in persuasive vs. unpersuasive human generated counterspeech and in human vs. machine-generated counterspeech (§6).
+
+Humans prefer more specific strategies. In both our user study (\$4) and data study (\$5), ALTQUAL was the most preferred and used method. Similarly, the delta award difference and the preference ranking both showed EXTERNAL, BROAD, and CNTR-EXS as more convincing. These top strategies, with the exception of broadening, are methods that require specific reasoning about the implication. Moreover, correctly identifying external factors and devise counter examples requires up-to-date world knowledge. In this work, we did not focus on the correctness of the usage of these strategies by machines, but - given their persuasiveness to humans - further study is required to
+
+ensure their quality.
+
+LLMs use less anti-stereotypical examples. Across the two models, GPT-4 and Alpaca, we see that LLMs do not use anti-stereotypical examples as often as humans, and instead use more general strategies such as denouncing. However, we see from our results and previous work (Finnegan et al., 2015) that humans prefer countering strategies with specific examples. These findings suggest that LLMs may lack exposure to anti-stereotypical examples and reveals an interesting challenge for improving machine-generated counterspeech.
+
+Stereotypes about each target group are handled differently. From both our human evaluations and data analysis, we see different usage of strategies for each stereotype. For example, we observe increased use of EXTERNAL to challenge socio-economic stereotypes (e.g., poor folks, homeless). Similarly, stereotypes about race-ethnicity (e.g., black, person of color, Asian) and appearance (e.g, fat, overweight, slim) also use more EXTERNAL. These results suggest that there are similarities in strategy usage between identity groups that might be associated with stereotypes with similar characteristics. These results show that there is no one correct strategy for counterspeech and a deeper examination is necessary to select strategies with an understanding of the stereotype and group.
+
+Different intended effects result in different strategy usage. Our results (§6.1, §6.3) also show that different intended effects (e.g., changing a viewpoint) result in different strategy usage. Although most hate speech datasets are limited to short social media comments (Mathew et al., 2019; Yu et al., 2022), as our Reddit data shows, there are diverse forms of harmful speech that requires countering. Therefore, our findings highlight the importance of further investigation into contextual differences in countering hate speech.
+
+# Limitations & Ethical Considerations
+
+While we attempt a comprehensive understanding of countering strategies through user study and analysis of human and machine-generated counterspeech, we face several limitations as well as unanswered ethical considerations, which are discussed below.
+
+Human Judgement Variations in Counterstatements Despite our efforts at creating and
+
+evaluating a diverse set of stereotypes and counterstatements with human evaluators, there are still variations in human judgement that we need further investigation. We also only test relatively short counter-statements and limited variations in how the strategies are presented. Therefore, it would be an important step in future works to understand these human variations and account for diversity in both counter-statements and participant pool.
+
+Human Strategy Evaluation Instructions To intrinsically motivate and empower annotators, which can increase annotation quality (August et al., 2018), we instructed annotators to select the most convincing statement as a "content moderator". However, this framing could limit the context of convincingness of counter-statements to one group of users whose values and decisions can sometimes disagree with the community members (Weld et al., 2022). Therefore, future works should expand this framing and perform experiments under multiple contexts with varying audiences and intentions, especially focusing on counterspeaker motivations (Buerger, 2022), to understand how these variations interact with strategy effects and preferences.
+
+Limitations in Strategy Labeling While we explored different prompts and settings for strategy labeling to improve performance, we did not explore different methods or models. We have also made an effort to create high quality annotations among the authors but were only able to annotate a total of 200 samples across two datasets. This points to a possible further direction into investigating strategy labeling and extraction methods.
+
+Limitations in Counterspeech Generation In this work, we only explore two LLMs for counterspeech generation: GPT-4, a closed model which we have limited information about, and Alpaca. Especially with GPT-4, our lack of understanding of training data and procedure leads to unanswered questions about the results shown in this work. We also used naive prompting where we ask the model to "help respond" to the original post to simulate a non-expert interaction with these models. Therefore, future work should explore different settings and prompts including various definitions of counterspeech and countering strategies to improve counterspeech generation.
+
+Limited Definition of Harm While counterspeech avoids censoring, it can still have some biases and backfiring effects (Lasser et al., 2023). Additionally, due to differences in perceptions of toxicity (Sap et al., 2022), deciding which posts to respond to could introduce biases, and excessive machine responses could limit conversations. Therefore, future works should strive to understand when these systems should be used to generate counterspeech and how they should be integrated into applications.
+
+Machine-generated Persuasive Content Automatic language generation systems have a potential to affect the way users think (Jakesch et al., 2023). Since our main research question asks strategies to persuade readers against harmful beliefs, there is a greater risk of it being used in unintended applications to do the opposite. Additionally, automatically generated counterspeech can be incorrect and therefore can spread false information especially about social groups. Considering these risks, it is important to also investigate into systems and regulations that can protect stakeholders from dangers of malfunctioning or misused systems (Crawford, 2021).
+
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+
+# A Dataset Details
+
+Below sections describe the datasets used in the experiments in detail.
+
+# A.1 Multitarget-CONAN (MTCONAN)
+
+Starting with a seed dataset of human generated hate speech and counterspeech pairs  $(N = 880)$  from NGO workers, Fanton et al. (2021) generate a total of 5,003 hate speech and counterspeech pairs using human-in-the-loop methods. In particular, they used a machine author and human reviewer paradigm where data was generated iteratively using edits made by NGO workers.
+
+# A.2 Change My View (CMV)
+
+The CMV community on Reddit is "dedicated to civil discourse" and utilizes the delta system to explicitly mark view-changing posts. When a comment changes a user's view, either the original poster or other community members can respond with a "Δ" symbol, marking the comment as persuasive. This unique interaction among community members highlights the persuasiveness of various countering strategies and offers valuable insights into their usage and impact on readers.
+
+![](images/5778b64003ed6a39cfb28752587466dd990cf667d11f35e9d4217f293bdcf64d.jpg)  
+Figure 7: The most convincing strategies by stereotypes.
+
+(a) Winning Argument Corpus The Winning Argument Corpus is a dataset of delta and non-delta awarded threads. For each Reddit thread, we consider only the root comment (i.e., direct replies to the original post) to evaluate the arguments used for countering the original post, rather than subsequent interactions. The corpus covers data collected from 2013 to 2015, and to supplement this dataset, we gather additional comments from 2016 to 2022.
+
+(b) Additional Reddit Data To select the most relevant subset of the scraped data (2016-2022)<sup>9</sup>, we follow these steps:
+
+1. Extract posts (longer than 50 words) with demographic-related words in the title<sup>10</sup>, similar to Tan et al.  
+2. For these posts, retrieve only the (root,  $\Delta$ -comment) pairs, where a delta has been awarded to the comment<sup>11</sup>.  
+3. Augment this subset with (root, non- $\Delta$  comment) pairs, where non- $\Delta$  comments are direct replies to the root post that did not receive a delta. For each root post, we select a non- $\Delta$  comment based on the highest sentence-BERT similarity (Reimers and Gurevych, 2019) and at least  $30\%$  word count overlap with the  $\Delta$  comment.
+
+Finally, we result in a dataset (referred to as CMV) of 5,191 comments across the Winning Argument Corpus (Tan et al., 2016) and the additionally scraped Reddit data.
+
+# B Implied Stereotype Extraction
+
+Our goal is to label posts from both the MTCO-NAN and CMV datasets with the following information: (1) whether the post is harmful towards
+
+a demographic group (harmful_to_group), (2) if harmful_to_group, which demographic group the post is targeting (target_group), and (3) if harmful_to_group, the stereotypes implied by the post in the form of a generic.
+
+Based on evidence of its labeling abilities (Ziems et al., 2023), we prompt GPT-4 with 3 example posts (two harmful and one non-harmful) to generate these annotations. Following this, we annotate the hate speech posts from the MTCONAN dataset and the titles from the CMV dataset.[12] The results were then validated by three human expert-annotators and the percentage of those marked correct were  $99.0\%$ ,  $98.5\%$ , and  $98.5\%$  for harmful_to_group, target_group, and the generated generics respectively. The detailed settings and prompts are listed in the following sections.
+
+# B.1 GPT-4 Stereotype Extraction Settings
+
+We use the following setting for stereotype extraction mentioned above: max_tokens  $= 256$  tempearture  $= 0.7$  and top_p  $= 1$
+
+We also use the following system message "You are evaluating and analyzing posts found online on whether they contain hate and how they express potentially harmful beliefs. Give all answers in JSON format." and the user message shown below filled with the original post.
+
+User message: "Is the following post harmful towards a demographic group? If so, which groups is the post targeting? Give the implication of the statement in a generic format (e.g., fat folks don't exercise, women are shallow, asian folks can't drive).\n\n"{}"\n\nGive an answer in the following format: {"is_hate_speech": bool, "harmful_to_group": bool, "target_group": str, "implied.Generic": str}
+
+Few-shot Examples We use the following posts as few-shot examples along with the correct labels:
+
+- "CMV: Black people are biologically more prone to violence."  
+- "Did you hear the one about the Jewish philanthropist? Me neither."
+
+![](images/84950984c17c243db01fab6e886b585a2c0f30eeaebff8c12dd4cf3e7d3c8d59.jpg)  
+Figure 8: Non-delta comments and machine-generated counterspeech comparisons of strategy usage annotated with independent  $t$ -test  $P$  values. The legends indicate ns ( $p < 1.00$ ), ** ( $p < .01$ ), and **** ( $p < .0001$ ). Error bars indicate standard error.
+
+- "Why? So you can get angry and pretend that all women make false rape claims and having sex with a woman is the biggest risk in the history of mankind?"
+
+The fist two examples are used as harmful posts and the last example is used as a non-harmful post.
+
+# C Extended Analysis
+
+In addition to our analysis in §6, here we provide a comparison of strategy usage between non-persuasive comments and machine-generated counterspeech and a qualitative analysis of human and machine-generated counterspeech.
+
+# C.1 CMV: Non-persuasive Comments vs Machine-generated Counterstatements
+
+As seen in Figure 8, machine-generated counter-speech shows similar discrepancies mentioned earlier in §6.2. Compared to persuasive (Δ) comments, non-persuasive (non-Δ) comments used more GEN-DEN, but still, GPT-4 used the strategy more significantly than humans whereas Alpaca used it slightly less. CNTR-EXS was used significantly less for both Alpaca and GPT-4.
+
+# C.2 Qualitative Analysis
+
+In Table 4 we show one example of a harmful post from our Reddit dataset. The original post, which claims that "gay men are incapable of a committed relationship", and its implication that, "gay men are promiscuous", is countered by community members with counter examples. However, GPT-4's
+
+repsonse is characterized by longer and frequent denouncing statements and lack of countering examples. Moreover, the broadening method used and labelled by GPT-4 does not actually counter the implication but rather sets the tone (e.g., "let's promote understanding [...] for everyone's individual choices").
+
+Across a number of samples, we observe some common patterns in the usage of countering strategies. First, GEN-DEN has higher occurrence in the beginning or end of the countering posts and is often supplemented with CNTR-EXS or EXTERNAL descriptions as supporting evidence. Additionally, many CNTR-EXS are from first person experiences (e.g., "my gay friends") or the use of second person pronouns (e.g., "you'd get made fun of"). These observations point to future directions in generating counterstatements, including tone setting and directly addressing the reader.
+
+# D GPT-4 Generation Settings & Prompts
+
+For all generation, we set max_tokens to 256 and use tempearture  $= 0.7$ , and top_p = 1.
+
+# D.1 Counterspeech Generation
+
+We provide the following system message to GPT-4: "You are helping people respond to harmful posts online. Reply directly to the post." along with the following user message where we {post} is replaced with the original post.
+
+User message: "How should I respond to a post that says: "\{post\}?"
+
+# D.2 Strategy Labeling
+
+We use the following system message: "You are analyzing countering strategies used in counter arguments of stereotypes expressed as generics. Give all answers in a JSON format." along with user message and few-shot examples that differ by strategy.
+
+The prompts are shown in Table 5. We fill each prompt with counterspeech and implication. Moreover, we use the examples shown in Table 6 and their respective labeling as few shot examples to the model. We use two examples for each strategy labeling task.
+
+# E Alpaca Generation Setting & Prompts
+
+For counterspeech generation, we used alpaca-7b model from huggingface
+
+Title: Gay men are incapable of a committed loving, monogamous relationship. CMV
+
+Implied Stereotype: Gay men cannot have committed, loving, monogamous relationships
+
+Post: This is from my personal anecdotal experiences. There are exceptions, but  $98\%$  of gay men prefer a lifestyle of casual, multiple sexual relationships. A partial cause of this is the trauma of being marginalized and demonized by society; but given the choice and freedom to choose, a gay man will overwhelmingly choose a lifestyle of multiple partners over a monogamous committed relationship. I support gay marriage, but our portrayal of a gay family unit is a rarity from what is actually happening, statistically
+
+$\Delta$  I'll just chime in on a similar note to others. If you're view is based on anecdotal experience, you should be swayed by the anecdotal experiences of others. I've met lots of gay people, but most of them I honestly don't know what their sexual lifestyle is. But the two close gay friends I have are *both* in long-term monogamous relationships. Meanwhile, I know lots of heterosexual friends who at least at this point in their lives seem entirely disinterested in a long term monogamous relationship. So my experiences seem to refute your position.\n\n
+
+Of course my evidence is only anecdotal, but I am gay and have been in a committed monogamous relationship for 17 years. That is the longest term relationship in my family, including immediate family who have all been married at least there times. I am also part of several gay themes meetups and am,active in several gay organizations, in all instances, majority of men are partnered and most of those relationships are a decade if not decades long.\nAlso, of course you admitted this. It is wildly unfair to judge gay people by criteria that they are not permitted to engage in, namely marriage.
+
+While I understand that you're sharing your personal anecdotal experiences, it's important not to generalize the experiences and preferences of an entire group based on individual cases. The preferences and choices of individuals within the LGBTQ+ community are as diverse as those in any other community. It's crucial to recognize that people's choices and lifestyles are influenced by various factors, and it's not fair to assume that one's sexual orientation is the primary determinant of their relationship preferences. Moreover, using a percentage like "98%" without any supporting data or research can be misleading and perpetuate harmful stereotypes. It's essential to remember that people's choices and preferences are personal and should not be used to judge or define an entire community. Instead, let's promote understanding, acceptance, and respect for everyone's individual choices and lifestyles, irrespective of their sexual orientation.
+
+Table 4: Examples of responses for comparison where  $\Delta$  denotes delta awarded comment,  $\nabla$  denotes non-delta comment, and  $\square$  denotes GPT-4 generated counterspeech. Highlights were added to example quotes chosen by GPT-4 during each counter strategy labeling task.
+
+chavinlo/alpaca-native with max_new_token set to 512.
+
+We used the following prompt template where  $\{\mathrm{input}\}$  is replaced by the post.
+
+Prompt: “Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+Instruction:
+
+You are helping people respond to harmful posts online. Generate a response to the following input.
+
+Input:
+
+{input}
+
+## Response:
+
+# F Strategy Labeling Error Analysis
+
+An example of a post with its predictions and labels are shown in Table 7. Our research team labelled 1
+
+for strategy ALT-GRP since the comment mentions another group "an amputee"; however, GPT-4 did not label this strategy as present.
+
+Moreover, the commenter provides an EXTER- NAL reason for misunderstanding of the original poster by pointing out that "it's typically not so much the malefemale roles they're refuting, but that the way their brain sees their body." This strategy, however, was also predicted to be not present by GPT-4. Such errors highlight the difficulty of the task and model shortcomings.
+
+# G User Study Details
+
+We use the following settings for user study experiment in  $\S 4$
+
+# G.1 Qualification Process & Attention Check
+
+The qualification process included three questions:
+
+1. Humans are mammals.
+
+How true do you think is the above statement?
+
+2. People are right handed.
+
+What percentage of people do you think are right handed?
+
+<table><tr><td>Strategy</td><td>User Message</td></tr><tr><td>ALT-GRP</td><td>Alternate groups are used to challenge generic stereotypes (e.g., women are emotional) and emphasize the presence of various other groups that share similar characteristics (e.g., men can also be emotional). Does the following post include alternate groups to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;alt_groups&quot;: bool, &quot;alt_groups_quotes&quot;: [str]}</td></tr><tr><td>ALT-QUAL</td><td>Alternate qualities are used to challenge stereotypical generic statements (e.g., feminists hate men) by presenting additional qualities that emphasize more positive or accurate characteristics (e.g., feminists are inclusive, respectful, and supportive of equality for both men and women). Does the following post include alternate qualities to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;altqualities&quot;: bool, &quot;altqualities_quotes&quot;: [str]}</td></tr><tr><td>CNTR-EXS</td><td>Counter examples challenge stereotypical generic statements (e.g., women are domestic) by highlighting the existence of numerous instances that contradict such statements (e.g., many women are very career-driven such as women in the medical field, women CEOs, and women professors). Does the following post include counter examples to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;counterexamples&quot;: bool, &quot;counterexamples quotes&quot;: [str]}</td></tr><tr><td>EXTERNAL</td><td>External justifications are used to refute stereotypical generic statements (e.g., fat individuals don&#x27;t engage in exercise) by highlighting external factors that have contributed to the perpetuation of such stereotypes (e.g., media portrayals and beauty standards have reinforced this stereotype, despite the fact that fat individuals do exercise). Does the following post include external reasons to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;external&quot;: bool, &quot;external quotes&quot;: [str]}</td></tr><tr><td>BROAD</td><td>Broadening is used to counter stereotypical generic statements (e.g., women are shallow) by questioning the boundaries imposed on the group to demonstrate either the multitude of variations within the group that render the boundary meaningless or the shared characteristics among all humans (e.g., women are complex individuals like everyone else, and some may be shallow but that cannot be generalized to all women). Does the following post use broadening to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;broadening&quot;: bool, &quot;broadening_quotes&quot;: [str]}</td></tr><tr><td>GEN-DEN</td><td>General denouncing is used to challenge stereotypical generic statements (e.g., feminists hate men) by showing broad disapproval (e.g., this is an unnecessary and hurtful generalization about feminists). Does the following post use general denouncing to counter the statement that {implied stereotype}?\n\nPost:{counterspeech}\n\n{&quot;general-denouncing&quot;: bool, &quot;general-denouncing_quotes&quot;: [str]}</td></tr></table>
+
+Table 5: Prompts used for strategy labeling. We fill {implied stereotype} and {counterspeech} with respective values.
+
+# 3. Penguins can't fly.
+
+What percentage of penguins do you think can't fly?
+
+The workers answer using a slider with percentage ranging 0 to 100 or 11 point likert scale. We accept the answer to correct for each question if they answer 1) 10, 2) greater than or equal to  $50\%$ , and 3) 10. We consider a worker qualified if they score 3 on this task. The workers were paid 0.22 USD for the qualification task. We had 110 participants out of which 95 passed both attention check and choosing different options for most convincing and second most convincing option.
+
+# G.2 Task Instructions
+
+In our task instructions, we asked participants to choose the counter-statement that they found most effective in countering the stereotype.
+
+The exact wording in the instruction was the following: "Pretend you are playing the role of an online content moderator and fact-checker, where your job is to provide counter-statements or corrections when people say things that are stereotypical, generalizations, or blatant biases against certain demographic groups."
+
+Additionally, we provided the following details:
+
+- Determine which of the 6 statements is most effective in counteracting the stereotype by selecting one of 6 options.  
+Note: please select the counter-statements that you think are best to counter the stereotype. There is no right answer.
+
+# G.3 Example UI
+
+UI used for human evaluation is shown in Figure 9. We use attention check, which asks the annotator to
+
+<table><tr><td>Implication</td><td>counterspeech</td></tr><tr><td>lesbians are butch</td><td>&quot;That stereotype is prevalent cause it&#x27;s the easiest to see. There are tons of lesbians that do not look butch and you wouldn&#x27;t know without asking their sexuality. To expand this think of the stereotype of gay feminine men. The bears normally aren&#x27;t outted because they don&#x27;t look stereotypical most of the time.&quot;</td></tr><tr><td>black folks are violent</td><td>&quot;What should be done with this information?\\n\\nShould police target more black people and put them in jail with less scrutiny? Because that what cops in US already do.&quot; 
+&quot;I mean you are not taking into account all the things that affect testosterone production. Eg smoking is more prevalent among black people than it is with white people.(Smoking increases testosterone fyi) Also talking about blacks as an ethnic group is fucking idiotic since african people are genetically by far more varied than people of european descent.&quot;</td></tr><tr><td>feminists are not willing to acknowledge false accusations and women&#x27;s capacity for wrongdoing</td><td>&quot;No one is saying women never do anything wrong - you&#x27;re pretty much completely strawmanning the position. They&#x27;re saying false accusations are rare and unpunished sexual assault is common. None of this has anything to do with accountability, either.&quot;</td></tr></table>
+
+Table 6: Counterspeech and implications used for few-shot prompting.  
+
+<table><tr><td>Implied Generic Comment</td><td colspan="6">“Alternative gender identities are invalid and not to be acknowledged” 
+“How do you feel about transgenderism? From people who I’ve spoken to about being transgender, it’s typically not so much the male/female roles they’re refuting, but that the way their brain sees their body and how it ought to feel is different from what it actually is; almost like the way an amputee has phantom limbs, someone who is transgender feels the different parts of their body.”</td></tr><tr><td>Strategies</td><td>ALT-GRP</td><td>ALT-QUAL</td><td>CNTR-EXS</td><td>EXTERNAL</td><td>BROAD</td><td>GEN-DEN</td></tr><tr><td>Predictions</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Labels</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr></table>
+
+Table 7: An example of a comment from CMV with GPT-4 predictions and labels for each strategy.
+
+select the number between 1 to 5 that is randomly populated.
+
+# H Demographic Identity Related Words
+
+The demographic related words used for data filtering and analysis of countering strategies are shown in Table 8.
+
+# Task
+
+Statement: $(post)
+
+This statement implies the following stereotype: ${stereo}
+
+It may not be politically correct to say, but how much do you agree that this stereotype is true?
+
+Strongly disagree Somewhat disagree Neither disagree nor agree Somewhat agree Strongly agree
+
+# Counter Statements:
+
+<table><tr><td>Counter Statement 1: 
+${example1}</td></tr><tr><td>Counter Statement 2: 
+${example2}</td></tr><tr><td>Counter Statement 3: 
+${example3}</td></tr><tr><td>Counter Statement 4: 
+${example4}</td></tr><tr><td>Counter Statement 5: 
+${example5}</td></tr><tr><td>Counter Statement 6: 
+${example6}</td></tr></table>
+
+Now choose the most convincing counter-statement out of the previous 6 statements:
+
+<table><tr><td>Statement 1: ${example1}</td></tr><tr><td>Statement 2: ${example2}</td></tr><tr><td>Statement 3: ${example3}</td></tr><tr><td>Statement 4: ${example4}</td></tr><tr><td>Statement 5: ${example5}</td></tr><tr><td>Statement 6: ${example6}</td></tr></table>
+
+Please select the fourth option from the left, to double check you're doing this task attentively.
+
+![](images/6a184ce4236bb86077dbd1336ceefc0f8a25019ba05b9f122e95948fa67ba46d.jpg)  
+Figure 9: UI used for human evaluation in  $\S 4$
+
+Now choose the second-most convincing counter-statement out of the remaining statements (excluding the one you selected above):
+
+<table><tr><td>Statement 1: $(example1)</td></tr><tr><td>Statement 2: $(example2)</td></tr><tr><td>Statement 3: $(example3)</td></tr><tr><td>Statement 4: $(example4)</td></tr><tr><td>Statement 5: $(example5)</td></tr><tr><td>Statement 6: $(example6)</td></tr></table>
+
+<table><tr><td>Demographic Dimension</td><td>Terms</td></tr><tr><td>gender identity</td><td>women, men, guys, girls, nonbinary, transgender, cisgender, agender, trans, non-binary, cis, sex, gender</td></tr><tr><td>intersectional</td><td>white men, black women, black men, white women, straight white, queer of color, straight white men, queer white men, queer white women, queer men of color, queer women of color, queer men, queer women, trans of color, cishet white, cisgender heterosexual white, transgender of color, non-disabled white, disabled of color</td></tr><tr><td>sexual orientation</td><td>straight, heterosexual, gay, lesbians, bisexuals, queer, asexual, homosexual, lgbtq, lgbt, monogamous, polyamorous, gay men, lesbian women, butch, bear, femme, feminine</td></tr><tr><td>age</td><td>teenagers, older, millennials, elderly, old, young, middle aged, younger</td></tr><tr><td>race ethnicity</td><td>person of color, people of color, asian americans, asian-americans, asians, asian, african americans, african-americans, black, white-americans, white americans, white, caucasians, hispanic, latinx, latinos, latinas, latin americans, native americans, native, indigenous, native american/first nation, arabs, american indians, alaska native, native hawaiians, pacific islanders</td></tr><tr><td>nationality</td><td>Chinese, Japanese, American, Canadian, Indian, Middle Eastern, European, African, Korean, Mexican, Russian, Cuban, Italian, German, French, Jamaican, Filipino, non-American, foreign, foreigners, Americans</td></tr><tr><td>religion</td><td>buddhists, hindus, christians, jewish, jews, agnostic, muslims, mormons, orthodox, atheists, Taoists, protestants, christian, catholics, sikhs, amish, non-religious, satanists, muslim</td></tr><tr><td>disability</td><td>able-bodied, non-disabled, disabled, paralyzed, vision impaired, blind, hearing impaired, deaf, hard of hearing, differently abled, hearing impairment, a visual impairment, vision impairment, disability, paraplegia, quadriplegia, short, cognitive disability, intellectual disability, learning disability, ADHD, a brain injury, autism, autistic, depression, bipolar disorder, psychosocial disability, a mental health condition, mentally disabled, mental illness, mental disorder</td></tr><tr><td>appearance</td><td>tall, short, fat, thin, slim, overweight, bald, ugly, beautiful, light skinned, dark skinned, attractive, unattractive, obese, obesity</td></tr><tr><td>education</td><td>highly educated, less educated, smart, less smart, dumb, stupid</td></tr><tr><td>personality</td><td>introverts, extroverts</td></tr><tr><td>politics</td><td>democrats, libertarians, liberals, republicans, conservatives, feminists</td></tr><tr><td>socio</td><td>rich, wealthy, poor, homeless, lower class, welfare, middle class, working class, upper class, formerly incarcerated, first generation, immigrants, refugees, US citizens</td></tr><tr><td>country</td><td>China, India, the United States, Indonesia, Pakistan, Brazil, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, Philippines, Egypt, Vietnam, DR Congo, Turkey, Iran, Germany, Thailand, the United Kingdom, France, Italy, South Africa, Myanmar, Kenya, South Korea, Colombia, Spain, Uganda, Argentina, Algeria, Sudan, Ukraine, Iraq, Afghanistan, Poland, Canada, Morocco, Saudi Arabia, Uzbekistan, Peru, Angola, Malaysia, Mozambique, Ghana, Yemen, Nepal, Venezuela, Madagascar, Cameroon, North Korea, Australia, Belgium, Cuba, Greece, Portugal, Sweden, Austria, Israel, Switzerland, Singapore, Norway, Finland, Denmark, New Zealand, Cambodia, Ethiopian, Arabic, Polish, Italians</td></tr><tr><td>continents</td><td>Africa, Asia, the Middle-East, Europe, North America, Central America, South America, Oceania</td></tr><tr><td>other</td><td>racism, sexism</td></tr></table>
+
+Table 8: Demographic dimensions and related terms.

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2023/Beyond Good Intentions_ Reporting the Research Landscape of NLP for Social Good/full.md

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+# Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
+
+Fernando Gonzalez $^{1,\ast}$  Zhijing Jin $^{1,2,\ast}$  Bernhard Scholkopf $^{1,2}$  Tom Hope $^{4,5}$  Mrimmaya Sachan $^{1,\dagger}$  and Rada Mihalcea $^{3,\dagger}$ $^{1}$ ETH Zürich  $^{2}$ MPI for Intelligent Systems  $^{3}$ University of Michigan  
+ $^{4}$ The Hebrew University of Jerusalem  $^{5}$ Allen Institute for AI (AI2) {fgonzalez, jinzhi, msachan} @ethz.ch bs@tue.mpg.de tomh@allenai.org mihalcea@umich.edu
+
+# Abstract
+
+With the recent advances in natural language processing (NLP), a vast number of applications have emerged across various use cases. Among the plethora of NLP applications, many academic researchers are motivated to do work that has a positive social impact, in line with the recent initiatives of NLP for Social Good (NLP4SG). However, it is not always obvious to researchers how their research efforts tackle today's big social problems. Thus, in this paper, we introduce NLP4SGPAPERS, a scientific dataset with three associated tasks that can help identify NLP4SG papers and characterize the NLP4SG landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying the task they solve and the methods they use. Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG.
+
+# 1 Introduction
+
+With the rapid advancement of natural language processing (NLP) technology (Radford et al., 2018; Devlin et al., 2019; Brown et al., 2020; Ouyang et al., 2022), there has been a growing interest in using NLP for applications with social impact in recent years (Wu et al., 2020; de Mello et al., 2019; Jin and Mihalcea, 2023). This effort has been condensed into the initiative of "NLP for Social Good" (NLP4SG) (Jin et al., 2021), which began with an overall theoretical framework
+
+![](images/97d4af9052ae0b02a5a5ec27346fb34cb99d0dcca5c18932885b969df1a870ba.jpg)  
+Figure 1: To generate the NLP4SG progress report, we provide a PaperAnalyzer, which identifies social good papers (Task 1), classifies the relevant UN SDGs (Task 2), and analyzes salient scientific terms (Task 3). We process all ACL Anthology papers to create a Sankey diagram (bottom) on our website.
+
+(Jin et al., 2021) in 2021, and is reaching broader community impact through various workshops at NLP conferences (Field et al., 2021; Biester et al., 2022).
+
+However, most of the efforts so far have consisted of disparate community discussions, opinion pieces, and projects, yet without a unifying community-based insight. This is partly due to a lack of understanding of how NLP research efforts align with social good implications. We argue that it is critical to have community reports
+
+based on solid empirical evidence, similar to the United Nations' (UN) annual compilation of the report on Sustainable Development Goals (SDGs) (e.g., United Nations, 2021, 2022). Such progress reports for NLP4SG can help calibrate intuitions with facts and (1) inspire the NLP community to understand how well NLP research address social good; (2) raise awareness of under- or unaddressed areas; (3) serve as a reference for researchers who start in the field when deciding on what topics to take on; and (4) foster better connections between the research community and social science experts, allowing for more impactful research and feedback to improve NLP4SG.
+
+To this end, we design a new suite of tasks for analysing NLP4SG papers, spanning from identification of NLP4SG papers, classifying them into SDGs, to identifying the task and methods used in them. We create NLP4SGPAPERS, a scientific dataset of 5,000 papers annotated with the above three tasks, and use the dataset to build an "NLP for NLP" PaperAnalyzer system (Figure 1) that applies the state-of-the-art NLP models to the NLP papers.
+
+Using our PaperAnalyzer system, we parse the entire database of 76K ACL Anthology papers, and find the following trends: (1)  $13.07\%$  of the papers are related to social good; (2) healthcare and education are popular goals among NLP4SG papers, but goals such as poverty and hunger are largely unaddressed; (3) most common tasks addressed by the community in NLP4SG are machine translation, text classification, and toxicity detection, and although the most common models are large language models (LLMs) (e.g., Radford et al., 2018; Devlin et al., 2019; Liu et al., 2019), these innovations have not been deployed in all tasks.
+
+In summary, our contributions are as follows:
+
+1. We introduce a suite of novel tasks to analyze NLP4SG papers, and support it with NLP4SGPAPERS, a scientific dataset annotated with 5,000 NLP papers.  
+2. We provide our PaperAnalyzer system, which consists of LLM-based models on the three tasks.  
+3. We analyze the entire ACL Anthology to understand key trends in NLP4SG from 1980.  
+4. We open-source our NLP tools, and build a publicly accessible website to visualize the
+
+analysis results, making the progress tracking accessible for anyone interested in NLP4SG.
+
+# 2 Goals of the NLP4SG Report
+
+We begin by listing the key questions that drive our NLP4SG progress report and the desired properties of a good NLP4SG report, and highlight what purpose the report will serve. Finally, we introduce our proposed NLP-for-NLP pipeline.
+
+Key Questions. When envisioning the NLP4SG progress report, we find the following questions crucial to address:
+
+Q1 Overall estimate. How large is the proportion of NLP4SG papers among all NLP papers? How does this proportion change over the years?  
+Q2 Social good aspects. Among NLP4SG papers, what specific social good aspects do they tend to address?  
+Q3 Technical solutions. For each social good goal, how are the papers addressing them? For example, what tasks do people formulate, and what models do people use? Is there an overlooked space?
+
+Properties of a Good Progress Report. After settling in the key questions that the report should aim to answer, we propose several criteria for a good automation of the NLP4SG progress report. A good report should (P1) comprehensively address the questions; (P2) base its answers on representative, large data; (P3) require little repetitive manual effort, ideally largely automated; (P4) be transparent and open-sourced so that it is trustworthy; (P5) be reader-friendly for both technical and non-technical audiences; and (P6) be easy to access for all parties, including but not limited to researchers, social good organizations such as NGOs, funding agencies, and the general public.
+
+Missions. This report aims to serve several purposes. First, we want to convey an overall positive and encouraging message that there is a growing and diverse community of researchers who are interested in NLP4SG. Second, we want to improve the cohesion of this community, by making the information on the existing research landscape easier to access, so that people can build on each other's research. Third, we aim to inspire more people to develop novel tasks and use
+
+![](images/6073185e392fc7ff9a7bff0009c43bb8e996c08984b6fa5414fe48baddf3f459.jpg)  
+Figure 2: Our pipeline to automate the NLP4SG report.
+
+cases to fill in the under-addressed social impact areas. Finally, we want to make the current progress transparent and public to all interested parties, especially non-research parties, including organizations such as NGOs working on NLP4SG, decision-makers, and the general public. By doing this, we can better connect the research community with all other related communities, not only to broadcast the impact of NLP4SG to the broader community, but also to help improve NLP research with external feedback, and formulate research questions more aligned with real-world problems.
+
+Our Proposed NLP-for-NLP pipeline. We develop an NLP-for-NLP pipeline to automatically analyze papers for the NLP4SG report, as shown in Figure 2. As part of this pipeline, we develop a PaperAnalyzer system consisting of various models trained to answer the three questions (Q1-Q3).
+
+The pipeline consists of the following steps: (1) We annotate various datasets that can help us answer the three questions (meeting P1 and P2). (2) We use this data to train and evaluated our PaperAnalyzer models. (3) Next, we run our NLP4SG PaperAnalyzer (which meets P3) on a large repository of NLP papers, and compose a comprehensive NLP4SG progress report, together with an open-source GitHub release of our code (which meets P4) and an interactive interface whose screenshots are in Appendix A (which meets P5 and P6). (4) Finally, we distill some key findings for the NLP community, to pave the way for more cooperative, evidence-guided community efforts to fuel NLP4SG.
+
+# 3 NLP4SGPAPERS Dataset
+
+We build a NLP4SGPAPERS dataset to address the following three tasks as illustrated in Figure 1:
+
+- Task 1: Binary classification of NLP4SG papers (for Q1).  
+- Task 2: Multi-label multi-class categorization of NLP4SG papers into the 17 UN SDGs (for Q2).  
+- Task 3: Identification of salient scientific terms (tasks and methods) in NLP4SG papers (for Q3).
+
+Next, we introduce the data collection process, and then analyze the data statistics for each task.
+
+# 3.1 Dataset Collection
+
+Data Source and Preprocessing. We identify the ACL Anthology as an ideal data source. We obtain 76,229 papers from ACL Anthology that were published by May 27, 2022. We randomly sample 5,000 papers uniformly over the entire ACL Anthology and parse their titles and abstracts. See Appendices B.1 and B.2 for discussions about the data sources and our data preprocessing details. We split the dataset into 2,500 train samples, 500 development samples, and 2,000 test samples. Statistics of the data are shown in Table 1.
+
+Task Formulation. Let us denote our dataset as  $D := \{x_i\}_{i=1}^N$  consisting of  $N$  papers, each paper  $x := (t, a)$  contains a title  $t$  and abstract  $a$ .
+
+Task 1 is a binary classification task  $f_{1}:\mathcal{X}\to \mathcal{Y}$ , which maps each paper to a binary label space  $\mathcal{Y} = \{0,1\}$  representing whether a paper address a social good task  $(y = 1)$  or not  $(y = 0)$ .
+
+Task 2 is a multi-label multi-class classification task  $f_{2}:\mathcal{X}^{\prime}\to \mathcal{Z}$ , which maps NLP4SG papers  $\mathcal{X}^{\prime} = \{\pmb {x}|f_{1}(\pmb {x}) = 1,\pmb {x}\in D\}$  to a space  $\mathcal{Z}$ , which is the power set of all 17 UN SDGs.
+
+In Task 3, we extract the main NLP tasks addressed and methods used in the NLP4SG papers,  $f_{3}: \mathcal{X}' \to \mathcal{W}_{\mathrm{t}} \times \mathcal{W}_{\mathrm{m}}$ , where  $\mathcal{W}_{\mathrm{t}}$  and  $\mathcal{W}_{\mathrm{m}}$  represent the space of the power sets of the main tasks and methods in NLP4SG papers, respectively. Given our desiderata, it is better to make the extracted terms easy to use for summarizing trends in our
+
+Sankey diagram (Figure 1). Inspired by the annotation scheme of Papers with Code, we use task names such as "machine translation" and "toxicity detection"; and we use method names such as "BERT-based models," "long short-term memory networks (LSTMs)," and "support vector machines (SVMs)." We provide the detailed list in Appendix B.3.4. This allows us to use Task 3 to provide evidence that can help us answer high-level questions such as what NLP4SG work uses what type of technology, as shown in Figure 1.
+
+Data Annotation. The data associated with each task was annotated by two NLP researchers who are proficient in English. We provide detailed annotation guidelines for each task in Appendix B.3. Briefly, the inter-annotator agreements are  $92.93\%$  Cohen's kappa for Task 1,  $88.67\%$  Cohen's kappa for Task 2, and achieve a high similarity score of  $84.63\%$  BERTScore (Zhang et al., 2020) between the annotator's responses for Task 3. We resolved cases of disagreement by first asking them to review potential oversights, and then clarifying the annotation principles for samples that are ambiguous. With these clarifications, the annotators converged to an agreement through re-iteration and discussions.
+
+# 3.2 Dataset Characteristics
+
+Data Overview for Task 1. We show the overall statistics in Table 1. We can observe that  $11.84\%$  of the papers are related to social good (592 papers of our sample of 5000).
+
+<table><tr><td></td><td># All Papers</td><td># SG Papers</td><td># Sents</td><td># Tokens</td><td>Vocab</td></tr><tr><td>Total</td><td>5,000</td><td>592 (11.84%)</td><td>40,533</td><td>656,939</td><td>45,088</td></tr><tr><td>Train</td><td>2,500</td><td>284</td><td>20,305</td><td>330,618</td><td>29,274</td></tr><tr><td>Dev</td><td>500</td><td>57</td><td>4,112</td><td>65,705</td><td>10,511</td></tr><tr><td>Test</td><td>2,000</td><td>251</td><td>16,116</td><td>260,616</td><td>24,913</td></tr></table>
+
+Table 1: Statistics for Task 1. We show the number of social good-related papers (# SG Papers), along with the general statistics, including the total number of papers (# All Papers), sentences (# Sents), tokens (# Tokens), and vocabulary size (Vocab).
+
+Data Overview for Task 2. For Task 2, we first show in Table 2 the distribution of all 17 UN SDGs among all the 592 social good-related papers. The distribution is strongly skewed, with some goals covering the majority of the data. For example,
+
+the top three goals - G3 Health (34.12%), G16 Peace (31.93%), and G4 Education (16.22%) - cover a total of 82.27% of the NLP4SG papers. Many goals rarely occur or are even not present in the dataset, such as G1 Poverty and G2 Hunger. This distribution suggests either a lack of attention from the NLP4SG community to some of these UN goals or challenges in addressing these goals through NLP. Additional analyses are presented in Section 5.
+
+To enrich the data for low- or zero-occurrence classes, we perform upsampling to increase the number of samples corresponding to all the classes that constitute less than  $5\%$  of our dataset. Specifically, we take the unannotated part of the ACL Anthology, run the best model for Task 1 to identify social good papers, and use SDG-specific keywords to obtain highly possible paper candidates for the under-addressed SDGs. Then, we manually check the SDGs that these papers address. By this procedure, we enrich the original data by 167 more papers, which are more distributed over the low- or zero-occurrence classes. Details of the upsampling procedure are in Appendix B.3.3.
+
+We report the statistics of the upsampled dataset in Table 2 and more in Appendix B.3.3. Since each paper can correspond to several UN SDGs (on average, 1.10 goals per paper), we plot the heatmap of co-occurrences of the SDGs in Figure 3. As we can see, some of the top correlations are seen between G2 Hunger and G8 Economy, as well as between G5 Gender and G10 Inequalities.
+
+![](images/63f9fd4ce086442be562e008ed1ee5d7999d5038a322d19a562393f72d15c1e1.jpg)  
+Figure 3: The correlation heatmap of the 17 UN SDGs.
+
+<table><tr><td>UN SDG</td><td>Proportion</td><td># Samples
+(Before → After)</td></tr><tr><td>G1. Poverty</td><td>0.00%</td><td>0 → 0</td></tr><tr><td>G2. Hunger</td><td>0.00%</td><td>0 → 4</td></tr><tr><td>G3. Health</td><td>34.12%</td><td>202 → 202</td></tr><tr><td>G4. Education</td><td>16.22%</td><td>96 → 97</td></tr><tr><td>G5. Gender</td><td>3.04%</td><td>18 → 85</td></tr><tr><td>G6. Water</td><td>0.00%</td><td>0 → 1</td></tr><tr><td>G7. Energy</td><td>0.00%</td><td>0 → 0</td></tr><tr><td>G8. Economy</td><td>2.36%</td><td>14 → 58</td></tr><tr><td>G9. Innovation</td><td>13.85%</td><td>82 → 97</td></tr><tr><td>G10. Inequalities</td><td>5.24%</td><td>31 → 32</td></tr><tr><td>G11. Sustainable Cities</td><td>0.84%</td><td>5 → 16</td></tr><tr><td>G12. Consumption</td><td>0.17%</td><td>1 → 1</td></tr><tr><td>G13. Climate</td><td>0.17%</td><td>1 → 20</td></tr><tr><td>G14. Life Below Water</td><td>0.00%</td><td>0 → 2</td></tr><tr><td>G15. Life on Land</td><td>0.00%</td><td>0 → 0</td></tr><tr><td>G16. Peace</td><td>31.93%</td><td>189 → 201</td></tr><tr><td>G17. Partnership</td><td>2.70%</td><td>16 → 21</td></tr></table>
+
+Table 2: Statistics of the data for Task 2. We first show the natural proportion of UN SDGs in the dataset, which motivates us to do upsampling. And we show the number of samples before and after upsampling. G1, G7, and G15 remain absent from the dataset even after upsampling.
+
+Data Overview for Task 3. Since there has been significant previous work (Jain et al., 2020; Luan et al., 2018) on extracting salient information from scientific literature, we only annotate test data for this task to evaluate existing models in our domain of NLP4SG. On average, the spans of task terms are 3.6 words long, and the spans of method terms have 2.4 words long. Also, each paper has an average of 1.06 tasks and 1.5 methods, and the entire set has a total of 263 unique tasks and 335 unique methods.
+
+# 4 NLP4SG PaperAnalyzer
+
+Our PaperAnalyzer system works as shown in Figure 1, with three sequential steps: we first train a binary classifier (addressing Task 1), followed by a UN SDG goal classifier (addressing Task 2), and finally processing the paper with a pretrained task and method extractor (addressing Task 3).
+
+# 4.1 NLP4SG Binary Classifier
+
+Experimental Setup. We adopt several commonly used LLMs on our NLP4SGPAPERS dataset, including BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), SciBERT (Beltagy et al., 2019), and InstructGPT (Ouyang et al., 2022) text-davinci-002.
+
+We use distant supervision (DS) for data augmentation when finetuning the models. To obtain dis
+
+tantly supervised data, we use two methods to identify additional NLP4SG papers from the unannotated set of almost 76K papers. We first apply keyword matching using a list of curated NLP4SG keywords such as "healthcare" and "education", and then we also include papers with high cosine similarity with the textual descriptions of the UN SDGs. See the full list of keywords and details of how we compile the distantly supervised data in Appendix D.1.1, and see our model implementation details in Appendix D.1.2.
+
+<table><tr><td></td><td>F1</td><td>Acc</td><td>P</td><td>R</td></tr><tr><td>Random (Proportional)</td><td>6.68</td><td>79.05</td><td>7.61</td><td>5.95</td></tr><tr><td>Random (Uniform)</td><td>15.65</td><td>46.65</td><td>9.77</td><td>39.29</td></tr><tr><td>InstructGPT (Zero-Shot)</td><td>28.83</td><td>80.25</td><td>27.49</td><td>30.30</td></tr><tr><td>InstructGPT (Few-Shot)</td><td>40.72</td><td>86.75</td><td>49.73</td><td>34.47</td></tr><tr><td>BERT</td><td>65.87</td><td>92.90</td><td>83.03</td><td>54.58</td></tr><tr><td>BERT + DS</td><td>73.14</td><td>94.05</td><td>84.38</td><td>64.54</td></tr><tr><td>RoBERTa</td><td>74.89</td><td>94.30</td><td>83.74</td><td>67.73</td></tr><tr><td>RoBERTa + DS</td><td>75.16</td><td>94.35</td><td>83.82</td><td>68.13</td></tr><tr><td>SciBERT</td><td>73.42</td><td>94.10</td><td>84.90</td><td>64.68</td></tr><tr><td>SciBERT + DS</td><td>75.98</td><td>94.15</td><td>78.39</td><td>73.71</td></tr><tr><td colspan="5">Ablation Study</td></tr><tr><td>SciBERT + DS by Similarity</td><td>62.96</td><td>89.35</td><td>55.86</td><td>72.11</td></tr><tr><td>SciBERT + DS by Keyword</td><td>74.68</td><td>94.10</td><td>80.93</td><td>69.32</td></tr></table>
+
+Table 3: Model performance on Task 1, including the random baselines, whose distribution are proportional to the label distribution or uniform; as well as common LLMs, with and without our proposed DS technique. We report the F1 score, precision (P), and recall (R) of the positive class, as well as accuracy (Acc). For the best-performing model, SciBERT+DS, we also conducted ablation studies removing the different subsets of our DS data.
+
+Results. We show our model performance for the binary classification task in Table 3. The best-performing model is SciBERT+DS, which is enhanced by the addition of our DS technique. DS contributes 2.56 percentage points (pp) improvement over the baseline SciBERT model on fully supervised data only. Moreover, with the ablation study, we show that by using only DS by keyword we get an improvement of  $1.26\mathrm{pp}$  on the F1 score. Using only similarity DS does not improve the F1 score, but causes an increase in recall of  $7.43\mathrm{pp}$ . Both DS techniques combined help the model to increase the F1 score to  $75.98\%$ . We choose the model with the best F1 score because we want to get a precise but also a complete report of the NLP4SG landscape.
+
+Interpretability & Error Analysis. To understand the working of our model, we apply a
+
+commonly used interpretability tool, the local interpretable model-agnostic explanations (LIME) (Ribeiro et al., 2016) to our best performing model, SciBERT+DS. LIME outputs the words that our model relies on as positive and negative evidence for the classification. Some common positive keywords for our model include "biomedical", "students", and "social", while some negative keywords are "lexical", "Parsing" and "grammar". The details of our LIME implementation and more visualized examples can be found in Appendix E.1.
+
+Our model makes a few common error types, including: (1) For false positives (FP), many papers include the common word "social", which appears in  $10.10\%$  of the FP papers. We suspect that this is due to the frequent use of "social media", which is not necessarily related to social good, and the fact that "social" is a good indicator of an NLP4SG paper since it appears in  $15.03\%$  of the true positive papers. (2) Among false negative (FN) samples, a large subset is innovation and education-related papers, constituting  $30.30\%$  and  $27.27\%$  respectively among the FN, larger than their proportion  $(13.55\%$  and  $23.90\%)$  in the entire test set. A reason might be that the frequent use of words like "learning" and scientific vocabulary makes our model struggle identifying a research paper that uses machine learning vs one addressing education topics.
+
+# 4.2 UN SDG Goal Classifier
+
+Experimental Setup. For Task 2, we evaluate zero-shot models using all the 759 annotated NLP4SG papers as our test set, as it is challenging to train classifiers for 17-class classification with large imbalance that results in several low-occurrence classes. Specifically, we first adopt the pretrained MNLI classifiers, including BART (Lewis et al., 2020), DistilBERT (Sanh et al., 2019), and DeBERTa (He et al., 2021). We also include the InstructGPT model (Ouyang et al., 2022), which we ask to generate a list of relevant SDGs for the paper. More experimental setup details are in Appendix D.2, including the prompts, implementation details for the models, and the evaluation metrics.
+
+Results & Error Analysis. We evaluate all models and report their results in Table 4. Among all the models, InstructGPT has the highest perfor
+
+mance, scoring  $69.37\%$  F1. It also has the highest performance on all other measures, such as partial list match with  $74.57\%$  of the samples, and exact list match with  $66.66\%$  of the samples.
+
+<table><tr><td>Model</td><td>F1</td><td>PM</td><td>EM</td><td>P</td><td>R</td></tr><tr><td>BART</td><td>27.30</td><td>37.55</td><td>10.80</td><td>44.22</td><td>35.65</td></tr><tr><td>DistilBERT</td><td>26.09</td><td>58.23</td><td>3.95</td><td>17.78</td><td>56.66</td></tr><tr><td>DeBERTa</td><td>25.50</td><td>39.13</td><td>11.20</td><td>37.82</td><td>35.89</td></tr><tr><td>InstructGPT</td><td>69.37</td><td>74.57</td><td>66.66</td><td>77.64</td><td>67.95</td></tr></table>
+
+Table 4: Model performance on Task 2. We report the weighted F1, percentage of samples whose ground-truth list of goals exactly matches (EM) with the model output, percentage of samples whose ground-truth list of goals partially matches (PM) with the model output, precision (P), and recall (R).  
+
+<table><tr><td>UN SDG</td><td>F1</td><td>P</td><td>R</td></tr><tr><td>G1. Poverty</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G2. Hunger</td><td>75.00</td><td>75.00</td><td>75.00</td></tr><tr><td>G3. Health</td><td>88.78</td><td>87.50</td><td>90.10</td></tr><tr><td>G4. Education</td><td>73.45</td><td>64.34</td><td>85.57</td></tr><tr><td>G5. Gender</td><td>84.56</td><td>98.44</td><td>74.12</td></tr><tr><td>G6. Water</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>G7. Energy</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G8. Economy</td><td>54.32</td><td>95.65</td><td>37.93</td></tr><tr><td>G9. Innovation</td><td>26.45</td><td>66.67</td><td>16.49</td></tr><tr><td>G10. Inequalities</td><td>27.40</td><td>24.39</td><td>31.25</td></tr><tr><td>G11. Sustainable Cities</td><td>44.78</td><td>29.41</td><td>93.75</td></tr><tr><td>G12. Consumption</td><td>50.00</td><td>33.33</td><td>100.00</td></tr><tr><td>G13. Climate</td><td>85.71</td><td>100.00</td><td>75.00</td></tr><tr><td>G14. Life Below Water</td><td>80.00</td><td>66.67</td><td>100.00</td></tr><tr><td>G15. Life on Land</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G16. Peace</td><td>80.74</td><td>84.07</td><td>77.66</td></tr><tr><td>G17. Partnership</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Weighted Average</td><td>69.37</td><td>77.64</td><td>67.95</td></tr></table>
+
+Table 5: The class-specific performance of our best-performing InstructGPT method.
+
+We also perform a more detailed breakdown of the performance of our best-performing model by different UN SDGs in Table 5. We can see that it is easy for the model to distinguish goals such as health, gender equality, and climate. However, other goals such as social inequalities and innovation are ambiguous for the model. One possible reason is that some NLP papers use domain-specific words such as "bias," which can also mean data bias, or spurious correlations in the data, so the model might confuse it with the general meaning of bias towards or against certain groups of people in society.
+
+# 4.3 Task and Method Analyzer
+
+Experimental Setup. Although our goal is to extract the main tasks and methods for our Sankey diagram, our setting is novel and distinct from tra
+
+ditional sentence-level named entity recognition (NER), which aims at span-level extraction of all named entities. Instead, our setting is document-level as we just need one mention of the same term in case there are co-references or paraphrases. We also accept generative answers if there are no appropriate existing spans. Thus, we evaluate Task 3 using common metrics for extractive and generative question answering (QA) (Rajpurkar et al., 2016; Chen et al., 2019; Sai et al., 2023), such as exact match, F1, and BERTScore (Zhang et al., 2020).
+
+We first apply commonly used LLMs finetuned on QA datasets, such as BERT (Devlin et al., 2019), RoBERTa, and DeBERTa (He et al., 2021). We also include the scientific NER models, PURE (Zhong and Chen, 2021) and SciREX (Jain et al., 2020). Finally, we include the general-purpose model, InstructGPT (Ouyang et al., 2022). Model implementation details are in Appendix D.3.
+
+<table><tr><td rowspan="2">Model</td><td colspan="3">Task</td><td colspan="3">Method</td></tr><tr><td>BS</td><td>F1</td><td>EM</td><td>BS</td><td>F1</td><td>EM</td></tr><tr><td>BERT</td><td>82.29</td><td>35.08</td><td>15.94</td><td>75.04</td><td>23.08</td><td>4.38</td></tr><tr><td>RoBERTa</td><td>81.39</td><td>33.43</td><td>14.74</td><td>76.47</td><td>27.30</td><td>9.56</td></tr><tr><td>DeBERTa</td><td>80.36</td><td>31.28</td><td>11.16</td><td>75.62</td><td>26.05</td><td>4.78</td></tr><tr><td>PURE</td><td>71.30</td><td>34.67</td><td>5.58</td><td>69.65</td><td>30.43</td><td>5.98</td></tr><tr><td>SciREX</td><td>84.30</td><td>49.03</td><td>14.74</td><td>72.14</td><td>33.70</td><td>6.37</td></tr><tr><td>InstructGPT</td><td>84.65</td><td>43.94</td><td>19.12</td><td>77.95</td><td>32.05</td><td>11.55</td></tr></table>
+
+Table 6: Model performance on Task 3 by BERTScore F1 (BS), F1, and Exact Match (EM). Due to space limitations, we omit the "text-davinci" when describing the InstructGPT.
+
+Results. We show the model performance for Task 3 in Table 6. The best model by BERTScore is InstructGPT-002, reaching  $84.65\%$  for task extraction, and  $77.95\%$  for method extraction. It also achieves a higher exact match score than other strong models such as SciREX.
+
+# 5 Insights
+
+Using our NLP4SG PaperAnalyzer system, we analyze all the 76K NLP papers from ACL Anthology to answer our driving questions Q1, Q2, and Q3. Additionally, we also build a demo website at nlp4sg. vercel. app to make our analysis results more accessible to all parties interested in NLP4SG. Our website starts with a homepage in Figure 4, whose visualization plots we will refer to for the related analysis in this section. For a full overview of various demos on the website, see
+
+Appendix A.
+
+# NLP for Social Good
+
+An initiative aiming to empower natural language processing (NLP) researchers to expand the societal impact of their work.
+
+![](images/a80f99c385f6365789ec8f40b28a9914d6c768be246b33971b25e7ef647f0e1a.jpg)  
+NLP4SG Research Overview
+
+![](images/539bfaf4d2bcd0e1e94d3574d58cd1adad2343d53d59765b6a4757831c3da17c.jpg)  
+Topic Clustering
+
+![](images/07f52e5177ea6cd5a188c73c5f499ecb53e4915816d0939911fa9821db9346fa.jpg)  
+NLP4SG Tasks and Methods  
+Figure 4: The homepage of our NLP4SG website.
+
+![](images/d27f34d153a476f96dc284360bc96c6017ea8f72a689ac4cf1d5d43c09c85694.jpg)  
+Organizations Diagram
+
+Q1. How Many NLP4SG Papers Are There? We apply our NLP4SG binary classifier to the entire ACL Anthology with 76K papers. Overall, there are around  $13.07\%$  social good-related papers. For a more detailed overview, we plot the proportion of social good-related papers among all NLP papers every year from 1980 in Figure 5. We can see a clear rising trend of NLP4SG in recent years. Notably, the proportion of NLP4SG papers increased to  $19.0\%$  in 2020 from the initial percentage of  $8.2\%$  in 1980. It is important to note that while these percentages provide us with directional insight into the trend, their accuracy is contingent on the precision of the model used for analysis.
+
+Q2. What Aspects of Social Good Do the Pap-ers Address? Next, we are interested in the question of what social good aspects are addressed by NLP research.
+
+In Figure 6, we show three bar charts side by side. The left one shows the importance scores of all the 17 SDGs according to Yang et al. (2020)'s worldwide survey of 360 sustainability researchers, where they incorporate all expert ratings into an overall priority score. The chart in
+
+![](images/05af179b86be8977db06838de5761dd54c541b3e9b69595b6dc663fba779c94b.jpg)  
+Figure 5: Percentage of social good-related papers (top) and the total number of papers (bottom) in ACL Anthology each year from 1980.
+
+![](images/4648ea933d9db33818edf0c840f6400004b60a677d914ce53280efa783e32cfe.jpg)  
+Figure 6: SDGs importance vs actual efforts. We use darker red for shorter bars, so the overlooked goals (especially on the right chart) can draw more attention.
+
+the middle is compiled from our survey on "How current NLP researchers think about NLP4SG" at ACL 2021 of 80 NLP researchers, where we ask the subjects "What social good applications do you think the NLP community should consider?" and then map the answers (e.g., NLP for education, and combating misinformation) to corresponding SDGs. The survey details are in Appendix F.1. Finally, we apply our UN SDG goal classifier to all the ACL Anthology papers, and visualize the distribution of the 17 goals into the right panel of Figure 6. Our demo website contains a version of this tri-view comparison in Appendix A.1, where the user can specify whether they want to look at the overall trend, or the trend in any specific year.
+
+From the overall comparison in Figure 6, we can
+
+see a strong mismatch between the importance of the goals (left) and the actual size of research efforts (right). For example, NLP4SG papers mostly address health, peace, and education, which are admittedly important goals. However, they hardly address other important goals such as poverty and climate, which are pressing social issues. This might be because it may be more challenging to address these topics, e.g., lack of available datasets and expertise, trends dominating researchers' attention, funding climate (e.g., healthcare research being better funded), ease of publishing papers, the popularity of the field which may help gain citations more easily, the existence of good benchmark datasets, and so on. Note that we intentionally avoid getting into technological solutionism, and a more accurate framing should be trying to make the best use of the space where NLP can help (Appendix F.2), and the first two bar charts are all references to help gain better insights into this.
+
+Q3. What Technical Solutions Are There? Div- ing deeper into our analysis, we study how existing research formulates tasks for each social good goal, and what methods they tend to adopt.
+
+We visualize the distribution of tasks and methods in a Sankey diagram at the bottom of Figure 1, and add the connections to the SDGs. Briefly, the Sankey diagram takes as input the SDG-task-method information identified from each of the 9K NLP4SG papers from ACL Anthology. Hence, the diagram forms a comprehensive overview of what goals are addressed by what kind of task formulations, and then solved by what kind of models. Thus, researchers can quickly gain insights into NLP4SG and get an overview of the technical landscape in one place, and then spot opportunities where a better solution is yet to be proposed, e.g., new task proposals for an under-addressed goal such as poverty, or methodological upgrades for existing tasks so that the field can benefit from the latest advancement of NLP models.
+
+The most common tasks in NLP4SG papers are: machine translation, which is present in  $6.30\%$  of the papers; classification in  $4.06\%$ ; toxicity detection in  $3.32\%$ ; named entity recognition in  $2.19\%$ ; and sentiment analysis in  $1.51\%$ . The most common methods are: LLMs present in  $12.18\%$  of the NLP4SG papers, classifiers in  $2.98\%$ , deep neural networks in  $2.51\%$ , word embeddings in  $1.80\%$ , and SVMs in  $1.62\%$ .
+
+On our Sankey diagram demo page, we also enable clicks into each goal, task, or method to see the corresponding list of papers. We show the titles and abstracts of the papers and highlight text spans that contain the tasks and methods, as illustrated in Figure 7. This function is helpful to facilitate NLP4SG literature review, and saves time for future researchers.
+
+List of NLP4SG Papers
+
+Goal: Good Health and Well-Being Task: named entity recognition
+
+1. Named Entity Recognition in Swedish Health Records with Character Based Deep Bidirectional LSTMs
+
+Goal: Good Health and Well-Being
+
+Tasks: named entity recognition
+
+Methods: character based deep bidirectional recurrent neural network
+
+We propose an approach for named entity recognition in medical data, using a character based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a  $60\%$  improvement in F 1 score over the baseline, and our system generalizes well between different datasets
+
+Figure 7: An example paper in the paper list after clicking on "Health" and "Named Entity Recognition."
+
+# 6 Related Work
+
+AI for Social Good. Recent years have seen a rising effort to apply AI technologies to various aspects of social good (e.g., Chen et al., 2020; Yu et al., 2018). To enhance the community which cares about AI for social good (Tomasev et al., 2020; Jin et al., 2021; Hager et al., 2019), it is crucial to gain insights about the progress overview. However, existing efforts mostly rely on time-consuming manual compilation, from surveying hundreds of papers (Cowls et al., 2021; Jin et al., 2021), conducting limited case studies (Floridi et al., 2020; Hager et al., 2019), to pairing existing papers on tasks and datasets with SDGs (Yeh et al., 2021). Some work uses keyword matching to extract social good-related papers (Shi et al., 2020; Fortuna et al., 2021), but our work is the first to build a comprehensive paper analysis system, and we are the only work designing a suit of various tasks, annotating a dataset, and using state-of-the-art NLP models to support such analysis.
+
+NLP on Scientific Literature. is an active research area on building NLP models for analysing scientific text. Example research directions include dataset creation (Ammar et al., 2018; Lo et al., 2020; Wang et al., 2020); modeling for tasks such as information extraction (Jain et al., 2020; Luan et al., 2018; Zhong and Chen, 2021; Hope
+
+et al., 2021), summarization (Cachola et al., 2020), question answering (Dasigi et al., 2021), and general language modeling (Beltagy et al., 2019; Taylor et al., 2022). There are notable visualization platforms e.g. for COVID-19 research papers, such as search engines to browse papers (Bhatia et al., 2020), challenges and directions (Lahav et al., 2022), and associations between research groups and topics (Hope et al., 2020). Our work has a unique focus on identifying NLP4SG papers, aligning them with SDGs, and visualizing the research landscape, and it is the first visualization platform for NLP4SG based on NLP papers.
+
+# 7 Conclusion
+
+In this work, we make possible the reporting of the state of NLP4SG, with the help of several novel tasks specific to NLP4SG papers, the construction of a richly annotated NLP4SGPAPERS dataset, and the development of a PaperAnalyzer system based on state-of-the-art LLMs. We use this system to provide researchers insights into the landscape of NLP4SG research, paving the way for a more evidence-driven pursuit of NLP4SG that goes beyond good intentions. To make this information more accessible, we also create a website to visualize our analysis.
+
+# Limitations
+
+One limitation of this work is that the models are not yet perfect, so the insights on the entire ACL Anthology are bottlenecked by the best model performance. To make up for this, we also provide in Appendix G.2 an additional analysis with only the gold, annotated data, which are accurate, but smaller and less representative of the entire landscape of NLP4SG. Since our system is modular, and we can plug in better models for each task, we welcome future work to further improve the current models. One example improvement direction is to model more inputs than the title and abstract, for instance by taking into consideration the full text and meta information such as the venue and publication year of the paper.
+
+Our sampling technique is designed to ensure the representativeness of our sample within the larger population of academic papers in our dataset, in line with our primary objective to reflect the distribution of each SDG. We acknowledge that there may be variations in the distribution of papers over
+
+the years, particularly in earlier years with limited number of papers. In future work, a more detailed analysis could be conducted to track the distribution of NLP4SG papers over time, providing a more nuanced understanding of how these papers have evolved and complementing our broader findings regarding SDG representation.
+
+Another limitation is that the definition of social good might experience a gradual shift when society evolves its ethical standards. There is no fixed, definitive answer, so we welcome community discussions. This paper is a snapshot of existing thinking frameworks (Cowls et al., 2021; Jin et al., 2021) and our understanding by 2023. Updating the definition of social good is key to mitigating the risks of overlooking important societal problems. In the long run, we suggest community-wide discussions and iterations to update the definition of social good over time.
+
+Finally, beyond the three tasks we propose for NLP4SG papers, there could be potentially other tasks, such as generating task formulations for under-addressed goals, suggesting new task-method combinations, and so on. For example, some NLP tasks that could address issues related to poverty could include sentiment analysis to understand public perceptions of poverty and related issues, information extraction from government documents to identify and track funding for poverty reduction programs, and machine translation to make information about poverty reduction programs and resources available to non-English speakers. Moreover, currently our work is limited to the knowledge within NLP, so it will be better if future work can collect data about the downstream real-world impact of each research trace, which can provide better feedback for the community.
+
+# Ethical Considerations
+
+This work aims to provide insights about the state of NLP4SG research. It helps to increase the awareness of NLP researchers about applying NLP for positive social applications. In addition, the data we use is based on the open-sourced ACL Anthology dataset, so there are no user privacy concerns. To make this line of work more inclusive and comprehensive, we suggest a regular update of the social good identifier to keep up with the ethical standards of the community.
+
+# Author Contributions
+
+The idea and mission of the paper were mainly devised by Zhijing and Rada as an important step in the NLP for Social Good initiative.
+
+Fernando put tremendous efforts into running all the models, building the demo website, and various analyses. Zhijing guided the work, and supervised the entire implementation process. Fernando and Zhijing equally contributed to the dataset collection.
+
+Mrinmaya supervised Fernando's Master thesis, from which this work extended. Mrinmaya and Bernhard gave high-level feedbacks of the project. Tom gave insightful suggestions about how to design the paper and the dataset, as well as the common NLP models to use for scientific text. Zhijing, Fernando, Mrinmaya and Rada led the paper writing, and all authors contributed to improving the writing.
+
+# Acknowledgments
+
+We thank Niklas Stoehr for his feedbacks on our earlier work (Jin et al., 2021) and his suggestion that we could further automate that framework. We appreciate Dan Lahav for his insightful discussions to improve the orientations of this paper and its role in NLP4SG initiative. We acknowledge Jad Beydoun for helping with the dataset collection process, and Flavio Schneider for deploying the basic structure of the website based on which we built the visualizations. We thank Ariel Holtzman for the brilliant brainstorming of the paper title, Laura Biester for insightful suggestions when proofreading the paper, and LIT labmates Ashkan Kazemi and Artem Abzaliev for suggestions to improve the messages that this paper convey.
+
+This material is based in part upon works supported by the Swiss National Science Foundation (Project No. 197155); a Responsible AI grant by the Haslerstiftung; an ETH Grant (ETH-1921-1); the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ:01IS18039B; the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 390727645; and by the John Templeton Foundation (grant #61156). Zhijing Jin is supported by PhD fellowships from the Future of Life Institute and Open Philanthropy.
+
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+Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. Technical report, OpenAI. 1, 2  
+Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don't know: Unanswerable questions for SQuAD. In Association for Computational Linguistics (ACL). 21  
+Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and
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+Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Empirical Methods in Natural Language Processing (EMNLP). 7  
+Paulo E. Rauber, Alexandre X. Falc  
+textasciitilde ao, and Alexandru C. Telea. 2016. Visualizing time-dependent data using dynamic t-sne. In 18th Eurographics Conference on Visualization, EuroVis 2016 - Short Papers, Groningen, The Netherlands, June 6-10, 2016, pages 73-77. Eurographics Association. 15  
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+
+# A Overview of the Demo Website
+
+We create a demo website at https:// nlp4sg.verbel.app/. Our website starts with a homepage in Figure 4, which provides access to all the analysis visualization plots made in Javascript. We introduce each visualization function in detail below.
+
+# A.1 Visualization 1: SDG Alignment
+
+We first provide a visualization in Figure 8 of the SDGs (Section 5) from three aspects: the importance scores of SDGs collected in a survey on sustainability researchers (Yang et al., 2020), NLP researchers' opinions, and the number of ACL Anthology papers addressing the goals. In addition to the static plot in Figure 6, we enable a scroll bar on the top to select the view of papers only in a certain year at https://nlp4sg.verbcl.app/sdg.
+
+![](images/4b2024cc7fc457a82e233a26c159b8b71940e3908e08123cdcbc16eeef60dd16.jpg)  
+Figure 8: Visualization of the importance of/efforts on the SDGs by the sustainability researchers, NLP researchers, and actual ACL Anthology papers.
+
+# A.2 Visualization 2: Sankey Diagram of the Research Landscape
+
+The Sankey diagram of the research landscape is shown in the bottom of Figure 1 or it can be directed accessed at https://nlp4sg. vercel.app/sankey. We include in Appendix G.1 the details of the scientific term normalization to clean the data for the Sankey diagram.
+
+# A.3 Visualization 3: Paper Browser
+
+Corresponding to the Sankey diagram, we also enable clicks into each goal, task, or method to see the corresponding list of papers, an example of which is shown in Figure 7 (https://nlp4sg.verbcl.app/sankey). We show the titles and abstracts of the papers and highlight
+
+text spans that contain the tasks and methods. This function is helpful to facilitate NLP4SG literature review, and saves time for future researchers.
+
+# A.4 Visualization 4: Supporting Organizations
+
+To understand the support structure behind the pursuit of the SDGs, we collect the organizations supporting each research paper. We ran named entity recognition models by the Stanza Python package (Qi et al., 2020) over the Acknowledgment section. In case no organizations can be identified, then we use hand-crafted rules such as taking the main organization name of the authors if it is not a research institute, as these organizations could be self-supported. We visualize the distribution of organizations in the forms of a word cloud in Figure 9 and a Sankey diagram corresponding to each SDG in Figure 10.
+
+![](images/20106c1093135643d04e7ed47cf76c79ea2881cc6097caeb77e11e62b2a4899d.jpg)  
+Figure 9: Word cloud of the organizations mentioned in the NLP4SG papers.
+
+![](images/2b104e50be4d93cc3d54b1d7e2cbc6e9dbcb23bcad3db8ddef8c8529243064a7.jpg)  
+Figure 10: Sankey diagram of the organizations supporting the NLP4SG research work on each SDG.
+
+# A.5 Visualization 5: Paper Cluster Visualization
+
+We also make a bottom-up plot of all the papers at a glance at https://nlp4sg.verbcl.
+
+app/papers. Specifically, we use the text embeddings of the papers by sentence transformer (Reimers and Gurevych, 2019), project them using t-SNE (Rauber et al., 2016), and cluster by the HDBSCAN clustering algorithm (Campello et al., 2013). A screenshot of the paper clustering visualization page is in Figure 11.
+
+![](images/86b98f80d8dff9b55abfbe10d72487aeaa6e8c4cdb003519f2e2e05e32aa474e.jpg)  
+Figure 11: Clustering of the NLP4SG papers.
+
+# B Data Collection Details
+
+# B.1 Data Source
+
+We identify ACL Anthology as a quality source of data, since it contains a large set of peer-reviewed NLP papers. There are other potential sources of peer-reviewed NLP papers, such as papers submitted to the NLP track at other venues such as NeurIPS and ICLR; as well as non-peer-reviewed papers on arXiv under the category cs.CL. However, for the former, it is hard to algorithmically identify NLP papers with high precision and recall, not to mention the different copyright regulations; for the latter, it is hard to control the paper quality if we open to non-peer-reviewed data. After balancing all the concerns and also thinking of our target audience, the NLP community, we think ACL Anthology, which is peer-reviewed and maintained by the NLP community, is a good data source for our work.
+
+# B.2 Preprocessing
+
+We conduct the following preprocessing on the ACL Anthology data. To identify the paper entries in the entire anthology, we remove those that are workshop proceedings, invited talks, and book reviews, by a set of hand-crafted rules, such as identifying the title starting with "Book Reviews: ..."
+
+or ending with "... Workshop." To clean the titles, we remove the special symbols "\{}" and "\}" in titles such as "\{N\}o{P}ropaganda at \{S\}em{E}\val-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification."
+
+Moreover, not all entries on the anthology provide a non-empty abstract, so for papers with missing abstracts, we parse their PDFs using the PDF-to-JSON converter doc2json Python package<sup>7</sup> to extract the abstracts.
+
+# B.3 Annotation Scheme
+
+We introduce the annotation scheme for the three tasks below.
+
+# B.3.1 Annotating Task 1: NLP4SG Classification
+
+Inspired by previous frameworks defining NLP4SG (Jin et al., 2021) and AI for social good (Cowls et al., 2021), we design a decision flowchart to identify whether an NLP paper addresses a social good-related problem in Figure 12.
+
+![](images/bbdbc2d7a7693beec8dced51f577d099fc9db6b700db3091e9761221e9f9b61b.jpg)  
+Figure 12: A decision flowchart used for the annotation of Task 1, NLP4SG binary classification.
+
+Specifically, there are two important decision criteria. One is about the relevance to social good topics, e.g., the 17 UN SDGs. We also include social problems in the digital era, such as online toxicity, fake news, and privacy, into their corresponding SDG, i.e., (Goal 16) Peace, justice and strong institutions. The second criterion is about how directly the paper addresses the social problem. For
+
+example, general linguistic tools such as syntax parsing could be an important technology, but not carrying direct social implications. We adopt the categorization of the stage of a technology in Jin et al. (2021), and only count those with a clear implication for downstream applications (i.e., Stage 3 and 4).
+
+To get the idea through to the annotators, we provide both the formal guideline above, and also concrete examples as follows.
+
+What is included: (Example set 1) Directly related to the high-level definition of SDGs, mentioning, e.g., healthcare; mental health (psycho-counseling, hope speech); education; facilitating efficient scientific research (which belongs to Goal 9 Industry, innovation and infrastructure); helping employment (job matching, training job skills); helping collaboration among decision-makers. (Example set 2) Related to the fine-grained subcategories of SDGs, e.g., encouraging civic engagement, and enabling social problem tracking for the goal of (Goal 16) Peace, justice and strong institutions. (Example set 3) Social problems in the digital era: e.g., online toxicity, misinformation, privacy protection, and deception detection.
+
+What is excluded: (Example set 1) General-purpose, coarse-grained NLP tasks: machine translation, language modeling, summarization, sentiment analysis, etc. (Example set 2) General-purpose, fine-grained NLP tasks: news classification; humor detection; technologies to increasing productivity, e.g., email classification, report generation, meeting note compilation (because they are application-agnostic which could be used for both good and bad purposes, and also a bit too general); textbook-related QA but using it as a benchmark to improve the general modeling capabilities; tasks whose data is socially relevant, but the task is neutral (e.g., POS tagging for parliament speech); NLP to help other neutral disciplines, e.g., chemistry; tasks a bit too indirectly related to SDGs, e.g., parsing historical language document, or cultural heritage-related tasks, low-resource MT, which bridges resources from one community to another, but is a bit too indirect, and also depends case by case on the actual language community, plus there is a tradeoff between efficiency and equality. (Example set 3) Tasks with controversial nature or unknown effect (varying a
+
+lot by how people use them in the future): e.g., news comment generation; financialNLP, which could be used in either way to help the economy, or perturb the market for private profits; simulated NLP tools for the battlefield; user-level demographic prediction.
+
+As mentioned in the Limitations and Ethical Implications sections, the definition of social good might experience a gradual shift when society evolves its ethical standards. There is no fixed, definitive answer, so we start with relatively inclusive criteria here, and welcome community discussions and iterations to improve the definitions. This paper is a snapshot of existing thinking frameworks (Cowls et al., 2021; Jin et al., 2021) and our understanding by 2023. In the long run, we suggest community-wide discussions and iterations to update the definition of social good from time to time.
+
+# B.3.2 Annotating Task 2: SDG Classification
+
+We let the annotators read the official descriptions and indicators of the 17 SDGs on the UN website such as https://sdgs.un.org/goals/goal1. For each paper identified as NLP4SG in Task 1, we ask the annotators to label all relevant SDGs.
+
+We present examples of NLP4SG paper titles corresponding to each SDG below.
+
+# G2. Hunger
+
+A Gold Standard for CLIR evaluation in the Organic Agriculture Domain  
+- CRITTER: a translation system for agricultural market reports
+
+# G3. Health
+
+- A Treebank for the Healthcare Domain  
+Automatic Analysis of Patient History Episodes in Bulgarian Hospital Discharge Letters
+
+# G4. Education
+
+- An MT learning environment for computational linguistics students  
+- Salinlahi III: An Intelligent Tutoring System for Filipino Heritage Language Learners
+
+# G5. Gender
+
+- An Annotated Corpus for Sexism Detection in French Tweets  
+- Mitigating Gender Bias in Machine Translation with Target Gender Annotations
+
+# G6. Water
+
+- A conceptual ontology in the water domain of knowledge to bridge the lexical semantics of stratified discursive strata
+
+# G8. Economy
+
+- Multilingual Generation and Summarization of Job Adverts: the TREE Project  
+- Situational Language Training for Hotel Receptionists
+
+# G9. Innovation
+
+- An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols  
+- Retrieval of Research-level Mathematical Information Needs: A Test Collection and Technical Terminology Experiment
+
+# G10. Inequalities
+
+- Analyzing Stereotypes in Generative Text Inference Tasks  
+- Recognition of Static Features in Sign Language Using Key-Points
+
+# G11. Sustainable Cities
+
+- FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events  
+- Trouble on the Road: Finding Reasons for Commuter Stress from Tweets
+
+# G12. Consumption
+
+- Multiple Teacher Distillation for Robust and Greener Models
+
+# G13. Climate
+
+- CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims  
+- Tackling Climate Change with Machine Learning
+
+# G14. Life Below Water
+
+- Marine Variable Linker: Exploring Relations between Changing Variables in Marine Science Literature  
+- Literature-based discovery for Oceanographic climate science
+
+# G16. Peace
+
+- On Unifying Misinformation Detection  
+- Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter
+
+Since Goal 16 is a big category including tasks such as toxicity detection, and fighting misinformation, we provide an additional label when a paper within Peace addresses more fine-grained tasks. We collect statistics for the main subcategories: 44 samples  $(23.28\%)$  for toxicity detection, 44 samples  $(23.28\%)$  for fighting misinformation, 11 samples  $(5.82\%)$  for privacy protection, and 6 samples  $(3.17\%)$  for deception detection.
+
+# G17. Partnership
+
+- MEDAR: Collaboration between European and Mediterranean Arabic Partners to Support the Development of Language Technology for Arabic  
+- The Telling Tail: Signals of Success in Electronic Negotiation Texts
+
+# B.3.3 Annotating Additional Data for Task 2: Upsampling
+
+To enrich the data for Task 2, we upsample the low-occurrence classes. We denote all the classes with less than  $5\%$  proportion in our dataset as the low-occurrence classes. We first automatically extract candidate papers that might correspond to the low-occurrence classes, and then manually annotate their SDG classes. To automatically identify a candidate pool of papers regarding the low-occurrence classes, we first run the best model of Task 1 on the entire ACL Anthology to extract 9K NLP4SG papers, and among them, we use our curated keywords corresponding to the SDGs to extract candidate papers.
+
+In Table 7, we can see that by the automatic matching algorithm, we get 261 candidate papers in total, including 1 candidate paper for poverty, 25 for hunger, 67 for gender, ..., and 40 for partnership. And after manually annotating these 261 papers, we obtain 4 confirmed papers for hunger, 67 for gender, 1 for water, 44 for economy, ..., and 5 for partnership. We use this upsampled data as additional data for Task 2, resulting in 759 total NLP4SG papers for Task 2.
+
+<table><tr><td>UN SDG</td><td>#Matched Papers</td><td>#Papers after Annotation</td></tr><tr><td>G1. Poverty</td><td>1</td><td>0</td></tr><tr><td>G2. Hunger</td><td>25</td><td>4</td></tr><tr><td>G3. Health</td><td>High Occurrence</td><td>0</td></tr><tr><td>G4. Education</td><td>High Occurrence</td><td>0</td></tr><tr><td>G5. Gender</td><td>67</td><td>67</td></tr><tr><td>G6. Water</td><td>3</td><td>1</td></tr><tr><td>G7. Energy</td><td>2</td><td>0</td></tr><tr><td>G8. Economy</td><td>67</td><td>44</td></tr><tr><td>G9. Innovation</td><td>High Occurrence</td><td>15</td></tr><tr><td>G10. Inequalities</td><td>High Occurrence</td><td>1</td></tr><tr><td>G11. Sustainable Cities</td><td>33</td><td>11</td></tr><tr><td>G12. Consumption</td><td>0</td><td>0</td></tr><tr><td>G13. Climate</td><td>21</td><td>19</td></tr><tr><td>G14. Life Below Water</td><td>2</td><td>2</td></tr><tr><td>G15. Life on Land</td><td>0</td><td>0</td></tr><tr><td>G16. Peace</td><td>High Occurrence</td><td>12</td></tr><tr><td>G17. Partnership</td><td>40</td><td>5</td></tr><tr><td>Total</td><td>261</td><td>167</td></tr></table>
+
+Table 7: Number of upsampled papers by SDGs. We first identify the candidate papers (# Matched Papers), and then manually annotate them (# Papers after Annotation). In total, there are 261 candidate papers, from which 167 are identified as relevant for NLP4SG.
+
+# B.3.4 Annotating Task 3: Salient Scientific Term Analysis
+
+We present the title and abstract of social good papers to the annotator and ask them to write down a list of the primary tasks and methods addressed by each paper. In case there are hyponymy and hypernymy relationships among the scientific terms, we take the more specific term that is still common enough to show up in the Sankey diagram. For example, we would use the term LSTMs, but not neural networks (too broad), or stacked BiLSTMs with co-attention (too specific to be frequent enough to show up on the Sankey diagram). We utilized this information to enable the annotators to choose the relevant task and method spans from the title and abstract, along with an extra text box as an option in case an exact span was not available.
+
+Below are the list of some common tasks and methods.
+
+List of common tasks: automatic speech recognition, COVID-19-related analysis, text classification, data collection, event extraction, fact checking, fake news detection, gender bias mitigation, information extraction, information retrieval, linguistic analysis, machine translation, NLP applications, named entity recognition, natural language generation, question answering, relation extraction, rumor detection, sentiment analysis, stance detection, text summarization, toxicity detection, etc.
+
+List of common methods: BERT, classifiers, computational models, conditional random fields, convolutional neural networks, ensemble methods, LSTM, multi-task learning, RoBERTa, supervised learning, support vector machines, topic models, transfer learning, word embeddings, etc. In the absence of more specific methods, it is also acceptable to annotate more coarse-grained methods if they are the only ones mentioned in the paper: language models, language technology, machine learning methods, machine translation system, NLP models, neural networks, recurrent neural networks, transformers, etc.
+
+Title: Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections Abstract: Detecting healthcare-associated infections poses a major challenge in healthcare. Using natural language processing and machine learning applied on electronic patient records is one approach that has been shown to work. However the results indicate that there was room for improvement and therefore we have applied deep learning methods. Specifically we implemented a network of stacked sparse autoencoders and a network of stacked restricted Boltzmann machines. Our best results were obtained using the stacked restricted Boltzmann machines with a precision of 0.79 and a recall of 0.88.
+
+Task: predict healthcare-associated infections
+
+Method: restricted Boltzmann machines, autoencoders
+
+Table 8: Example annotation of Task 3.
+
+# B.4 Annotator Background
+
+The data associated with each task was annotated by two NLP researchers who are proficient in English. There are both female and male researchers, as well as white and Asian demographic backgrounds.
+
+# B.5 Inter-Annotator Agreement
+
+For Task 2, we computed the Cohen's Kappa score of the binary classification of each SDG as shown in Table 9, and calculate the weighted average as  $88.67\%$ . Moreover, the partial match score is  $96.46\%$  and the exact match is  $85.35\%$ .
+
+# C More Dataset Information
+
+# C.1 License
+
+License or terms for use and/or distribution: The dataset is open-sourced with the Creative Commons Attribution Share Alike 4.0 license. We provide the ACL Anthology paper IDs, title, abstract, publication year, and acknowledgements section from ACL Anthology, with our annotations. Ma
+
+<table><tr><td>UN SDG</td><td>Cohen&#x27;s kappa</td></tr><tr><td>G1. Poverty</td><td>-</td></tr><tr><td>G2. Hunger</td><td>-</td></tr><tr><td>G3. Health</td><td>92.48%</td></tr><tr><td>G4. Education</td><td>95.19%</td></tr><tr><td>G5. Gender</td><td>86.32%</td></tr><tr><td>G6. Water</td><td>-</td></tr><tr><td>G7. Energy</td><td>-</td></tr><tr><td>G8. Economy</td><td>73.49%</td></tr><tr><td>G9. Innovation</td><td>84.92%</td></tr><tr><td>G10. Inequalities</td><td>68.96%</td></tr><tr><td>G11. Sustainable Cities</td><td>33.15%</td></tr><tr><td>G12. Consumption</td><td>100.00%</td></tr><tr><td>G13. Climate</td><td>100.00%</td></tr><tr><td>G14. Life Below Water</td><td>-</td></tr><tr><td>G15. Life on Land</td><td>-</td></tr><tr><td>G16. Peace</td><td>89.63%</td></tr><tr><td>G17. Partnership</td><td>79.31%</td></tr></table>
+
+Table 9: Cohen's kappa coefficients for each SDG.
+
+terials prior to 2016 in the ACL Anthology are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License, and materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
+
+# C.2 NLP4SG through the years
+
+While papers addressing social problems in NLP have been predominantly published in recent years, it's important to include older papers in our analysis to get a complete picture of the growth of NLP4SG. Some examples of NLPSG papers for every decade are:
+
+1960s: Automated Processing of Medical English. https://aclanthology.org/C69-1101  
+1970s: Powerful ideas in computational linguistics - Implications for problem solving, and education. https://aclanthology.org/P79-1028.pdf  
+1980s: Temporal Inferences in Medical Texts. https://aclanthology.org/P85-1002  
+1990s: ANTHEM: advanced natural language interface for multilingual text generation in healthcare (LRE 62-007). https://aclanthology.org/1996. amta-1.27  
+2000s: Collaborative Annotation of Sign Language Data with Peer-to-Peer Technology. https://aclanthology.org/L04-1280/  
+2010s: Detecting Offensive Tweets in Hindi
+
+English Code-Switched Language. https://aclanthology.org/W18-3504/
+
+2020s: Gender Bias in Machine Translation. https://aclanthology.org/2021.tacl-1.51/
+
+# C.3 Task 2 Co-Occurrence Matrix
+
+In Figure 13, we show the co-occurrence matrix with the absolute number of co-occurrences.
+
+![](images/f5337ba1c5b7a70f62b973ce4b6f3ad30458732a42c6e0bee872e4b62fd69f7b.jpg)  
+Figure 13: Heatmap of co-occurrences of UN SDGs on NLP4SG papers.
+
+# D Experimental Details
+
+# D.1 Models for Task 1
+
+# D.1.1 Distant Supervision
+
+For the keyword matching, we curate a list of social good-related keywords provided in our GitHub repository which extends the AI for social good keywords by Shi et al. (2020). We compare the performance of keyword matching based on the title vs. title and abstract in Table 10, and find that keyword matching in titles gives higher performance, so we adopt the title keyword matching as the first DS method.
+
+For the second DS method, cosine similarity matching, we calculate the cosine similarity scores between the text embeddings of the title + abstract of the paper and the text embeddings of the description of each one of the UN SDGs and take the most similar UN SDG. We add a paper to the positive set if its text similarity is among the top  $1\%$  most similar papers in the unlabeled data from the ACL Anthology. We add a paper to the negative set if its similarity is in the bottom
+
+$50\%$  percentile. For the text embedding similarity, we use a general-purpose sentence similarity model, all-distilroberta-v1 which is a pre-trained distilroberta-base (Sanh et al., 2019) model finetuned on a 1B sentence pairs dataset.
+
+Merging both DS sets, the entire DS set consists of positive samples from papers whose titles match the keywords, and papers which are very similar to the UN SDG descriptions; and negative samples from papers that are very different from the UN SDG descriptions.
+
+We report the quality of data extracted by these DS methods evaluated on the training set in Table 10, which correlates with the ablation study results in Table 11. We can see that the performance of the entire DS set is higher than that of the keyword-only set, which is higher than the similarity-only set.
+
+<table><tr><td></td><td>F1</td><td>P</td><td>R</td></tr><tr><td>Keyword Match (Title)</td><td>66.67</td><td>79.90</td><td>57.19</td></tr><tr><td>Keyword Match (Title+Abstract)</td><td>55.17</td><td>42.50</td><td>78.60</td></tr><tr><td>Cosine Similarity</td><td>8.33</td><td>47.06</td><td>4.57</td></tr><tr><td>Keyword Match (Title) + Cosine Similarity</td><td>72.02</td><td>76.85</td><td>67.76</td></tr></table>
+
+Table 10: Quality evaluation of the distant supervision heuristics reported on the training set.  
+
+<table><tr><td></td><td>F1</td><td>Acc</td><td>P</td><td>R</td></tr><tr><td>SciBERT with No DS</td><td>73.42</td><td>94.10</td><td>84.90</td><td>64.68</td></tr><tr><td>SciBERT + DS by Similarity</td><td>62.96</td><td>89.35</td><td>55.86</td><td>72.11</td></tr><tr><td>SciBERT + DS by Keyword</td><td>74.68</td><td>94.10</td><td>80.93</td><td>69.32</td></tr></table>
+
+Table 11: For the best-performing model, SciBERT+DS, we conduct ablation studies by removing the different subsets of our DS data. The resulting performance correlates with the DS data quality reported in Table 10, where keyword + similarity gets the best quality, and similarity only gets a very low quality.
+
+# D.1.2 Models Implementation Details
+
+BERT, RoBERTa, and SciBERT We finetune SciBERT (Beltagy et al., 2019) on the NLP4SG text classification task. We freeze the embedding layer and the first 11 encoder layers, and we finetune the parameters of the 12th encoder layer, the pooled, and the classifier layer.
+
+We use one GPU model NVIDIA Quadro RTX 6000 with 24 GiB. We train the classifier for 15 epochs using a batch size of 32. The train
+
+ing time for our best-performing model is approximately 1.5 hours. We manually grid search with different learning rates (5e-03, 5e-04, 5e-05, 5e-06) and different scheduler types ("constant", "linear", "cosine with restarts") to make a total of 12 runs and chose the best hyperparameters based on f1 score on the dev set. The chosen learning rate is 5e-05 and a linear scheduler. All the runs together make a total of around 18 GPU hours. We tried with a similar setting using BERT-large and RoBERTa-large, obtaining lower performances.
+
+InstructGPT For the second approach, we design a prompt and ask InstructGPT in a zero-shot setting if a paper is NLP4SG or not. The prompt contains 3 elements:
+
+- Introduction to the task: We use the sentence "There is an NLP paper with the title and abstract:"  
+- Content of the paper: The title and abstract concatenated  
+- Final question: We use the sentence "Is this paper contributing to the UN Sustainable Development Goals? Answer yes or no."
+
+We use the OpenAI  $\mathrm{API}^9$  to access GPT. We use "davinci-text-002." We keep the default values of the API, and set the temperature to zero and maximum number of tokens of the response as 50.
+
+Few-shot InstructGPT We also perform experiments using few-shot settings, i.e., 2,4,8,16, and 32 shots, and report the results for the setting with the best performance in the dev set. For those experiments, we only use the title of the paper and the same proportion of positive and negative examples.
+
+Are these papers contributing to the UN Sustainable Development Goals or helping society?
+
+Paper: [Title + Abstract]
+
+Answer: [Yes/No]
+
+Paper: [Title + Abstract]
+
+Answer: [Yes/No]
+
+Paper: [Title + Abstract]
+
+Answer:
+
+[InstructGPT response]
+
+# D.2 Models for Task 2
+
+Model 1: InstructGPT We design a prompt to get which UN goal a paper contributes to. Since InstructGPT already has background knowledge of which are the UN SDGs, and what is the purpose of each one of them we do not need to include that explanation in the prompt. The prompt has the following components:
+
+- Introduction to the task: We use the sentence "There is an NLP paper with the title and abstract:"  
+- Content of the paper: The title and abstract concatenated.  
+- Final question: We use the sentence "Which of the UN goals does this paper directly contribute to? Provide the goal number and name."
+
+We parse the response, allowing multiple goals for each paper.
+
+Model 2: Pretrained MNLI classifier We use a pretrained MNLI classifier (bart-large-mnli) as a zero-shot text classifier, an approach proposed by Yin et al. (2019). We get the probability of entailment/contradiction of the content of the paper with the description of each one of the UN goals obtained from the UN website. We consider that a paper contributes to a UN SDG if the probability of entailment is 0.5 or higher. If there is no SDG with a probability higher than 0.5 we take the SDG with the highest probability. We also tested distilbert (typeform/distilbert-base-uncased-mnli) and DeBERTa (MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli).
+
+# D.3 Models for Task 3
+
+PURE and SciREX PURE (Zhong and Chen, 2021) is a model for end-to-end relation extraction. We use PURE trained on the SciERC (Luan et al., 2018) dataset of scientific abstracts.
+
+SciREX (Jain et al., 2020) is a document level IE model that not only identifies mentions, but also models their saliency, and their coreference links.
+
+For SciREX and PURE, we run them on a GPU model NVIDIA Quadro RTX 6000 with 24 GiB. It takes around one hour to extract the information from 9K social good papers.
+
+BERT-based Models We used LLM finetunned on the SQUAD dataset (Rajpurkar et al., 2018). We provide as context the title and abstract of the paper and asked "Which NLP task does this paper address?" and "Which NLP method(s) does this paper use?". In particular we used the following models "bert-large-uncased-whole-word-masking-finetuned-squad", "deepset/roberta-large-squad2", and "deepset/deberta-v3-large-squad2".
+
+InstructGPT We use InstructGPT in a zero-shot setting in order to extract the relevant information. We use a different prompt for each of the 2 extraction tasks (tasks and methods). The prompt consists of 3 elements:
+
+- Instruction: Here we tell InstructGPT what to do in natural language form.  
+- Paper content: We concatenate the title and abstract of the paper.  
+- Final question: We present the question again to reinforce the explanation.
+
+# Prompt to obtain the tasks
+
+Identify the NLP task(s) that this paper is dealing with. Select a text span that is an appropriate answer, or if no span serves as a good answer, just come up with a phrase. Examples of tasks are: fake news detection, name entity recognition, question answering, etc.
+
+$$
+[ T i t l e + A b s t r a c t ]
+$$
+
+The primary NLP task(s) addressed in this paper are:
+
+$$
+[ \text {I n s t r u c t G P T}
+$$
+
+# Prompt to obtain the methods
+
+Identify the NLP method(s) used in this paper. Select a text span that is an appropriate answer, or if no span serves as a good answer, just come up with a phrase. Separate the methods with commas and don't include NLP tasks. Examples of methods are: BERT, SVM, CNN, etc.
+
+$$
+[ T i t l e + A b s t r a c t ]
+$$
+
+The primary NLP method(s) used in this paper are:
+
+# E Details of the Interpretability Analysis
+
+# E.1 Task 1: Example LIME Outputs
+
+Using the best model, we show examples of LIME output for true positive (TP), true negative (TN), false positive (FP), and false negative (FN) data samples in Figures 14 to 17, respectively.
+
+![](images/5cac9d71276dff70c09b2c51b7047c6784d982fe721a22b112a43abbb8b31ba7.jpg)  
+Figure 14: LIME output for a TP example.
+
+![](images/4a61af19c57d1e463ae6bb9959fc226d48fa0d715d33771be2f2cf6f40534db1.jpg)  
+Figure 15: LIME output for a TN example.
+
+![](images/be0f7ce6dde1cf33bf764d9834e3398e1c7833545ad166fba8d1e78aa580ad51.jpg)  
+Figure 16: LIME output for an FP example.
+
+# E.2 Task 2: Analyzing the Reasoning Chain of InstructGPT
+
+For the correctly classified samples by the best InstructGPT method, we perform further analysis to check the explanation quality of the model.
+
+We show two motivating examples in Table 12, where the first example shows sufficient intermediate reasoning steps starting from what the paper contributes, to intermediate effects of the paper, and finally to the end social impact, while the second example lacks intermediate reasoning steps, and mentions only what the paper does, followed the UN SDG right after, without intermediate explanations.
+
+Specifically, we denote a good "impact chain" as a complete and cohesive reasoning chain from the direct description of the paper's research output
+
+![](images/e66421894d82db8987cc069957729eec9a329b8cf4866c4b71ac87b0be39bed4.jpg)  
+Figure 17: LIME output for an FN example.
+
+to intermediate impact, and end impact. Then we randomly select 200 test samples, and use the following two criteria to check the explanation quality of InstrutGPT: (1) whether the mentioned "impact chain(s)" sound cohesive and reasonable, e.g., not missing necessary intermediate reasoning steps, which is analogous to the precision score, and (2) whether there are other obvious impact chains that are missing, which is analogous to the recall score. For each criterion, we assign 1 for good performance, 0 for bad performance, and 0.5 for partially good performance.
+
+We find that  $77.6\%$  of the explanations contain cohesive and convincing reasoning chains, and  $18\%$  of the explanations miss at least one obvious impact chain of how the paper contributes to UN SDGs.
+
+# SDG classification explanation
+
+There is an NLP paper with the title and abstract:
+
+[Title + Abstract]
+
+Is this paper contributing to the UN Sustainable Development Goals?
+
+Answer yes or no. If the answer is "yes", mention which goal the paper is contributing to and in which way it is contributing to that goal.
+
+[InstructGPT response]
+
+# F More Insights
+
+# F.1 Survey
+
+We conduct a survey from July 31, 2021 to August 31, 2021 about "How current NLP researchers think about NLP4SG." We receive 80 responses from NLP researchers, among which more than  $30\%$  are NLP professors. We reach the subjects at the ACL conference and also through emails. The demographics of the survey subjects are shown in Figure 18.
+
+# An Example with Sufficient Reasoning Steps for the Impact Chain
+
+Title: Extracting Symptoms and their Status from Clinical Conversations
+
+Abstract: This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. [...] We propose two novel deep learning approaches to infer the symptom names and their status: [...] This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations. [...]
+
+InstructGPT Response: Yes, this paper is contributing to the UN Sustainable Development Goal of Good Health and Well-Being. The paper describes models that are designed to extract symptoms from clinical conversations, which can assist medical providers in capturing symptoms mentioned by patients. This can ultimately lead to better diagnosis and treatment of diseases, and improve overall health and well-being.
+
+# An Example Lacking Intermediate Reasoning Steps for the Impact Chain
+
+Title: Extracting Symptoms and their Status from Clinical Conversations
+
+Abstract: Lexically-Triggered Hidden Markov Models for Clinical Document Coding. The automatic coding of clinical documents is an important task for today's healthcare providers. Though it can be viewed as multi-label document classification, the coding problem has the interesting property that most code assignments can be supported by a single phrase found in the input document. We propose a Lexically-Triggered Hidden Markov Model (LT-HMM) that leverages these phrases to improve coding accuracy. [...]
+
+InstructGPT Response: Yes, this paper is contributing to the UN Sustainable Development Goal of good health and well-being. The paper is doing this by proposing a Lexically-Triggered Hidden Markov Model (LT-HMM) that leverages phrases to improve coding accuracy for clinical documents. This will ultimately help healthcare providers provide better care to patients.
+
+Table 12: Examples of sufficient and insufficient reasoning about the impact chain of NLP4SG papers.
+
+The question in the survey that contributes to the SDG visualization is "What social good applications do you think the NLP community should consider?", for which we manually parse the open-ended answers to corresponding SDGs. Example answers are NLP for Education (22.9%), physical and mental health (18.6%), and combating misinformation (17.1%).
+
+# F.2 Ideal SDG alignment we want to push for
+
+We show an ideal framework in Figure 19, where for each goal, NLP can help with a subset of the things. And the meaningful action space is the gap between the current NLP research landscape vs. what NLP can potentially do.
+
+![](images/94aa232cddac37b29edd3d7cb610e7c778621256f3c7f85051d64b68028fa659.jpg)  
+Background - Experience in NLP research
+
+![](images/8072b92d9ff52ce065bae0dee0842b694a2e5a29861aa7cec2af276a6407e4e6.jpg)  
+Background - What career stage(s) are you in?
+
+![](images/0896ae1cb0d8afe160ddd4def1c919e21d533eed9d1468a3e26e9f164896c173.jpg)  
+Background - Main research area(s)  
+Figure 18: Backgrounds of the survey subjects, including the number of years in NLP research, career stage, and research areas.
+
+# G More Website Details
+
+# G.1 Implementation Details
+
+Although we run the PaperAnalyzer on the entire ACL Anthology. For visualization, we mostly visualize 1980 to 2021, because there were not many papers before 1980, and also the progress tracking of NLP4SG in the recent decades might be more relevant for the audience. Also, since our data was crawled in the middle of 2022, the trend of 2022 is not comparable to the previous year 2021, so we omit 2022 for the moment, but it could be added in the future.
+
+For the visualization of the Sankey diagram, we conduct some preprocessing over the salient scientific terms produced by the model. Since there are variances in the expression of the same task or method, such as "long short-term memory networks," and "LSTMs", we group similar terms to
+
+![](images/653c7138093e475c78b42589ebf93b50bb4dc1020c5651084755290db6ad9477.jpg)  
+Figure 19: Illustration of the action space NLP researchers can aim for.
+
+![](images/d82053e277550c966597ae9dae26dc1c8838dafc7f38da5e1e15e53597330041.jpg)
+
+gether with the following procedure:
+
+- We get a vector representation for each word with Word2Vec (Mikolov et al., 2013). We train Word2Vec with the abstracts of all the ACL Anthology papers, we set the parameter window to 10 and vector size 100.  
+- We use the agglomerative clustering function in scikit-learn (Pedregosa et al., 2011) on the word vectors, with the parameter distance threshold set to 0.2.  
+- We measure the ratio score similarity between each word of a group to its centroid and just keep words in the cluster that have a similarity score greater than 75. We used the library "TheFuzz"10 which uses Levenshtein distance to compute its similarity measure.  
+- We take that automatic clustering as a starting point and then manually check each one of the clusters, add new elements and create new clusters if necessary.
+
+# G.2 Demo on the Gold Data
+
+As mentioned in the Limitations section, one constraint is that the visualizations are bottle-necked by the model performance. As a supplementary, we also provide the visualization based on our annotated NLP4SGPAPERS dataset, which is smaller but accurate.
+
+We produce all the visualization figures mentioned in Section 5, but based on the ground-truth annotation in our NLP4SGPAPERS dataset. Figure 20 visualizes the proportion of NLP4SG papers over the years, where the proportion of NLP4SG papers steadily increased from 2000.
+
+We also show the Sankey diagram, using the gold data only, in Figure 21 and the SDG visualization in Figure 22.
+
+![](images/a291586d921ee21ee8ba2aa8521b51bc165e306f4fa3f4d301c36e9c64fea276.jpg)  
+Figure 20: Percentage of social good-related papers (top) and the total number of papers (bottom) in NLP4SGPAPERS each year from 1980.
+
+![](images/0bebc3209d4290b429db3ba83c545e545730452be3a0e67ee43655c64445b38b.jpg)  
+Figure 21: Sankey diagram based on gold annotations at https://nlp4sg.verbel.app/sankey_gold.
+
+![](images/18a7ceb38107045c494c728d4317d2de290a828ae222620493f2f128586ad33b.jpg)  
+Figure 22: SDG visualization based on gold annotations at https://nlp4sg.verbel.app/sdg_gold.

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+# Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
+
+Bingsheng Yao
+
+Rensselaer Polytechnic Institute
+
+Ishan Jindal
+
+IBM Research
+
+Lucian Popa
+
+IBM Research
+
+Yannis Katsis
+
+IBM Research
+
+Sayan Ghosh
+
+UNC Chapel Hill
+
+Lihong He
+
+IBM Research
+
+Yuxuan Lu
+
+Northeastern University
+
+Shashank Srivastava
+
+UNC Chapel Hill
+
+Yunyao Li
+
+Apple
+
+# James Hendler
+
+Rensselaer Polytechnic Institute
+
+# Dakuo Wang
+
+Northeastern University
+
+# Abstract
+
+Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts' real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.
+
+# 1 Introduction
+
+State-of-the-art (SoTA) language models (Devlin et al., 2019; Radford et al., 2019; Winata et al.,
+
+![](images/5850c65a5e1ebe02875f368d5a98455f3ac80198f440a0d8fc2b2fa0dab9b039.jpg)  
+Figure 1: Our dual-model AL system architecture at every iteration: 1) the AL data selector chooses a few unlabeled examples; 2) human annotators provide an explanation and label for each data instance; 3) the annotated explanations are used to finetune the explanation-generation model; 4) the annotated labels and generated explanations are used to finetune the prediction model. Then, humans can review the predicted labels and generated explanations for unlabeled data and start the next iteration. Green arrows indicate the training target.
+
+2021) demonstrate astonishing performance on various NLP tasks, including Question Answering (QA) and Question Generation (QG) (Rajpurkar et al., 2016; Duan et al., 2017; Kočisky et al., 2018; Yao et al., 2022), Natural Language Inference (NLI) (Bowman et al., 2015; Wang et al.,
+
+2018), etc. Despite the superior generative capabilities, the lack of faithful explainability within these "black boxes" may lead to mistrust of their predictions (Lipton, 2018), where humans, on the other hand, can develop intermediate rationales to facilitate the decision-making process.
+
+The lack of explainability and untrustworthiness of models is magnified in the real world (Drozdal et al., 2020), where domain experts rarely only annotate a decision label in their daily workflow without providing explanations (i.e., clinical diagnoses by clinicians) (Zhang et al., 2023), and humans need explanations to understand and trust model predictions (Zhang et al., 2021). Therefore, a few approaches were proposed to retrospectively analyze the probability distribution within the model or ask models to generate explanations along with predictions (Ribeiro et al., 2016; Lundberg and Lee, 2017; Yu et al., 2019; Rajagopal et al., 2021; Chen et al., 2021), despite, the former is still very difficult for laymen to understand while the latter explanations are not faithful toward predictions.
+
+As researchers looked into the quality (Carton et al., 2020; Yao et al., 2023) of human-annotated natural language explanations (Camburu et al., 2018; Rajani et al., 2019; Aggarwal et al., 2021), they discovered numerous issues in existing datasets (Geva et al., 2019; Chmielewski and Kucker, 2020; Narang et al., 2020; Sun et al., 2022), such that the human annotations are of low quality and significant inconsistency. Furthermore, the ever-increasing costs in terms of labor, finances, and time for large-scale, high-quality data annotations remain a persistent challenge for the research community. This challenge has given rise to various methodologies to reduce reliance on human annotations, such as Active Learning (AL) (Settles, 2009). AL is a human-in-the-loop framework that utilizes AL sampling strategies to iteratively select a small number of representative examples, request oracle annotations, and subsequently fine-tune the model using the annotated data. However, prior AL works predominantly focus on labels and overlook the fact that real-world scenarios often need both labels and natural language explanations.
+
+In this work, we propose a dual-model AL architecture for human annotation of labels and explanations, drawing inspiration from the human decision-making process. Our system consists of:
+
+1) An explanation-generation model guided by human-provided explanations
+
+2) A prediction model that accepts the data content and the generated explanations for prediction.
+
+We integrate AL to reduce human annotation efforts and establish human trustworthiness by actively engaging humans in the training process. We design a novel data diversity-based AL sampling strategy to select the most representative examples by exploiting the explanation annotations, which is analogous to the prevalent core-set (Sener and Savarese, 2017) strategy. Our AL architecture aims to support low-resource model predictions and AI trustworthiness by explicitly generating natural language explanations. Specifically, we request label and free-form explanation annotations for a very limited number of examples (e.g., 3 or 10) selected by our AL sampling strategy at every AL iteration. Subsequently, the generated explanations serve as input for the final prediction, demonstrating the potential for these explanations to support the model's predictions faithfully.
+
+We conduct two AL simulations with different amounts of samplings and iterations on a large-scale NLI dataset with human-annotated explanations to justify incorporating explanations in AL data selection can consistently outperform random, traditional data diversity-based, and model probability-based sampling strategies. We make the code publicly available<sup>1</sup>.
+
+A human evaluation of perceived validity, explainability, and preference of the generated explanations among our system, a SoTA explanation-generation system, and human-annotated explanations shows that, despite human explanations being ranked highest, explanations generated by our system are preferred over the SOTA system. Additionally, we conduct three ablation studies to explore the capability and potential of our proposed AL architecture in transfer learning, generalizability, and incorporating large language models (LLMs) for explanation generation to further reduce human efforts. LLMs demonstrate exceptional explanation-generation capabilities on relatively simple tasks. However, their effectiveness in handling complex real-world tasks warrants in-depth study.
+
+# 2 Related Work
+
+# 2.1 Datasets with Natural Language Explanations
+
+Wiegrefe and Marasovic (2021) conducted a com
+
+prehensive review of 65 datasets with explanations and provided a 3-class taxonomy: highlights, free-text, and structured. Among the large-scale datasets with free-text explanations, e-SNLI (Camburu et al., 2018) is a prominent one, which extended the Stanford Natural Language Inference (SNLI) corpus (Bowman et al., 2015), a classification task to determine the inference relation between two textual contexts (premise and hypothesis): entailment, contradiction, or neutral. The e-SNLI dataset (examples are shown in Appendix A) contains human-annotated free-form explanations for 549, 367 examples in train, 9, 842 in validation, and 9, 824 in test split.
+
+Another popular group of datasets extended the Commonsense QA (CQA v1.0 and v1.11 versions) datasets (Talmor et al., 2019), including two variants of Cos-E dataset (CoS-E v1.0 and CoS-E v1.11 (Rajani et al., 2019)) and the ECQA (Aggarwal et al., 2021) dataset. Many recent works (Narang et al., 2020; Sun et al., 2022) have found explanations in CoS-E to be noisy and low-quality, and thus, Aggarwal et al. (2021) carefully designed and followed the explanation annotation protocols to created ECQA, which is of higher quality compared with CoS-E.
+
+In this paper, we leverage the e-SNLI dataset as the benchmark dataset for our AL simulation experiment because 1) the classification task is popular and representative, 2) the massive data size ensures data diversity, and 3) explanations for a classification task may provide more effective help compared to CQA task where training and testing data may be unrelated. We additionally conduct an ablation study on the ECQA dataset to explore the generalizability of our proposed AL architecture.
+
+# 2.2 Active Learning for Data Annotation
+
+Owning to the paucity of high-quality, large-scale benchmarks for a long tail of NLP tasks, learning better methods for low-resource learning is acquiring more attention, such as Active Learning (AL) (Sharma et al., 2015; Shen et al., 2017; Ash et al., 2019; Teso and Kersting, 2019; Kasai et al., 2019; Zhang et al., 2022). AL iteratively 1) selects samples from the unlabeled data pool (based on AL sampling strategies) and queries their annotation from human annotators, 2) fine-tunes the underlying model with newly annotated data, and 3) evaluates model performance.
+
+A few AL surveys (Settles, 2009; Olsson, 2009;
+
+Fu et al., 2013; Schröder and Niekler, 2020; Ren et al., 2021) of sampling strategies provide two high-level selection concepts: data diversity and model probability. We propose a novel data diversity-based strategy that leverages human-annotated explanations to select data. Our data selector shares a similar concept with the established data-based clustering strategies (Xu et al., 2003; Nguyen and Smeulders, 2004) and core-set (Sener and Savarese, 2017) that aim to select the most representative data while maximizing diversity. Compared with model probability-based strategies, data diversity-based ones are model-agnostic and need much less computing resources, whereas the former requires inference on unlabeled examples to calculate probability.
+
+In addition to AL, Marasovic et al. (2022) introduces a few-shot self-rationalization setting that asks a model to generate free-form explanations and the labels simultaneously. Similarly, Bhat et al. (2021) proposes a multi-task self-teaching framework with only 100 train data per category, and Bragg et al. (2021) provides guidance on unifying evaluation for few-shot settings.
+
+# 2.3 Natural Language Explanation Generation
+
+Different approaches have been explored to enhance the model's explainability by asking them to generate natural language explanations. Some of them (Talmor et al., 2020; Tafjord et al., 2021; Latcinnik and Berant, 2020) propose systems to generate text explanations for specific tasks. Dalvi et al. (2022) propose a 3-fold reasoning system that generates a reasoning chain and asks users for correction. Other recent works (Paranjape et al., 2021; Liu et al., 2022; Chen et al., 2022) explore different prompt-based approaches to generate additional information for the task and examine the robustness and validity. We believe that our dual-model system provides and uses explanations explicitly towards prediction, while the self-rationalization setting falls short. Hase and Bansal (2022) argues that explanations are most suitable as input for predicting, and Kumar and Talukdar (2020) designed a system to generate label-wise explanations, which is aligned with our design hypothesis. Nevertheless, there exist other works (Wiegreffe et al., 2021; Marasovic et al., 2022; Zelikman et al., 2022) that explore the use of self-rationalization setting. We include the self-rationalization setting in our human
+
+evaluation of the explanation quality in Section 4.4.
+
+# 3 Dual-Model AL System
+
+# 3.1 System Architecture
+
+Figure 1 illustrates our proposed dual-model AL framework. The system comprises three primary modules: 1) an explanation-generation model that takes the data, fine-tunes on human-annotated explanations, and generates free-form explanations; 2) a prediction model that accepts the data content and the generated explanations as input, fine-tunes on human-provided labels, and predicts the final label; 3) an AL data selector that selects a set of representative examples in terms of the semantic similarity between each unlabeled data text and labeled data's human explanations. The AL data selector plays a crucial role in finding a small, highly representative set of samples at every iteration, and further details of our AL selector are in Section 3.2.
+
+In each AL iteration, after the data selector samples unlabeled examples for human annotations, we first fine-tune the explanation-generation model supervised by human-provided free-form explanations. Then, we instruct this model to generate explanations for the same set of data. Subsequently, we fine-tune a prediction model using the data content and explanations generated by the previous model as input, supervised by human-annotated labels. The fine-tuning process teaches the prediction model to rely on the explanations for predictions (Yao et al., 2023). Additionally, we fine-tune the prediction model with model-generated explanations instead of human-annotated ones for better alignment during inference, especially when no human annotations are available. After each AL iteration, we evaluate the framework on a standalone evaluation data split.
+
+Both the explanation-generation model and the prediction model can be any SoTA sequence-to-sequence models, such as BART (Lewis et al., 2020) and T5 (Raffel et al., 2020). In this work, we utilize T5 as the backbone for both models and design a prompt-based input template for both models, as shown in Table 1, inspired by a few existing works (Schick and Schütze, 2021; Gao et al., 2021; Zhou et al., 2023). To elucidate how each prompt addresses a different part of data content:
+
+1) "explain:" and "question:" are the leading prompts in the explanation-generation model and the prediction model, respectively, indicating different tasks for both models and are followed by
+
+# Explanation-generation Model:
+
+Training Input explain: what is the relationship between
+
+[hypothesis] and [premise] choice1: entailment choice2:
+
+neutral choice3: contradiction
+
+Training Target [human annotated explanations]
+
+Model Generation [generated free-form explanation]
+
+# Prediction Model:
+
+Training Input question: what is the relationship between [hypothesis] and [premise] choice1: entailment choice2: neutral choice3: contradiction  $< \mathrm{sep}>$  because [generated free-form explanation]
+
+Training Target [human annotated label]
+
+Model Prediction [predicted category]
+
+Table 1: The prompt-based input templates for both models in our system, with the e-SNLI (Camburu et al., 2018) dataset as an example.
+
+the original task content. For the e-SNLI dataset, the task content becomes "what is the relationship between" the hypothesis and premise sentences;
+
+2) "choiceN" is followed by candidate answers, where  $N \in [1,3]$  for the e-SNLI dataset corresponds to entailment, neutral, and contradiction. We pass the choices to the explanation-generation model, expecting that it will learn to generate freetext explanations that may reflect potential relationships between the data content and the task;  
+3) for the prediction model, an additional prompt "because:" is followed by the explanations generated by the explanation-generation model. We use a special token to separate the original task content and the explanation."
+
+# 3.2 AL Data Selector
+
+# Algorithm 1 Our Data Diversity-based AL Selector
+
+# Variables:
+
+$D_{train} \Rightarrow$  unlabeled data in train split
+
+$D_{prev} \Rightarrow$  previously-annotated data
+
+$d_p^{data} \Rightarrow$  data content as a string of  $d_p$  (for e-SNLI, it is the premise and hypothesis
+
+$d_p^{exp} \Rightarrow$  previously-annotated free-form explanation of  $d_p$ $x \Rightarrow$  number of data to be selected each iteration
+
+$n_{train} = \text{len}(D_{train})$ ;  $n_{prev} = \text{len}(D_{prev})$
+
+for  $D_{i}\in D_{train}$  do
+
+if iteration  $= = 0$  then
+
+$score_{d_i} = \frac{1}{n_{train}} \cdot \sum_{d_p \in D_{train}} similarity(d_i^{data}, d_p^{data})$
+
+else
+
+$score_{d_i} = \frac{1}{n_{prev}} \cdot \sum_{d_p \in D_{prev}} similarity(d_i^{data}, d_p^{exp})$
+
+end if
+
+end for
+
+$D_{train}^{\prime}$  = rank  $D_{train}$  by score
+
+$D_{\text{selected}} = \text{select} x \text{ data from } D_{\text{train}}^{\prime} \text{ with equal intervals}$
+
+Human annotation on  $D_{\text{selected}}$
+
+$D_{train} = D_{selected};D_{prev} + = D_{selected}$
+
+According to recent surveys of AL (Settles, 2009; Olsson, 2009; Fu et al., 2013; Schroder and Niekler, 2020; Ren et al., 2021), there are two primary approaches for AL data selection:
+
+model probability-based and data diversity-based approaches. Model probability-based approaches, firstly, aim to select examples about which the models are least confident. These approaches involve conducting inference on unlabeled data at every iteration, which consumes more time and computing resources. Unlike data diversity-based approaches, they are not model-agnostic, which may affect the effectiveness of the sampling strategies depending on the model in use.
+
+Secondly, data diversity-based approaches leverage various data features, such as data distribution and similarity, to select a representative set of examples from the candidate pool while maximizing diversity. This paper introduces a data diversity-based AL selection strategy that shares a concept similar to traditional data-based clustering strategy Nguyen and Smeulders (2004) and core-set strategy. However, our strategy differs from traditional strategies because ours incorporates human-annotated explanations for selection. More specifically, our data selector aims to choose examples that are representative of the unlabeled data pool in terms of average similarity to human-annotated explanations of all previously-labeled data while maximizing the diversity of newly-selected data.
+
+We assume that human-annotated explanations contribute significantly to the model's prediction and convey more information than the original data content alone. These explanations can reveal underlying relationships between concepts in the data content and the relations between the data content and choices. For instance, in the e-SNLI dataset, the data content consists of the concatenation of hypothesis and premise sentences. Later, we construct a baseline selector in the AL simulation experiment (Sec. 4.2) with the same setup, except that it only compares the similarity between data content. Additionally, we include random baseline and probability-based baseline strategies. Our results demonstrate that using human-annotated explanations for data selection consistently leads to improved prediction performance compared to using data content alone.
+
+Here we delve into the details of our data-based AL data selector (shown in Algorithm 1). For each unlabeled data instance, we use sentence-transformers (Reimers and Gurevych, 2019) to calculate the semantic similarity between its data content and every previously annotated explanation. Then, we take the averaged similarity scores for
+
+![](images/0939f6381c5f0e1b2beb721aa8e29b0cb4ea526761b204b1efeecaa4f7af2d25.jpg)  
+Figure 2: Preliminary experiment result of our dual-model system on e-SNLI (Camburu et al., 2018) dataset.
+
+each unlabeled example and rank all the unlabeled data in terms of the average similarity score. To select the most representative data in the candidate pool while maximizing diversity, we choose examples from the ranked data list with equal intervals. Note that in the first iteration, since no previously annotated explanations are available, we compare the similarity between the data content.
+
+# 4 Evaluation
+
+We conduct the AL simulation experiment with the e-SNLI (Camburu et al., 2018) dataset. The primary objective is to justify that our proposed dual-model framework, when combined with human-annotated explanations in AL data selection, can effectively identify more representative and helpful data from a reasonably large-scale dataset.
+
+Given that e-SNLI dataset comprises a substantial 549,367 examples in the train split, we performed a preliminary experiment to determine a reasonable number of candidate data for the AL simulation. This approach aims to save time and computing resources. Our goal is to identify an ideal candidate data size that would not introduce potentially biased feature distributions or significantly degrade model performance when compared to fine-tuning on the full dataset. We employ the pre-trained T5-base (Raffel et al., 2020) as the backbone for all the experiments and provide the hyperparameters in Appendix C.
+
+# 4.1 Preliminary Experiment
+
+The expected outcome of the aforementioned preliminary experiment is 1) to determine the upper bound of performance and observe how the performance of our dual-model system gradually decreases as we reduce the amount of training data, and 2) to identify a suitable candidate data size for
+
+the AL simulation.
+
+We also randomly sample the same amount of data for each category in the preliminary experiment to minimize potential bias introduced by uneven distribution, especially when the sampling size per iteration is very small. Specifically, we select eight different sampling amounts per category from the e-SNLI training split, ranging from [10, 50, 100, 500, 1500, 3000, 5000] and the complete data per category. Since the e-SNLI dataset consists of three categories: entailment, neutral, and contradiction, the total sampling size in each setting becomes [30, 150, 300, 1500, 4500, 9000, 15000, and 549, 367 (full train split)], respectively, as shown in Figure 2.
+
+For each sampling setting, we conduct three trials to obtain an averaged result. In each trial, we fine-tune the explanation generation model and the prediction model once and conduct a hyperparameter search. The framework is then evaluated on the test split of e-SNLI (9, 824 examples).
+
+The preliminary experiment results are shown in Figure 2, where the blue dot denotes the averaged prediction accuracy (in percentages) at each setting, and the red bar indicates the standard deviation of accuracy among three trials. Notably, with more than 1,500 data per category, the performance drop compared to the full train split is inconspicuous (84.08% to 87.02%), while the standard deviation is below 0.5%. This observation indicates that using 1% of the original training data size only leads to a performance drop of merely 3%. Additionally, we found that even with only 10 data points per category (30 data in total), our system still achieves an average accuracy of 45%, although the deviation is relatively significant. Furthermore, when we extend the training data size from 100 to 500 data per category (300 to 1500 in total), a reasonably applicable setting in real-world scenarios, the accuracy can reach over 80% accuracy, showing promising results considering that the amount of training data is much smaller than the size of evaluation samples.
+
+# 4.2 Simulation Experiment: Evaluation Setup
+
+Based on the findings from the preliminary experiment, we decide to use 3,000 examples per category (9,000 in total) as the candidate unlabeled data pool for the Active Learning simulation.
+
+Inspired by the few-shot evaluation guidance (Bragg et al., 2021), we conduct 80 trials for each AL setting and calculate the averaged per
+
+![](images/fcb87fd614309a6eb168e6d78858d94d5f1cb23ae3be052ebbba600797ffbae6.jpg)  
+(a) Setting 1: 9 examples per iteration + 20 iterations
+
+![](images/2166bc5991d46e8efc2e7af3ee63ec0ba05a2a82a98be0bbfa72aceb88708cee.jpg)  
+(b) Setting 2: 30 examples per iteration + 15 iterations  
+Figure 3: Results of AL Simulation experiment on our Dual-model system with different data selectors.
+
+formance for ours and the baseline data selectors at every iteration. During each trial, we start by randomly selecting 3,000 examples per category from the complete train dataset, then use the same data to conduct AL simulations with different data selectors in our dual-model framework. This way, we can ensure the performance differences during each trial are not due to different unlabeled data pools but to actual differences in the performance of the AL data selectors. For the evaluation, we randomly sample 300 examples per category (900 in total) from the test split of e-SNLI every trial and evaluate with the same test data after each iteration.
+
+The AL simulation comprises two settings, where we simulate annotating 180 and 450 data instances, respectively. These two levels of data annotations reasonably mimic real-world scenarios where users have limited budgets, annotators, and data for annotation. Specifically, we experiment with the following two settings:
+
+1) For every iteration, select 3 examples per category (9 in total) with 20 iterations, which results in 180 examples altogether;  
+2) For every iteration, select 10 examples per category (30 in total) with 15 iterations, which results in 450 examples altogether.
+
+Our AL simulation experiment involves our data selector, two baselines, and an additional model
+
+probability-based selector. Our data selector, described in Section 3.2, is a novel data diversity-based sampling strategy that leverages human-annotated explanations. For comparison, we use a random data selector as the basic benchmark and another traditional data diversity-based algorithm that shares the same procedures with ours, except that it only compares the similarity between each unlabeled data's content and the previously-labeled data's content, not using the human-annotated explanations. The probability-based selector conducts inference on unlabeled data and selects examples with the least probability at every iteration. We fix the same set of hyperparameters (Appendix C).
+
+Worth noting that our data selector does not use task content in previously labeled examples; instead, we exclusively rely on human-annotated explanations to demonstrate their greater utility compared to task content. In the first iteration, both ours and the data diversity baseline perform identically because no previously annotated data is available.
+
+# 4.3 Simulation Experiment: Result
+
+The AL simulation results are presented in Figure 3. To explain the diagrams in detail, each dot is the average accuracy on 80 trials at every iteration for each data selector. The green/yellow/red/blue dots denote our data selector/data diversity-based baseline/random selector/model probability-based selector, respectively. We observe that our data selector consistently maintains an advantage over the traditional data-based sampling baseline, while the traditional one consistently beats the random baseline by a significant margin. Additionally, we observe that the model probability-based selector outperforms the random baseline in both settings.
+
+To summarize, our data selector outperforms both baselines in every iteration for both AL settings, indicating that using human-annotated explanations in the data selector with our dual-model AL framework is more beneficial than using the data content alone. Even with only 180 and 450 data to be annotated in each setting, our system can achieve  $55\%$  and  $72\%$  accuracy on average, respectively. We anticipate that our experiment will reach a similar performance around  $85\%$  as shown in Figure 2 but converge much faster than the random selector if we continue the AL process.
+
+# 4.4 Human Evaluation Setup and Results
+
+To qualitatively evaluate the explainability of the generated explanations from our system against
+
+<table><tr><td>Yes / No Count</td><td>Label</td><td>Exp.</td><td>Exp. → Label</td><td>Trustworthy AI</td></tr><tr><td>Ground-truth</td><td>83 / 7</td><td>86 / 4</td><td>87 / 3</td><td>78 / 12</td></tr><tr><td>Dual-model (ours)</td><td>64 / 26</td><td>68 / 22</td><td>48 / 42</td><td>35 / 55</td></tr><tr><td>Self-rationalization</td><td>42 / 48</td><td>67 / 23</td><td>51 / 39</td><td>21 / 69</td></tr></table>
+
+Table 2: Human evaluation results.
+
+a SoTA few-shot explanation-generation system, the self-rationalization baseline (Marasovic et al., 2022), and the human ground-truth, we recruited three human participants to conduct a human evaluation following the prior literature (Xu et al., 2022). The self-rationalization baseline is a T5-base model, which uses the same input template of our explanation-generation model shown in Table 1 but asks the model to generate both the label and explanation simultaneously.
+
+We leverage AL setting 1 described in Section 4.2 to fine-tune our system with a total of 180 examples over 20 epochs and use the same 180 examples to fine-tune the self-rationalization baseline. Both systems are used to infer the complete test split of e-SNLI after fine-tuning; then, we randomly sample 80 examples for the human study.
+
+For each data instance, the rater is presented with the textual content of the premise and hypothesis of the original data paired with three sets of labels and explanations from our system, baseline system, and the human-annotated ground-truth from the eSNLI dataset. Participants who are not aware of the source of each label-explanation pair are asked to answer four questions with [Yes/No]:
+
+1) Is the Prediction correct?  
+2) Is the Explanation itself a correct statement?  
+3) Regardless of whether the AI Prediction and Explanation is correct or not, can the Explanation help you to understand why AI has such Prediction?  
+4) Will you trust & use this AI in real-world decision-making?
+
+To ensure inter-coder consistency, we first conduct a 30-min tutorial session to educate all three participants with ten examples to build a consensus among them. In the actual experiment, each of the three participants is then asked to rate 30 data instances (20 unique ones and 10 shared ones), which make up a total of 70 data instances, and 360 ratings (3 rater*30 instances*4 questions). We first calculated the Inter-Rater Reliability score (IRR) among them for each of the four questions. With the IRR score of (Q1: 1, Q2: 0.89, Q3: 0.98, Q4: 0.87), we are confident that the three coders have the same criteria for further result analysis.
+
+Our questions all have binary responses, and we
+
+rely on Chi-square analysis (Elliott and Woodward, 2007) to examine the statistical significance of the rating groups' differences. As shown in Table 2, the participants rated human ground-truth explanations highest across all four dimensions. Between our system and the few-shot self-rationalization system (baseline), participants believe our systems' predicted labels are more likely to be correct, with 64 'valid' ratings out of 90 for our system versus 42 out of 90 ratings for the baseline. Chi-square test indicates such a difference is statistically significant  $(\chi^2(1) = 21.61, p < 0.01)$ .
+
+When asked whether they would trust the AI if there were such AI systems supporting their real-world decision-making, 35 out of 90 answered 'Yes' for our system, and it is significantly better than the baseline system (21 'Yes' out of 90)  $(\chi^2(1) = 12.17, p < 0.01)$ . In comparison, 78 out of 90 times people voted that they would trust the human-annotated explanation's quality.
+
+As for Question 2 ("the validity of the generated explanation") and Question 3 ("whether the generated explanation is supporting its prediction"), the human evaluation fails to suggest statistically meaningful results between our system and the baseline system  $(\chi^2(1) = 0.06, p = 0.89$  for explanation validity, and  $\chi^2(1) = 0.41, p = 0.52$  for explanation supporting prediction). In summary, human participants believe our system can outperform the baseline system on the label prediction's quality and the trustworthiness of AI dimensions. Still, there is a large space to improve as human evaluators believe the ground-truth label and explanation quality is much better than either AI system.
+
+# 4.5 Ablation Study 1: Transfer to Multi-NLI
+
+We conduct an ablation study with transfer learning through AL simulation from e-SNLI to Multi-NLI (Williams et al., 2018). This study explores whether the explanation-generation model trained on e-SNLI is helpful for AL on a similar task.
+
+The transfer-learning ablation study consists of the following steps: 1) fine-tune an explanation-generation model using our AL framework on the e-SNLI dataset; 2) freeze the explanation-generation model and use it to generate explanations in the AL simulation for Multi-NLI; 3) fine-tune the prediction model for Multi-NLI at every iteration. Unlike the e-SNLI experiment, our AL data selection algorithm will use model-generated explanations to select examples at every iteration in the transfer learn
+
+![](images/8f0c8a6d0c8002bff1252f01cb701f9967091d30fe19c9abdf8a5db89b9c757f.jpg)  
+(a) Setting 1: 9 examples per iteration + 20 iterations
+
+![](images/795a73e161e4ef1c7da130ff42f1b005fe618462edb812751ef7ec92e37a9def.jpg)  
+(b) Setting 2: 30 examples per iteration + 15 iterations  
+Figure 4: Ablation study results of AL simulation experiment on our Dual-model system with different data selectors on ECQA dataset.
+
+ing AL simulation. We fine-tune the explanation-generation models on e-SNLI with the same two settings in the previous experiment, average the result on 15 trials of experiments, and keep consistent with every other hyper-parameters.
+
+The ablation results are shown in Figure 6 of Appendix B. The blue/red lines denote the explanation-generation model is fine-tuned on e-SNLI with each setting in Section 4.2 correspondingly. We observe that the explanation-generation model consistently provided helpful explanations, leading to an improvement in the system's prediction performance, with accuracy reaching more than  $65\%$ . In addition, the explanation-generation model fine-tuned on more data can perform better, suggesting that it had learned to generate more helpful explanations.
+
+# 4.6 Ablation Study 2: Our AL Framework on ECQA
+
+We additionally conduct an AL Simulation experiment on ECQA (Aggarwal et al., 2021) (a recent dataset extends the CommonsenseQA dataset with high-quality human-annotated explanations) with our data selector, random baseline, and similarity-based baseline that does not use explanations. We comply with the same experiment settings for the eSNLI AL simulations described in Section 4.2. The
+
+![](images/4f8d820ada2623cd2907f25a2f93656f7c330d4b7b1c27fb69fda9a5854d5b48.jpg)  
+(a) e-SNLI Dataset
+
+![](images/65122090e626ddcd070f58cabec056f378a00aca5a3c0da7703fb207a5b82647.jpg)  
+(b) ECQA Dataset  
+Figure 5: Ablation study results of AL simulation experiment with FLAN-T5-XL for explanation (exp.) generation in our Dual-model framework compared with best human-annotated explanations on e-SNLI (top) and ECQA (bottom) datasets.
+
+results are shown in Figure 4, where our proposed data selection strategy can consistently outperform both baselines in both simulation settings. Interestingly, the similarity-based baseline performs similarly to the random baseline, which could be because using data content alone is not sufficient to select more helpful and representative examples while using human-annotated explanations can facilitate better data selection consistently.
+
+# 4.7 Ablation Study 3: LLM for Explanation Generation
+
+The recent prevalence of instructional-finetuned large language models (LLMs) (Wei et al., 2021; Chowdhery et al., 2022; Ouyang et al., 2022) with exceptional generation capabilities off-the-shelf enabled a straightforward idea upon our dual-model framework: can LLMs generate natural language explanations that are on par or even of higher quality than human-annotated ones, to facilitate the prediction model fine-tuning process? We conduct ablation experiments to leverage FLAN-T5-XL (Chung et al., 2022) for explanation generation in our framework to substitute the T5 model fine-tuned on human explanations (LLM-AL, hereinafter). We conduct the AL simulations
+
+on e-SNLI and ECQA datasets to explore whether we can further reduce human annotation efforts.
+
+The results are presented in Figure 5, where a horizontal dotted line represents the benchmark of the explanation generation model fine-tuned on human-annotated explanations in Section 4.3 and 4.6. The LLM-AL framework significantly outperforms the explanation generation model guided by human annotation in both Active Learning settings. However, we hypothesize the LLM's explanation generation capability can vary from task to task. It may be highly efficient in relatively easy tasks, such as e-SNLI and ECQA datasets, both of which are training datasets for FLAN-T5. Yet, LLMs may struggle to provide helpful explanations in complex real-world domain-specific tasks, where human experts' feedback may still be necessary and preferred. This leads to another potential avenue for future work: exploring the capability and limitations of leveraging LLMs for explanation generation in real-world scenarios.
+
+# 5 Conclusion and Future Work
+
+In summary, this paper introduces a novel dual-model AL system designed to address the common real-world need for domain experts to provide both classification labels and natural language explanations. Our system comprises a purpose-built data diversity-based AL example selector and two sequence-to-sequence language models, one for explanation generation and the other for label prediction. Through an AL simulation evaluation and a human assessment of the e-SNLI dataset, our results demonstrate the effectiveness of explanations in AL sampling with our system. They consistently outperform both baselines, and the explanations generated by our system are preferred over a state-of-the-art explanation-generation system.
+
+Our work lays a step-stone towards a human-centered interactive AI solution (it can be easily implemented as an interactive system as illustrated in Fig 7 in Appendix D) that supports domain experts for their data annotation tasks. Many real-world tasks still require domain experts to review and annotate each data instance with a decision and an explanation for accountability purposes (e.g., a lawyer reviewing and signing off on a legal document). We invite fellow researchers to join us in advancing this research direction, essential for supporting this prevalent real-world requirement.
+
+# 6 Limitations
+
+In this paper, we demonstrate the effectiveness of our framework on a representative large-scale classification dataset (e-SNLI), but there are many other NLP tasks, such as question answering and commonsense reasoning. The generalizability of our system on other NLP tasks remains unexplored. Another limitation is that this work proposed a data diversity-based AL selector design. We benchmark it with a traditional data diversity-based selector as well as a model probability-based design to demonstrate the usefulness of explanations. Prior literature has proposed other designs, such as ensemble approaches, which are not evaluated in this paper.
+
+# Acknowledgements
+
+This work was supported by the Rensselaer-IBM AI Research Collaboration (http://airc.rpi.edu), which is part of the IBM AI Horizons Network (http://ibm.biz/AIHorizons).
+
+# References
+
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+
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+
+# Appendix
+
+# A e-SNLI Examples
+
+Table 3 illustrates an example data of each category in the e-SNLI dataset. Every data instance contains a premise and hypothesis along with a human annotated label and free-form explanation.
+
+Premise: This church choir sings to the masses as they sing joyous songs from the book at a church.
+
+Hypothesis: The church is filled with song.
+
+Label: entailment
+
+Human-annotated explanation: "Filled with song" is a rephrasing of the "choir sings to the masses.
+
+Premise: A man playing an electric guitar on stage.
+
+Hypothesis: A man is performing for cash.
+
+Label: neutral
+
+Human-annotated explanation: It is unknown if the man is performing for cash.
+
+Premise: A couple walk hand in hand down a street.
+
+Hypothesis: A couple is sitting on a bench.
+
+Label: contradiction
+
+Human-annotated explanation: The couple cannot be walking and sitting a the same time.
+
+Table 3: Sample data of each category in e-SNLI (Camburu et al., 2018) dataset.
+
+# B Transfer Learning Ablation Study Diagrams
+
+Figure 6 shows the results of our Ablation Study results described in Section 4.5. The explanation-generation model is fine-tuned from AL on e-SNLI dataset with two different AL settings, then we freeze the explanation-generation model to train the prediction model in AL simulation for Multi-NLI dataset under two settings. Setting  $1/2$  refers to the settings for Active Learning Simulation in Section 4.2.
+
+# C System Environment and Hyper-Parameters
+
+The computing resource of all the experiments we conducted in this paper has 128 Gigabytes of RAM. In addition, we use 2 NVIDIA Tesla V100 GPU for the preliminary experiment and 8 NVIDIA Tesla V100 GPU for the AL simulation experiment.
+
+# C.1 Preliminary Experiment
+
+For the Preliminary experiment described in Section 4.1, we leverage the same set of finetuning hyper-parameters other than the number of fine-tuning epochs for the explanation-generation model (denotes as  $M_{EG}$ ) and the prediction model (denotes as  $M_P$ ). The same set
+
+![](images/1566af5fc8004c2f07a9c177399c7fd9341e7673f377319893c49e98d2f85f37.jpg)
+
+![](images/7763ccb45986b4f28a72f115143346eed8d1849a591afa0e08d51712f235760e.jpg)  
+(a) Active Learning on Multit-NLI using explanation-generation model from e-SNLI with Setting 1  
+(b) Active Learning on Multit-NLI using explanation-generation model from e-SNLI with Setting 2
+
+Figure 6: Results of Transfer Learning Ablation Study of AL Simulation experiment on our Dual-model system from e-SNLI to Multi-NLI. Setting  $1/2$  refers to the settings for Active Learning Simulation in Section 4.2.
+
+of hyper-parameters is: batch_size_per_GPU = 2; learning_rate =  $1e^{-4}$ ; input_max_length = 512; target_max_length = 64
+
+We conduct a hyper-parameter search for the number of fine-tuning epochs for each amount of sampled examples, details are shown in Table 4.
+
+<table><tr><td># of train data per category / total</td><td>epoch for MRG</td><td>epoch for MP</td></tr><tr><td>10 / 30</td><td>25</td><td>100</td></tr><tr><td>50 / 150</td><td>25</td><td>250</td></tr><tr><td>100 / 300</td><td>10</td><td>250</td></tr><tr><td>500 / 1500</td><td>5</td><td>50</td></tr><tr><td>1500 / 4500</td><td>5</td><td>50</td></tr><tr><td>3000 / 9000</td><td>5</td><td>25</td></tr><tr><td>5000 / 15000</td><td>5</td><td>25</td></tr><tr><td>Full</td><td>1</td><td>1</td></tr></table>
+
+Table 4: Fine-tuning epochs of each model in our dual-model system with different data amount settings.
+
+# C.2 AL Simulation Experiment
+
+For both of the AL Simulation settings we experimented in Section 4.2, we leverage the same set of hyper-parameters for fine-tuning our dual-model AL system: batch_size_per_GPU =
+
+2; learning_rate = 1e-4;  $M_{EG}$  _train_epoch = 20,  $M_P$  _train_epoch = 250; input_max_length = 512; target_max_length = 64
+
+# D Proposal for an Interactive System
+
+Our proposed dual-model system can be easily implemented as an interactive human-centered AI system for supporting domain experts and human annotators in labeling both labels and explanations.
+
+![](images/6309e63f7c1f47a4025f9cd1ff886b93125acb1270d91413c0b8100884d46db0.jpg)  
+Figure 7: Our proposed dual-model system can be implemented as an interactive AL-based data annotation system to speed up users' annotation productivity. Such a system can simply have an interface with four output functions (i.e., display unlabeled data, display AL selected data, display generated-explanation, and display predicted labeled) and one input function (i.e., annotate label and explanation for the unlabeled data.

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+        "text": "Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding",
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+        "text": "Xi Zhu, Xue Han, Shuyuan Peng, Shuo Lei, Chao Deng and Junlan Feng*",
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+        "text": "{zhuqian,hanxueai,pengshuyuan,leishuo,dengchao,fengjunlan}@chinamobile.com",
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+        "text": "Effectively encoding layout information is a central problem in structured document understanding. Most existing methods rely heavily on millions of trainable parameters to learn the layout features of each word from Cartesian coordinates. However, two unresolved questions remain: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are massive learnable parameters truly necessary for layout representation? In this paper, we address these questions by proposing Layout Attention with Gaussian Biases (LAGaBi): Firstly, we find that polar coordinates provide a superior choice over Cartesian coordinates as they offer a measurement of both distance and angle between word pairs, capturing relative positions more effectively. Furthermore, by feeding the distances and angles into 2-D Gaussian kernels, we model intuitive inductive layout biases, i.e., the words closer within a document should receive more attention, which will act as the attention biases to revise the textual attention distribution. LAGaBi is model-agnostic and language-independent, which can be applied to a range of transformer-based models, such as the text pre-training models from the BERT series and the LayoutLM series that incorporate visual features. Experimental results on three widely used benchmarks demonstrate that, despite reducing the number of layout parameters from millions to 48, LAGaBi achieves competitive or even superior performance. Our code is available on GitHub<sup>1</sup>.",
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+        "text": "1 Introduction",
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+        "text": "Structured document understanding (SDU) has gained significant research attention in the field of intelligent document processing (Park et al., 2019; Jaume et al., 2019; Han et al., 2023). It focuses on extracting layout structures and contents from scanned or digital documents, leading to enhanced",
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+        "text": "performance in several downstream tasks like form comprehension and receipt understanding.",
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+        "text": "Unlike conventional text understanding (Liu et al., 2019; Vaswani et al., 2017; Kenton and Toutanova, 2019), SDU goes beyond comprehending serialized text and requires the ability to interpret documents with diverse layouts (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). Documents with varying layouts often contain text fields positioned in different ways. To take advantage of existing pre-trained language models, early methods (Xu et al., 2020; Li et al., 2021a,c; Appalaraju et al., 2021; Chi et al., 2020) propose to directly add 2-D position embedding to the word embedding for each word as input for Transformer. The position embedding encode the 2-D absolute coordinates  $(x_0, y_0, x_1, y_1)$  of each word in the document through multi position encoding layers, where  $(x_0, y_0)$  represents the upper left point and  $(x_1, y_1)$  represents the lower right point of the bounding box for each word. Some researches (Powalski et al., 2021; Hong et al., 2022; Lee et al., 2022; Xu et al., 2021a; Huang et al., 2022) further proposed that absolute positions are inefficient for representing the spatial relationships between words. They employ relative positions between words to encode spatial relationships. For example, Hong et al.(2022) map the relative positions into embeddings, which are then multiplied with the semantic embedding of the word to calculate inter-word layout scores. This score is incorporated into the self-attention layers to combine semantic and layout features.",
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+        "text": "Despite the significant progress made, we argue that current methods, whether based on absolute or relative positioning, heavily rely on a large number of trainable parameters for position embeddings from Cartesian coordinates, often comprising millions of parameters. This raises two unexplored questions: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are",
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+        "text": "massive learnable layout parameters truly necessary? Concerning the former, various coordinate systems exist, including Cartesian, polar, and spherical coordinates, yet previous research has solely focused on Cartesian coordinates. Regarding the latter, it is intuitively expected that words closer within a document should receive more attention. However, this simple inductive bias may not be effectively learned solely through gradient updates.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "In this paper, we present a unified investigation of the two aforementioned problems. Regarding the choice of coordinate systems, we find that polar coordinates offer a more efficient representation than Cartesian coordinates for expressing relative positions. By computing the differences in distance and angle between two words in polar space, polar coordinates outperform their Cartesian counterparts by providing extra angle information. For layout learning, we discover that layout modeling can be achieved by a specific distribution: words closer in space receive higher layout scores, eliminating the need for extra position embeddings. Combining these two choices, we propose LAGaBi (Layout Attention with Gaussian Biases). LAGaBi formulates pairwise spatial relationships between tokens using the distance and angle in the polar coordinate system. Moreover, the distance and angle are fed into a 2-D Gaussian distribution to output a layout score. We choose the Gaussian distribution because it guarantees that the layout score decreases when either the distance or angle variables increase, making it the most commonly used distribution for this purpose. The layout score is then incorporated into the original self-attention as attention bias, resulting in a revised distribution that considers both text and layout features. We introduce trainable Gaussian kernels to better align the semantic and layout scores at different scales, adding just  $4 \\times$  attention heads extra parameters. For instance, based on RoBERTa (Liu et al., 2019) with 12 attention heads, there are only 48 additional parameters that need to be learned for encoding layout features.",
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+    },
+    {
+        "type": "text",
+        "text": "Extensive experiments demonstrate that LAGaBi achieves remarkable performance on diverse SDU benchmarks, including FUNSD (Jaume et al., 2019), CORD (Park et al., 2019), and XFUND (Xu et al., 2021b), across both monolingual and multilingual scenarios. LAGaBi emerges as a versatile module that seamlessly integrates with transformer-based language models, such as BERT (Kenton and Toutanova, 2019), RoBERTa, and InfoXLM (Chi",
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+    },
+    {
+        "type": "text",
+        "text": "et al., 2020), empowering them to effectively process structured documents and achieve significant performance gains of up to 27.01 points. Additionally, LAGaBi can be incorporated into complex SDU models that leverage visual features, such as LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022), leading to further performance enhancements and establishing new state-of-the-art results.",
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+    {
+        "type": "text",
+        "text": "2 Related Works",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Significant progress has recently been made by using the Transformer-based pre-trained model (PTM) to learn the cross-modality interaction between textual and layout information, which has been demonstrated to be critical for structured document understanding (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). LayoutLM (Xu et al., 2020) modified the input of BERT (Kenton and Toutanova, 2019) by adding position embedding layers to encode word-level 2-D coordinates, while StructuralLM (Li et al., 2021a) proposed to encode segment-level positions. LiLT (Wang et al., 2022a) encoded text and layout using two different transformer layers separately and adopted bi-directional attention to fuse them. Besides, there are a series of works that use multi-modal transformers to model text, layout, and image simultaneously, such as SelfDoc (Li et al., 2021b), DocFormer (Appalaraju et al., 2021), StrucTexT (Li et al., 2021c), ERNIE-Layout (Peng et al., 2022), mmLayout (Wang et al., 2022b).",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Most of the above approaches encode the 2-D absolute positions, ignoring the critical relative spatial relationships between words that are essential to textual semantic understanding. Hong et al.(2022) proposed to encode the spatial relationships as relative position embeddings, which are then multiplied with the semantic embedding of the token to calculate inter-word layout scores. This score is incorporated into the self-attention to combine semantic and layout features. GeoLay-outLM (Luo et al., 2023) introduces geometric relations and brand-new geometric pre-training tasks in different levels for learning the geometric layout representation, whose geometric relations are largely dependent on some pre-defined manual rules. TITL (Powalski et al., 2021) encodes the relative positions between words in a simpler manner, and it adopts linear layers to convert the 2-D discrete distance (implemented through bucketing)",
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+    {
+        "type": "page_number",
+        "text": "7774",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "image",
+        "img_path": "images/35671b573bf6ab9b3d1aaa4a0135314602f2ac360e439889814a9b5af1808c19.jpg",
+        "image_caption": [
+            "Layout Attention with Gaussian Biases",
+            "Figure 1: An illustration of our Layout Attention with Gaussian Biases (LAGaBi). For each query word, we first compute the spatial relationships of each keyword in a polar system centered on it. Then Gaussian kernels are employed to transform these spatial relationships into attention biases, which will revise the original query-key attention distribution. Such biases are shared across different Transformer layers. When using LAGaBi, we can encode the document layout structure without adding position embedding layers at the bottom of the network."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "between words into attention biases motivated by T5 (Raffel et al., 2020). Such relative attention biases have also been employed by LayoutLMv2 (Xu et al., 2021a) and LayoutLMv3 (Huang et al., 2022), yielding notable results. Different from them, we model the relative positions from a new perspective, i.e. introducing polar coordinates with distance and angle that are more efficient in representing relative spatial relationships. Furthermore, we employ the extremely lightweight Gaussian kernels to encode polar coordinates into attention biases, offering a more streamlined approach with fewer trainable parameters and aligning better with human intuition compared to linear layers.",
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3 Methodology",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Structured documents typically contain both textual words and layouts, where textual words denote the main content of the document, while layouts record the organizational form of text. Optical character recognition (OCR) as a classical technique for image document parsing can recognize the text as well as its locations. Formally, given a document  $\\mathcal{D}$ , OCR identifies the text words",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "$\\mathcal{W} = \\{w_i\\}_{i=1}^N$  and their associated layout positions  \n $\\mathcal{C} = \\{c_i\\}_{i=1}^N$ , where  $N$  is the number of words while  $c_i = [x_i^0, y_i^0, x_i^1, y_i^1]$  represents the left-top and right-bottom coordinates of the bounding box that contains the  $i_{th}$  word. An ideal model for document understanding should take both text words and their layout positions into consideration.",
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+    {
+        "type": "text",
+        "text": "The key to structured document comprehension, building upon the achievements of text understanding techniques such as Transformer (Vaswani et al., 2017), lies in effectively representing document layouts while remaining compatible with language representation. Therefore, there are two crucial problems that need to be solved: 1. How to model document layouts efficiently? 2. How to integrate the layouts into Transformer to guide textual semantic understanding of the document content?",
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+    },
+    {
+        "type": "text",
+        "text": "In this paper, we introduce a novel layout modeling method for structured document understanding, namely Layout Attention with Gaussian Biases (LAGaBi). As shown in Figure 1, it mainly consists of two parts: modeling relative positions with polar coordinates and layout attention with Gaussian biases. The former is responsible for modeling the 2-D relative positions between words by po",
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+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "7775",
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "lar coordinates which are capable of representing inter-word spatial relationships; The latter focuses on transforming polar coordinates into attention biases, which will modify the original semantic query-key attentions between words into a more suitable distribution that accurately captures the underlying layout structure of documents.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3.1 Spatial Relationships with Polar Coordinates",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "In a structured document, the relative spatial relationships between words are shown to be more important than their absolute coordinates, which can assist humans in better understanding pair-wise semantic dependencies. For example, tokens in the same line generally have stronger semantic associations with each other, while tokens that are farther away and on different lines are more difficult to form strong associations. Although some previous works (Powalski et al., 2021; Xu et al., 2021a; Li et al., 2021a) have proposed to model the relative distances, which acted as learnable attention biases, they still rely on learnable positional embeddings and only utilize the relative horizontal and vertical distances in Cartesian coordinate system.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Different from previous works, we propose to capture inter-word spatial relationships through polar coordinates, in which both orientations and distances can be preserved. For each query token, we can build a polar coordinate system centered at its position, and calculate the polar coordinates (spatial relationships) of its keys. More concretely, the position of the query token is regarded as the reference point (pole), and the horizontal direction in the current Cartesian coordinate system is set as the reference direction (polar axis) following the most common reading habit, i.e., left-to-right and top-to-down. Formally, given a query token with its 2-D coordinates  $c_{i}$  as the pole, the polar coordinates  $\\mathbf{u}_{ij} = (\\rho_{ij},\\theta_{ij})$  of the  $j_{th}$  key in this document page can be calculated as below:",
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+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\rho_ {i j} = \\sqrt {\\left(x _ {j} - x _ {i}\\right) ^ {2} + \\left(y _ {j} - y _ {i}\\right) ^ {2}} \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\theta_ {i j} = \\tan^ {- 1} \\left(\\left(y _ {j} - y _ {i}\\right) / \\left(x _ {j} - x _ {i}\\right)\\right) \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $\\rho_{ij} \\in [0,1]$  and  $\\theta_{ij} \\in [-\\pi / 2, \\pi / 2]$  denote the distance and angle (orientation) from the  $i_{th}$  token to the  $j_{th}$  token respectively, and  $(x_i, y_i)$  are the normalized coordinates of the top-left point of the  $i_{th}$  bounding box. For instance, in Figure 1, when taking \"REPORT\" as the reference point, the",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "spatial relationship between the words \"FORM\" and \"REPORT\" can be represented as a polar coordinate (0.064, 0), indicating a distance of 0.064 and an angle of 0 degrees, while that between the words \"YEAR\" and \"REPORT\" is (0.297, 1.432).",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3.2 Layout Attention with Gaussian Biases",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "How to use the essential relative spatial relationships to guide the model to perceive layout information is a problem worth exploring. Inspired by ALiBi (Press et al., 2021) and T5 bias (Raffel et al., 2020) that encode the 1-D relative position information as attention biases upon the query-key scores instead of positional embedding, we propose to revise attention scores/distribution with 2-D attention biases that integrate spatial relationships. Specifically, the attention score in a single-head self-attention can be modified as:",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\na _ {i j} = \\frac {\\exp \\left(\\mathbf {q} _ {i} \\mathbf {k} _ {j} ^ {T} / \\sqrt {d _ {k}} + \\alpha (\\mathbf {g} \\left(\\mathbf {u} _ {i j}\\right) - 1)\\right)}{\\sum_ {j = 1} ^ {N} \\exp \\left(\\mathbf {q} _ {i} \\mathbf {k} _ {j} ^ {T} / \\sqrt {d _ {k}} + \\alpha (\\mathbf {g} \\left(\\mathbf {u} _ {i j}\\right) - 1)\\right)} \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+            380,
+            880,
+            434
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathbf{q}_i$  is the  $i_{th}$  query vector,  $\\mathbf{q}_i$  is the  $j_{th}$  key vector, and  $d_k$  is the dimension of the attention head.  $\\mathbf{g}(\\mathbf{u}_{ij})$  denotes the attention biases, which is derived from the 2-D Gaussian kernel with learnable parameters based on polar coordinates  $\\mathbf{u}$  indicating spatial relationships. The Gaussian kernel ensures that words farther within the document are assigned a smaller layout score. Therefore, by incorporating a reversed term  $(\\mathbf{g}(\\mathbf{u}_{ij}) - 1)$ , we can significantly penalize the attention scores of query-key pairs that are farther, while making only slight revisions to the scores of closer pairs.  $\\alpha$  is a hyper-parameter that makes a trade-off between semantic association and spatial dependency. It denotes how much the spatial relationship between the key and the query contributes to their semantic association. In particular, a convenient formula of  $\\mathbf{g}(\\mathbf{u})$  is:",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {g} (\\mathbf {u}) = e x p \\left(- \\frac {1}{2} (\\mathbf {u} - \\boldsymbol {\\mu}) ^ {T} \\boldsymbol {\\Sigma} ^ {- 1} (\\mathbf {u} - \\boldsymbol {\\mu})\\right) \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $\\boldsymbol{\\Sigma}$  and  $\\pmb{\\mu}$  are learnable  $2\\times 2$  and  $2\\times 1$  covariance matrix and mean vector of a Gaussian kernel, respectively. We further restrict the covariances to have diagonal form, resulting in  $2\\times 2$  parameters per kernel for each attention head. Note that the Gaussian kernels are different across different attention heads, but are shared across different self-attention layers. Thus, there is a total of  $2\\times$ $2\\times N_{\\mathrm{heads}}$  learnable parameters for our attention biases, where  $N_{\\mathrm{heads}}$  denotes the attention head",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "7776",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "number in each self-attention layer. For example, taking the RoBERTa base as the backbone, there are 48 parameters that need to be learned. Notably, we only include layout information in the keys and queries but not in the values, ensuring that the text semantics are not corrupted.",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Properties of our LAGaBi: (1) It is easy to implement and can be adapted to any transformer-based model without changing its structure. (2) The position embedding layer is discarded, and there are very few parameters to learn, which can be done during the fine-tuning stage, so it is quite efficient. (3) It decouples layout and text understanding, allowing the potential of language models to be fully exploited in structured document understanding.",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4 Experiments",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.1 Datasets",
+        "text_level": 1,
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "Pre-training Dataset. Following LayoutLM (Xu et al., 2020), we also pre-train our model using the IIT-CDIP Test Collection 1.0 (Lewis et al., 2006), which is a large-scale dataset with over 11 million scanned document images. Only 1 million of them are used for fast pre-training. We pre-process each document page using Tesseract $^2$ , an open-source OCR engine, to retrieve the textual contents as well as their layouts. We normalize the coordinates of each token to integers in the range of 0 to 1000 and add an empty bounding box  $[0,0,0,0]$  to the special tokens [CLS], [SEP], and [PAD].",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Fine-tuning Datasets. We evaluate our method on both monolingual (English) and multilingual document information extraction datasets listed below. FUNSD (Jaume et al., 2019) is a form dataset that uses forms to extract and organize textual information. It contains 199 documents, 149 of which are for training and 50 of which are for testing. CORD (Park et al., 2019) is a dataset for receipt key information extraction that includes 800, 100, and 100 receipts for training, validating, and testing, respectively. XFUND (Xu et al., 2021b) is a multilingual version of FUNSD with 8 languages, each language containing 199 instances (149 for training and 50 for testing) as FUNSD.",
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+        "page_idx": 4
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+    {
+        "type": "text",
+        "text": "4.2 Implementation Details",
+        "text_level": 1,
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Our approach is model-agnostic and language-independent, which can be applied to a range of transformer-based models. In this paper, we have evaluated our method based on three kinds",
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "of baselines: 1) monolingual models (BERT (Kenton and Toutanova, 2019) and RoBERTa (Liu et al., 2019)), 2) multilingual model (InfoXLM (Chi et al., 2020)), and 3) document understanding models (LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022)). Gaussian kernels will be included in the self-attention blocks of each model, which describe the relative spatial relationship between tokens.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Pre-training. We only conduct pre-training tasks for the two monolingual models: BERT+LAGaBi and RoBERTa+LAGaBi. We initialize the weight of them with the corresponding baselines, except the Gaussian kernels. The parameters of the Gaussian kernels, namely covariance matrixes and mean vectors, are randomly initialized. Both models are simply supervised by masked language modeling (MLM) loss during pre-training. Adam optimizer (Kingma and Ba, 2014) is adopted with a learning rate of  $5e - 5$ , weight decay of  $1e - 2$  and  $(\\beta_{1},\\beta_{2}) = (0.9,0.999)$ . The batch size is set to 128 and all the two models are trained for 200,000 steps on 8 NVIDIA v100 32GB GPUs.",
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+    },
+    {
+        "type": "text",
+        "text": "Fine-tuning. In this paper, we mainly focus on the document understanding task of semantic entity labeling, which aims at assigning each semantic entity a BIO label. We add a token-level classification layer upon the base models (including monolingual, multilingual, and document understanding models) to predict the BIO labels for this task. Word-level F1 score is adopted as the evaluation metric. The fine-tuning process takes 2000 steps using a batch size of 16 and the Adam optimizer with a learning rate of 5e-5 for FUNSD and 7e-5 for CORD and XFUND. Fine-tuning configurations for document understanding models follow their official releases. Hyper-parameter  $\\alpha$  is set to 4 for all experiments, which has been tuned on the CORD's val set.",
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+    {
+        "type": "text",
+        "text": "4.3 Experimental Results",
+        "text_level": 1,
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.3.1 Performance on monolingual datasets",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 4
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+    {
+        "type": "text",
+        "text": "We first evaluate our method on the monolingual form and receipt understanding datasets. From the results shown in Table 1, we can observe that:",
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+    },
+    {
+        "type": "text",
+        "text": "(1) The lightweight LAGaBi enables simple integration with language models, allowing them to effectively process structured documents. For example, without any pre-training on document data, RoBERTa+LAGaBi has achieved  $84.84\\%$  and  $95.97\\%$  F1 scores on FUNSD and CORD, surpassing baseline model RoBERTa by  $18.38\\%$  and",
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+        "page_idx": 4
+    },
+    {
+        "type": "page_footnote",
+        "text": "$^{2}$ https://github.com/tesseract-ocr/tesseract",
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+        "page_idx": 4
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+    {
+        "type": "page_number",
+        "text": "7777",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/a49f32eac86de81824f97348bb3cf3c88611aa46cdd732b3e1d021d01591f33f.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>#Parameters</td><td>Pre-training</td><td>Modality</td><td>FUNSD</td><td>CORD</td></tr><tr><td>BERT (Kenton and Toutanova, 2019)</td><td>110M</td><td>-</td><td>T</td><td>60.26</td><td>89.68</td></tr><tr><td>RoBERTa (Liu et al., 2019)</td><td>125M</td><td>-</td><td>T</td><td>66.48</td><td>93.54</td></tr><tr><td>BROS (Hong et al., 2022)</td><td>110M</td><td>11M</td><td>T+L</td><td>81.21</td><td>-</td></tr><tr><td>FormNet (Lee et al., 2022)</td><td>217M</td><td>11M</td><td>T+L</td><td>84.69</td><td>-</td></tr><tr><td>LiLT (Wang et al., 2022a)</td><td>131M</td><td>11M</td><td>T+L</td><td>88.41</td><td>96.07</td></tr><tr><td>BERT+LAGaBi(w/o pre-train)</td><td>110M+48</td><td>-</td><td>T+L</td><td>74.14 (+13.88)</td><td>93.44 (+3.76)</td></tr><tr><td>BERT+LAGaBi(w/ pre-train)</td><td>110M+48</td><td>1M</td><td>T+L</td><td>87.27 (+27.01)</td><td>95.82 (+6.14)</td></tr><tr><td>RoBERTa+LAGaBi(w/o pre-train)</td><td>125M+48</td><td>-</td><td>T+L</td><td>84.84 (+18.36)</td><td>95.97 (+2.43)</td></tr><tr><td>RoBERTa+LAGaBi(w/ pre-train)</td><td>125M+48</td><td>1M</td><td>T+L</td><td>89.15 (+22.67)</td><td>96.56 (+3.02)</td></tr><tr><td>LayoutLM (Xu et al., 2020)</td><td>160M</td><td>11M</td><td>T+L+I</td><td>79.27</td><td>94.31*</td></tr><tr><td>LayoutLMv2 (Xu et al., 2021a)</td><td>200M</td><td>11M</td><td>T+L+I</td><td>82.70</td><td>94.95</td></tr><tr><td>StrucTexT (Li et al., 2021c)</td><td>107M</td><td>11M</td><td>T+L+I</td><td>83.09</td><td>-</td></tr><tr><td>DocFormer (Appalaraju et al., 2021)</td><td>183M</td><td>11M</td><td>T+L+I</td><td>83.34</td><td>96.33</td></tr><tr><td>LayoutLMv3 (Huang et al., 2022)</td><td>133M</td><td>11M</td><td>T+L+I</td><td>90.29</td><td>96.56</td></tr><tr><td>LayoutLM+LAGaBi(w/o pre-train)</td><td>160M+48</td><td>-</td><td>T+L+I</td><td>87.77 (+8.10)</td><td>94.93(+0.62)</td></tr><tr><td>LayoutLMv2+LAGaBi(w/o pre-train)</td><td>200M+48</td><td>-</td><td>T+L+I</td><td>88.16 (+5.49)</td><td>97.05(+2.10)</td></tr><tr><td>LayoutLMv3+LAGaBi(w/o pre-train)</td><td>133M+48</td><td>-</td><td>T+L+I</td><td>91.00 (+0.71)</td><td>97.05 (+0.49)</td></tr></table>",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Table 1: Performance on FUNSD and CORD for monolingual structured document understanding. \"T/L/I\" denotes the \"text/layout/image\" modality. (+x) denotes the gain in F1 score compared to base model, while * show the result from our re-implementation. All the F1 scores in percentage (%) are reported.",
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+    },
+    {
+        "type": "text",
+        "text": "$2.43\\%$  respectively. RoBERTa+LAGaBi also outperforms several representative document understanding models such as LayoutLM (Xu et al., 2020) and LayoutLMv2 (Xu et al., 2021a). The results show that LAGaBi is a powerful method for capturing essential layout features, allowing language models to be easily extended to adapt to structured document understanding tasks.",
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "(2) Pre-training brings profits. After pre-training on 1 million unlabeled document data, our method exhibits extra improvements. RoBERTa+LAGaBi with pre-train surpasses all other approaches except LayoutLMv3 (Huang et al., 2022). While there is still a minor difference on FUNSD between our model and LayoutLMv3, our method is significantly easier to implement, introducing only 48 extra learnable parameters to the vanilla Transformers structure, making it more computationally efficient and flexible.",
+            "(3) The LAGaBi could also be seamlessly coupled with other layout embedding and multimodal-based document understanding models, improving their performance even further. Performance improvements on LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a) and LayoutLmv3 (Huang et al., 2022)) are obvious, with F1 score gains of  $8.10\\%$ $5.49\\%$ $0.71\\%$  on FUNSD and  $0.62\\%$ $2.10\\%$ $0.49\\%$  on CORD. The process"
+        ],
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "is particularly efficient since it only requires fine-tuning based on the published pre-trained weights rather than pre-training from scratch. Furthermore, by combining LAGaBi with the top-performing LayoutLMv3 model, we achieve new state-of-the-art results on both FUNSD and CORD datasets.",
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+    },
+    {
+        "type": "text",
+        "text": "4.3.2 Performance on multilingual dataset",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Following the multi-lingual LayoutXLM (Xu et al., 2021b) and LiLT (Wang et al., 2022a), we also evaluate our method based on InfoXLM (Chi et al., 2020) on three sub-tasks: language-specific fine-tuning, multi-task fine-tuning, and zero-shot transfer learning. We first perform fine-tuning based on the Gaussian kernels with random initialization. For a fair comparison, following LiLT, we also adopt the pre-trained Gaussian kernels for further fine-tuning, and we employ the Gaussian kernels in RoBERTa+LAGaBi which have been pre-trained on 1M monolingual document data in Sec 4.3.1. Results on XFUND are shown in Table 2.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "LAGaBi is also valid in multilingual scenarios, allowing multilingual language models to understand structured documents and outperform existing best-performing methods. LAGaBi can largely increase the performance of the multilingual language model InfoXLM on all three tasks, regardless of whether the Gaussian kernels are",
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+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "7778",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "table",
+        "img_path": "images/b79103ec84c7f26479f4c5c306f05f2d4a6118e2737e47ba9a4d5a96f8d31072.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Task</td><td rowspan=\"2\">Model</td><td rowspan=\"2\">Pre-train Data size &amp; language</td><td rowspan=\"2\">FUNSD EN</td><td colspan=\"7\">XFUND</td><td rowspan=\"2\">Avg.</td></tr><tr><td>ZH</td><td>JA</td><td>ES</td><td>FR</td><td>IT</td><td>DE</td><td>PT</td></tr><tr><td rowspan=\"6\">Language-specific</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>87.74</td><td>77.61</td><td>61.05</td><td>67.43</td><td>66.87</td><td>68.14</td><td>68.18</td><td>70.47</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>88.68</td><td>78.65</td><td>62.30</td><td>70.15</td><td>67.51</td><td>70.63</td><td>70.08</td><td>72.07</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>89.24</td><td>79.21</td><td>75.50</td><td>79.02</td><td>80.82</td><td>82.22</td><td>79.03</td><td>80.56</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>89.38</td><td>79.64</td><td>79.11</td><td>79.53</td><td>83.76</td><td>82.31</td><td>82.20</td><td>82.51</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>89.38</td><td>84.01</td><td>78.06</td><td>82.80</td><td>84.40</td><td>84.95</td><td>83.25</td><td>83.78</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>89.65</td><td>84.73</td><td>77.51</td><td>83.86</td><td>85.19</td><td>83.89</td><td>83.47</td><td>84.06</td></tr><tr><td rowspan=\"6\">Zero-shot</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>41.44</td><td>30.23</td><td>30.55</td><td>37.10</td><td>27.67</td><td>32.86</td><td>39.36</td><td>38.24</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>44.08</td><td>36.03</td><td>31.02</td><td>40.21</td><td>28.80</td><td>35.87</td><td>45.02</td><td>41.19</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>60.19</td><td>47.15</td><td>45.65</td><td>57.57</td><td>48.46</td><td>52.52</td><td>53.90</td><td>55.61</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>61.52</td><td>51.84</td><td>51.01</td><td>59.23</td><td>53.71</td><td>60.13</td><td>63.25</td><td>60.61</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>47.91</td><td>46.13</td><td>48.98</td><td>54.84</td><td>47.40</td><td>53.23</td><td>58.82</td><td>55.08</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>39.88</td><td>35.77</td><td>44.82</td><td>54.45</td><td>46.72</td><td>51.64</td><td>56.37</td><td>51.73</td></tr><tr><td rowspan=\"6\">Multi-task</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.33</td><td>88.30</td><td>77.86</td><td>62.23</td><td>70.35</td><td>68.14</td><td>71.46</td><td>67.26</td><td>71.49</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>65.38</td><td>87.41</td><td>78.55</td><td>59.79</td><td>70.57</td><td>68.26</td><td>70.55</td><td>67.96</td><td>71.06</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.24</td><td>89.73</td><td>79.64</td><td>77.98</td><td>81.73</td><td>82.10</td><td>83.22</td><td>82.41</td><td>82.01</td></tr><tr><td>LiLT (2022a)</td><td>1M-Mono</td><td>85.74</td><td>90.47</td><td>80.88</td><td>83.40</td><td>85.77</td><td>87.92</td><td>87.69</td><td>84.93</td><td>85.85</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>86.67</td><td>90.90</td><td>86.49</td><td>81.74</td><td>86.44</td><td>87.74</td><td>88.18</td><td>86.72</td><td>86.89</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>86.35</td><td>92.00</td><td>86.86</td><td>81.50</td><td>87.00</td><td>87.96</td><td>87.58</td><td>87.24</td><td>87.22</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Table 2: The performance on FUNSD and XFUND with different settings, including language-specific fine-tuning (fine-tuning on X, testing on X), zero-shot transfer (fine-tuning on FUNSD, testing on X), and multi-task fine-tuning (fine-tuning on all 8 languages, testing on X). \"1M-Mono\" denotes 1 million monolingual (English) documents used for pre-training, while \"30M-Multi\" is the multilingual version. All the  $F_1$  scores in percentage (\\%) are reported.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "pre-trained or not. InfoXLM+LAGaBi using the pre-trained Gaussian kernels outperforms the top-performing method LiLT on both language-specific and multi-task fine-tuning tasks, with average F1 scores of  $84.06\\%$  and  $87.22\\%$ , demonstrating the efficacy of LAGaBi in multilingual scenarios. On the zero-shot transfer learning task, LAGaBi fell slightly behind its counterparts. This may be due to the inherent gaps between different languages, such as differences in reading order and semantic density. For example, English usually uses spaces to separate words and has uneven word lengths, while Chinese appears as a tighter sequence with smaller semantic units (i.e., characters). Such layout knowledge learned from a specific language shows limited contributions to other languages. This phenomenon also proves the effectiveness of LAGaBi in modeling layouts, i.e. it actually has acquired the layout knowledge for a specific language after fine-tuning on the corresponding data.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "4.4 Ablation Studies",
+        "text_level": 1,
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "To investigate the impacts of our learnable Gaussian kernels and polar coordinates, we have con",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "duced extensive ablation experiments based on several RoBERTa variants equipped with different layout encoding mechanisms (e.g. layout embedding layers, linear bias layers, and fixed/learnable Gaussian Kernels), and spatial relationships (e.g. distance, angle, and 2D-xy distance). All the variants are evaluated without further pre-training, and only fine-tuned for 2000 steps on FUNSD and CORD. Results on FUNSD's test set and CORD's validation set are shown in Table 3. From the result, we can observe that LAGaBi with learnable Gaussian kernels and polar coordinates (#8) can significantly outperform the baseline (#1) and the model with linear layout embedding layers (#2), indicating that models encoding layouts as attention biases are superior to layout embedding-based methods.",
+        "bbox": [
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Impact of the Gaussian kernels. To study the effects of various methods for converting polar coordinates to attention biases, we compared linear layers (#3), fixed Gaussian kernels (#4), and learnable Gaussian kernels (#8). The results demonstrate that linear layers are far less effective than Gaussian kernels, which is likely due to the fact that the Gaussian kernels are more in line with human",
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "7779",
+        "bbox": [
+            480,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/acbeb1cef02eb76200a1cf61c2bc39a5fa2106feb98842a9778acc11239fe0e9.jpg",
+        "image_caption": [
+            "Figure 2: Visualization of the attention maps. The word-word attention scores are obtained by aggregating token-level attention in the last layer of transformers. We also annotate the position and order of each word in the original document page, and the attention scores are rounded to two decimal places for better visualization."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            126,
+            86,
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+            260
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/0161785d95db7d0dfc41b3c7d76f5f37cd54d1afd9fc2ad28f21c9d1982ea513.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
+            364,
+            86,
+            608,
+            266
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/6989db1cdeefe9b10f61d6b4d16edecf0e49d27bce976180af2b668cd3820c94.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
+            630,
+            86,
+            872,
+            266
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "table",
+        "img_path": "images/b18acf3111c30e7ed338078dee2896a079a2d1df2123a6b6f932c5979bd1f359.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Task</td><td>#</td><td>Ablation Strategy</td><td>FUNSD</td><td>CORD</td></tr><tr><td></td><td>1</td><td>RoBERTa(baseline)</td><td>66.48</td><td>92.29</td></tr><tr><td></td><td>2</td><td>+ embedding layers</td><td>69.59</td><td>92.67</td></tr><tr><td rowspan=\"2\">Impact of Gaussian</td><td>3</td><td>+ linear bias layers</td><td>76.32</td><td>93.00</td></tr><tr><td>4</td><td>+ fixed kernels</td><td>83.81</td><td>93.00</td></tr><tr><td rowspan=\"3\">Impact of Polar-Coor.</td><td>5</td><td>+ Euclidean distance</td><td>73.05</td><td>93.72</td></tr><tr><td>6</td><td>+ Angle</td><td>84.48</td><td>94.70</td></tr><tr><td>7</td><td>+ 2D-xy distance</td><td>79.85</td><td>94.21</td></tr><tr><td></td><td>8</td><td>+ LAGaBi</td><td>84.84</td><td>94.77</td></tr></table>",
+        "bbox": [
+            124,
+            346,
+            478,
+            512
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "intuition than linear layers. Learnable Gaussian kernels (#8) also achieve better performance than fixed Gaussian kernels whose mean is 0 and variance is 1 (#4), since the learnable Gaussian kernels enjoy better flexibility to adapt to different formats. Impact of the polar coordinates. Polar coordinates, which consist of two elements: distance and angle, is a typical technique for describing spatial relationships. We analyzed the effects of distance (#5) and angle (#6), as well as the classical 2-D relative horizontal and vertical distances proposed by TITL (Powalski et al., 2021) (#7). The results suggest that the model with angle information (#6) is more effective than the model with distance information (#5 and #7). We hypothesize that this is because angles are less impacted by size scaling than distances, but they are more sensitive to location changes. Furthermore, LAGaBi achieves much better performance than the model that employs horizontal and vertical distances, which further reveals the superiority of polar coordinates.",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "4.5 Analysis",
+        "text_level": 1,
+        "bbox": [
+            507,
+            349,
+            621,
+            363
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Impact of  $\\alpha$ . Hyper-parameter  $\\alpha$  makes a tradeoff between semantic and layout contributions when computing pair-wise attention scores in our LABaBi, which is important. We conduct several experiments with different  $\\alpha$  settings to study the impact of  $\\alpha$  based on RoBERTa without any further pre-training.  $F_{1}$  scores on CORD's validation set are listed in Table 4, showing the model achieves its best performance when  $\\alpha = 4$ .",
+        "bbox": [
+            507,
+            370,
+            882,
+            514
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "table",
+        "img_path": "images/f1704fbeee8bb400f5d4443695270528c61f7682150762795336edf3ebd405f7.jpg",
+        "table_caption": [
+            "Table 3: Ablation studies on the effectiveness of the learnable Gaussian kernels and the polar coordinates."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>α =</td><td>0</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr><tr><td>CORD</td><td>92.29</td><td>92.82</td><td>93.08</td><td>93.46</td><td>94.77</td><td>94.39</td></tr></table>",
+        "bbox": [
+            522,
+            527,
+            870,
+            573
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Table 4: Impact of different  $\\alpha$  settings. All the scores are from CORD's validation set.",
+        "bbox": [
+            507,
+            583,
+            882,
+            612
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Visualization analysis. We visualize the word-level attention maps of the baseline RoBERTa and our RoBERTa+LAGaBi. Due to space limitations, in this paper, we only show the attention maps of the first 8 words in the input sequence. The case in Figure 2 is from the test set of FUNSD. As shown in Figure 2, the RoBERTa incorrectly associates the character \"R.\" with all the words, while most other words are treated as unrelated. According to the attention map in the RoBERTa+LAGaBi, greater attention scores arise between \"R.\" and \"F.\", \"J.\" and \"D.\", all of which are placed in the signature area, whereas attention scores between remote irrelevant words such as \"DATE\" and \"STRU\" are zeros. This demonstrates that LAGaBi indeed learns more accurate semantic associations by incorporating layout information. More examples and detailed analysis can be seen in Appendix.A.",
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "7780",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "5 Conclusion",
+        "text_level": 1,
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+        "text": "In this paper, we propose a model-agnostic and language-independent method that leverages Layout Attention with Gaussian Biases to encode the relative spatial positions for structured document understanding (SDU). Specifically, we first model the inter-word spatial relationships using polar coordinates. Then the query-key attention scores are revised by the Gaussian biases that are related to their spatial relationships. Our method can be applied to a series of Transformer-based models with extremely few parameters, improving their performance for SDU tasks. Experiments based on six transformer-based SDU models and three monolingual/multilingual benchmarks fully demonstrate the effectiveness of our proposal. This research provides new ideas for structured document understanding tasks, which are expected to promote the efficient development of document intelligence.",
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+    {
+        "type": "text",
+        "text": "Limitations",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Despite the superior performance exhibited by LAGaBi, it does have some limitations. Firstly, in our experiments with the LayoutLM series that integrate multi-modal features, LAGaBi was only fine-tuned for validation without pre-training. We believe that leveraging multi-modal pre-training could further improve LAGaBi's performance based on LayoutLM, and this will be explored in future investigations. Secondly, although we have empirically demonstrated the effectiveness of polar coordinates and Gaussian distribution in layout learning, our motivation is driven by a simple intuition rather than rigorous mathematical proof.",
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+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "We sincerely thank all the anonymous reviewers for their valuable comments and suggestions.",
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+    },
+    {
+        "type": "text",
+        "text": "References",
+        "text_level": 1,
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+            "Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. 2020. Layoutm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1192-1200.",
+            "Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, and Furu Wei. 2021b. Layoutxlm: Multimodal pre-training for multilingual visually-rich document understanding. arXiv preprint arXiv:2104.08836."
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+        "type": "text",
+        "text": "A Appendix",
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+        "text": "Figure 3 in this section shows the attention distribution of four additional examples on FUNSD. From all the examples, it can be observed that the RoBERTa model typically treats each word in isolation, while RoBERTa+LAGaBi can learn the more accurate correlations between different words based on their layout relationships.",
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+        "text": "We also visualize the word-level attention maps of LayoutLMv3 and LayoutLMv3+LAGaBi. LayoutLMv3 is currently the top-performing method for structured document understanding, which utilizes the 2-D relative positions through linear attention biases. From the attention maps shown in Figure 4, we can observe that LayoutLMv3+LAGaBi refers more to layout information when modeling the inter-word semantic correlations, while LayoutLMv3 is relatively independent. For example, in the third sample of Figure 4, LAGaBi learns more dense associations among \"Tiers\", \"II.\", and \"&\" in the neighborhood than LayoutLMv3, which is in line with human intuition.",
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2023/Beyond Layout Embedding_ Layout Attention with Gaussian Biases for Structured Document Understanding/fc2d1e06-8d86-4d7d-9ced-4589565dfec7_model.json

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+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "{zhuqian,hanxueai,pengshuyuan,leishuo,dengchao,fengjunlan}@chinamobile.com"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.261,
+                0.253,
+                0.341,
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+            "angle": 0,
+            "content": "Abstract"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.144,
+                0.28,
+                0.461,
+                0.75
+            ],
+            "angle": 0,
+            "content": "Effectively encoding layout information is a central problem in structured document understanding. Most existing methods rely heavily on millions of trainable parameters to learn the layout features of each word from Cartesian coordinates. However, two unresolved questions remain: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are massive learnable parameters truly necessary for layout representation? In this paper, we address these questions by proposing Layout Attention with Gaussian Biases (LAGaBi): Firstly, we find that polar coordinates provide a superior choice over Cartesian coordinates as they offer a measurement of both distance and angle between word pairs, capturing relative positions more effectively. Furthermore, by feeding the distances and angles into 2-D Gaussian kernels, we model intuitive inductive layout biases, i.e., the words closer within a document should receive more attention, which will act as the attention biases to revise the textual attention distribution. LAGaBi is model-agnostic and language-independent, which can be applied to a range of transformer-based models, such as the text pre-training models from the BERT series and the LayoutLM series that incorporate visual features. Experimental results on three widely used benchmarks demonstrate that, despite reducing the number of layout parameters from millions to 48, LAGaBi achieves competitive or even superior performance. Our code is available on GitHub<sup>1</sup>."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.763,
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+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.788,
+                0.489,
+                0.884
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+            "angle": 0,
+            "content": "Structured document understanding (SDU) has gained significant research attention in the field of intelligent document processing (Park et al., 2019; Jaume et al., 2019; Han et al., 2023). It focuses on extracting layout structures and contents from scanned or digital documents, leading to enhanced"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.254,
+                0.882,
+                0.286
+            ],
+            "angle": 0,
+            "content": "performance in several downstream tasks like form comprehension and receipt understanding."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.289,
+                0.885,
+                0.787
+            ],
+            "angle": 0,
+            "content": "Unlike conventional text understanding (Liu et al., 2019; Vaswani et al., 2017; Kenton and Toutanova, 2019), SDU goes beyond comprehending serialized text and requires the ability to interpret documents with diverse layouts (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). Documents with varying layouts often contain text fields positioned in different ways. To take advantage of existing pre-trained language models, early methods (Xu et al., 2020; Li et al., 2021a,c; Appalaraju et al., 2021; Chi et al., 2020) propose to directly add 2-D position embedding to the word embedding for each word as input for Transformer. The position embedding encode the 2-D absolute coordinates \\((x_0, y_0, x_1, y_1)\\) of each word in the document through multi position encoding layers, where \\((x_0, y_0)\\) represents the upper left point and \\((x_1, y_1)\\) represents the lower right point of the bounding box for each word. Some researches (Powalski et al., 2021; Hong et al., 2022; Lee et al., 2022; Xu et al., 2021a; Huang et al., 2022) further proposed that absolute positions are inefficient for representing the spatial relationships between words. They employ relative positions between words to encode spatial relationships. For example, Hong et al.(2022) map the relative positions into embeddings, which are then multiplied with the semantic embedding of the word to calculate inter-word layout scores. This score is incorporated into the self-attention layers to combine semantic and layout features."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.791,
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+            "angle": 0,
+            "content": "Despite the significant progress made, we argue that current methods, whether based on absolute or relative positioning, heavily rely on a large number of trainable parameters for position embeddings from Cartesian coordinates, often comprising millions of parameters. This raises two unexplored questions: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.14,
+                0.892,
+                0.398,
+                0.906
+            ],
+            "angle": 0,
+            "content": "*Junlan Feng is the corresponding author."
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.138,
+                0.906,
+                0.373,
+                0.918
+            ],
+            "angle": 0,
+            "content": "<sup>1</sup> https://github.com/zxilucky/LAGaBi"
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.138,
+                0.892,
+                0.398,
+                0.918
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+                0.521,
+                0.941
+            ],
+            "angle": 0,
+            "content": "7773"
+        },
+        {
+            "type": "footer",
+            "bbox": [
+                0.218,
+                0.946,
+                0.78,
+                0.958
+            ],
+            "angle": 0,
+            "content": "Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7773-7784"
+        },
+        {
+            "type": "footer",
+            "bbox": [
+                0.278,
+                0.958,
+                0.72,
+                0.972
+            ],
+            "angle": 0,
+            "content": "December 6-10, 2023 ©2023 Association for Computational Linguistics"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.493,
+                0.231
+            ],
+            "angle": 0,
+            "content": "massive learnable layout parameters truly necessary? Concerning the former, various coordinate systems exist, including Cartesian, polar, and spherical coordinates, yet previous research has solely focused on Cartesian coordinates. Regarding the latter, it is intuitively expected that words closer within a document should receive more attention. However, this simple inductive bias may not be effectively learned solely through gradient updates."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.236,
+                0.492,
+                0.768
+            ],
+            "angle": 0,
+            "content": "In this paper, we present a unified investigation of the two aforementioned problems. Regarding the choice of coordinate systems, we find that polar coordinates offer a more efficient representation than Cartesian coordinates for expressing relative positions. By computing the differences in distance and angle between two words in polar space, polar coordinates outperform their Cartesian counterparts by providing extra angle information. For layout learning, we discover that layout modeling can be achieved by a specific distribution: words closer in space receive higher layout scores, eliminating the need for extra position embeddings. Combining these two choices, we propose LAGaBi (Layout Attention with Gaussian Biases). LAGaBi formulates pairwise spatial relationships between tokens using the distance and angle in the polar coordinate system. Moreover, the distance and angle are fed into a 2-D Gaussian distribution to output a layout score. We choose the Gaussian distribution because it guarantees that the layout score decreases when either the distance or angle variables increase, making it the most commonly used distribution for this purpose. The layout score is then incorporated into the original self-attention as attention bias, resulting in a revised distribution that considers both text and layout features. We introduce trainable Gaussian kernels to better align the semantic and layout scores at different scales, adding just \\(4 \\times\\) attention heads extra parameters. For instance, based on RoBERTa (Liu et al., 2019) with 12 attention heads, there are only 48 additional parameters that need to be learned for encoding layout features."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.775,
+                0.491,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Extensive experiments demonstrate that LAGaBi achieves remarkable performance on diverse SDU benchmarks, including FUNSD (Jaume et al., 2019), CORD (Park et al., 2019), and XFUND (Xu et al., 2021b), across both monolingual and multilingual scenarios. LAGaBi emerges as a versatile module that seamlessly integrates with transformer-based language models, such as BERT (Kenton and Toutanova, 2019), RoBERTa, and InfoXLM (Chi"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.887,
+                0.231
+            ],
+            "angle": 0,
+            "content": "et al., 2020), empowering them to effectively process structured documents and achieve significant performance gains of up to 27.01 points. Additionally, LAGaBi can be incorporated into complex SDU models that leverage visual features, such as LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022), leading to further performance enhancements and establishing new state-of-the-art results."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "2 Related Works"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.275,
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+                0.612
+            ],
+            "angle": 0,
+            "content": "Significant progress has recently been made by using the Transformer-based pre-trained model (PTM) to learn the cross-modality interaction between textual and layout information, which has been demonstrated to be critical for structured document understanding (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). LayoutLM (Xu et al., 2020) modified the input of BERT (Kenton and Toutanova, 2019) by adding position embedding layers to encode word-level 2-D coordinates, while StructuralLM (Li et al., 2021a) proposed to encode segment-level positions. LiLT (Wang et al., 2022a) encoded text and layout using two different transformer layers separately and adopted bi-directional attention to fuse them. Besides, there are a series of works that use multi-modal transformers to model text, layout, and image simultaneously, such as SelfDoc (Li et al., 2021b), DocFormer (Appalaraju et al., 2021), StrucTexT (Li et al., 2021c), ERNIE-Layout (Peng et al., 2022), mmLayout (Wang et al., 2022b)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.614,
+                0.886,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Most of the above approaches encode the 2-D absolute positions, ignoring the critical relative spatial relationships between words that are essential to textual semantic understanding. Hong et al.(2022) proposed to encode the spatial relationships as relative position embeddings, which are then multiplied with the semantic embedding of the token to calculate inter-word layout scores. This score is incorporated into the self-attention to combine semantic and layout features. GeoLay-outLM (Luo et al., 2023) introduces geometric relations and brand-new geometric pre-training tasks in different levels for learning the geometric layout representation, whose geometric relations are largely dependent on some pre-defined manual rules. TITL (Powalski et al., 2021) encodes the relative positions between words in a simpler manner, and it adopts linear layers to convert the 2-D discrete distance (implemented through bucketing)"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "7774"
+        }
+    ],
+    [
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.386,
+                0.086,
+                0.601,
+                0.097
+            ],
+            "angle": 0,
+            "content": "Layout Attention with Gaussian Biases"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.129,
+                0.1,
+                0.87,
+                0.406
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.419,
+                0.884,
+                0.492
+            ],
+            "angle": 0,
+            "content": "Figure 1: An illustration of our Layout Attention with Gaussian Biases (LAGaBi). For each query word, we first compute the spatial relationships of each keyword in a polar system centered on it. Then Gaussian kernels are employed to transform these spatial relationships into attention biases, which will revise the original query-key attention distribution. Such biases are shared across different Transformer layers. When using LAGaBi, we can encode the document layout structure without adding position embedding layers at the bottom of the network."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.516,
+                0.49,
+                0.742
+            ],
+            "angle": 0,
+            "content": "between words into attention biases motivated by T5 (Raffel et al., 2020). Such relative attention biases have also been employed by LayoutLMv2 (Xu et al., 2021a) and LayoutLMv3 (Huang et al., 2022), yielding notable results. Different from them, we model the relative positions from a new perspective, i.e. introducing polar coordinates with distance and angle that are more efficient in representing relative spatial relationships. Furthermore, we employ the extremely lightweight Gaussian kernels to encode polar coordinates into attention biases, offering a more streamlined approach with fewer trainable parameters and aligning better with human intuition compared to linear layers."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
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+                0.264,
+                0.778
+            ],
+            "angle": 0,
+            "content": "3 Methodology"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.791,
+                0.491,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Structured documents typically contain both textual words and layouts, where textual words denote the main content of the document, while layouts record the organizational form of text. Optical character recognition (OCR) as a classical technique for image document parsing can recognize the text as well as its locations. Formally, given a document \\(\\mathcal{D}\\), OCR identifies the text words"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.515,
+                0.885,
+                0.628
+            ],
+            "angle": 0,
+            "content": "\\(\\mathcal{W} = \\{w_i\\}_{i=1}^N\\) and their associated layout positions  \n\\(\\mathcal{C} = \\{c_i\\}_{i=1}^N\\), where \\(N\\) is the number of words while \\(c_i = [x_i^0, y_i^0, x_i^1, y_i^1]\\) represents the left-top and right-bottom coordinates of the bounding box that contains the \\(i_{th}\\) word. An ideal model for document understanding should take both text words and their layout positions into consideration."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.629,
+                0.885,
+                0.79
+            ],
+            "angle": 0,
+            "content": "The key to structured document comprehension, building upon the achievements of text understanding techniques such as Transformer (Vaswani et al., 2017), lies in effectively representing document layouts while remaining compatible with language representation. Therefore, there are two crucial problems that need to be solved: 1. How to model document layouts efficiently? 2. How to integrate the layouts into Transformer to guide textual semantic understanding of the document content?"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.791,
+                0.887,
+                0.92
+            ],
+            "angle": 0,
+            "content": "In this paper, we introduce a novel layout modeling method for structured document understanding, namely Layout Attention with Gaussian Biases (LAGaBi). As shown in Figure 1, it mainly consists of two parts: modeling relative positions with polar coordinates and layout attention with Gaussian biases. The former is responsible for modeling the 2-D relative positions between words by po"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.521,
+                0.941
+            ],
+            "angle": 0,
+            "content": "7775"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.488,
+                0.198
+            ],
+            "angle": 0,
+            "content": "lar coordinates which are capable of representing inter-word spatial relationships; The latter focuses on transforming polar coordinates into attention biases, which will modify the original semantic query-key attentions between words into a more suitable distribution that accurately captures the underlying layout structure of documents."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.21,
+                0.414,
+                0.24
+            ],
+            "angle": 0,
+            "content": "3.1 Spatial Relationships with Polar Coordinates"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.248,
+                0.49,
+                0.488
+            ],
+            "angle": 0,
+            "content": "In a structured document, the relative spatial relationships between words are shown to be more important than their absolute coordinates, which can assist humans in better understanding pair-wise semantic dependencies. For example, tokens in the same line generally have stronger semantic associations with each other, while tokens that are farther away and on different lines are more difficult to form strong associations. Although some previous works (Powalski et al., 2021; Xu et al., 2021a; Li et al., 2021a) have proposed to model the relative distances, which acted as learnable attention biases, they still rely on learnable positional embeddings and only utilize the relative horizontal and vertical distances in Cartesian coordinate system."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.49,
+                0.49,
+                0.747
+            ],
+            "angle": 0,
+            "content": "Different from previous works, we propose to capture inter-word spatial relationships through polar coordinates, in which both orientations and distances can be preserved. For each query token, we can build a polar coordinate system centered at its position, and calculate the polar coordinates (spatial relationships) of its keys. More concretely, the position of the query token is regarded as the reference point (pole), and the horizontal direction in the current Cartesian coordinate system is set as the reference direction (polar axis) following the most common reading habit, i.e., left-to-right and top-to-down. Formally, given a query token with its 2-D coordinates \\( c_{i} \\) as the pole, the polar coordinates \\( \\mathbf{u}_{ij} = (\\rho_{ij},\\theta_{ij}) \\) of the \\( j_{th} \\) key in this document page can be calculated as below:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.177,
+                0.761,
+                0.488,
+                0.787
+            ],
+            "angle": 0,
+            "content": "\\[\n\\rho_ {i j} = \\sqrt {\\left(x _ {j} - x _ {i}\\right) ^ {2} + \\left(y _ {j} - y _ {i}\\right) ^ {2}} \\tag {1}\n\\]"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.171,
+                0.796,
+                0.488,
+                0.815
+            ],
+            "angle": 0,
+            "content": "\\[\n\\theta_ {i j} = \\tan^ {- 1} \\left(\\left(y _ {j} - y _ {i}\\right) / \\left(x _ {j} - x _ {i}\\right)\\right) \\tag {2}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.823,
+                0.49,
+                0.919
+            ],
+            "angle": 0,
+            "content": "where \\(\\rho_{ij} \\in [0,1]\\) and \\(\\theta_{ij} \\in [-\\pi / 2, \\pi / 2]\\) denote the distance and angle (orientation) from the \\(i_{th}\\) token to the \\(j_{th}\\) token respectively, and \\((x_i, y_i)\\) are the normalized coordinates of the top-left point of the \\(i_{th}\\) bounding box. For instance, in Figure 1, when taking \"REPORT\" as the reference point, the"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.165
+            ],
+            "angle": 0,
+            "content": "spatial relationship between the words \"FORM\" and \"REPORT\" can be represented as a polar coordinate (0.064, 0), indicating a distance of 0.064 and an angle of 0 degrees, while that between the words \"YEAR\" and \"REPORT\" is (0.297, 1.432)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.176,
+                0.862,
+                0.191
+            ],
+            "angle": 0,
+            "content": "3.2 Layout Attention with Gaussian Biases"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.196,
+                0.885,
+                0.372
+            ],
+            "angle": 0,
+            "content": "How to use the essential relative spatial relationships to guide the model to perceive layout information is a problem worth exploring. Inspired by ALiBi (Press et al., 2021) and T5 bias (Raffel et al., 2020) that encode the 1-D relative position information as attention biases upon the query-key scores instead of positional embedding, we propose to revise attention scores/distribution with 2-D attention biases that integrate spatial relationships. Specifically, the attention score in a single-head self-attention can be modified as:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.515,
+                0.381,
+                0.882,
+                0.435
+            ],
+            "angle": 0,
+            "content": "\\[\na _ {i j} = \\frac {\\exp \\left(\\mathbf {q} _ {i} \\mathbf {k} _ {j} ^ {T} / \\sqrt {d _ {k}} + \\alpha (\\mathbf {g} \\left(\\mathbf {u} _ {i j}\\right) - 1)\\right)}{\\sum_ {j = 1} ^ {N} \\exp \\left(\\mathbf {q} _ {i} \\mathbf {k} _ {j} ^ {T} / \\sqrt {d _ {k}} + \\alpha (\\mathbf {g} \\left(\\mathbf {u} _ {i j}\\right) - 1)\\right)} \\tag {3}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.436,
+                0.884,
+                0.71
+            ],
+            "angle": 0,
+            "content": "where \\(\\mathbf{q}_i\\) is the \\(i_{th}\\) query vector, \\(\\mathbf{q}_i\\) is the \\(j_{th}\\) key vector, and \\(d_k\\) is the dimension of the attention head. \\(\\mathbf{g}(\\mathbf{u}_{ij})\\) denotes the attention biases, which is derived from the 2-D Gaussian kernel with learnable parameters based on polar coordinates \\(\\mathbf{u}\\) indicating spatial relationships. The Gaussian kernel ensures that words farther within the document are assigned a smaller layout score. Therefore, by incorporating a reversed term \\((\\mathbf{g}(\\mathbf{u}_{ij}) - 1)\\), we can significantly penalize the attention scores of query-key pairs that are farther, while making only slight revisions to the scores of closer pairs. \\(\\alpha\\) is a hyper-parameter that makes a trade-off between semantic association and spatial dependency. It denotes how much the spatial relationship between the key and the query contributes to their semantic association. In particular, a convenient formula of \\(\\mathbf{g}(\\mathbf{u})\\) is:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.535,
+                0.72,
+                0.883,
+                0.75
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathbf {g} (\\mathbf {u}) = e x p \\left(- \\frac {1}{2} (\\mathbf {u} - \\boldsymbol {\\mu}) ^ {T} \\boldsymbol {\\Sigma} ^ {- 1} (\\mathbf {u} - \\boldsymbol {\\mu})\\right) \\tag {4}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.759,
+                0.885,
+                0.919
+            ],
+            "angle": 0,
+            "content": "where \\(\\boldsymbol{\\Sigma}\\) and \\(\\pmb{\\mu}\\) are learnable \\(2\\times 2\\) and \\(2\\times 1\\) covariance matrix and mean vector of a Gaussian kernel, respectively. We further restrict the covariances to have diagonal form, resulting in \\(2\\times 2\\) parameters per kernel for each attention head. Note that the Gaussian kernels are different across different attention heads, but are shared across different self-attention layers. Thus, there is a total of \\(2\\times\\) \\(2\\times N_{\\mathrm{heads}}\\) learnable parameters for our attention biases, where \\(N_{\\mathrm{heads}}\\) denotes the attention head"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+            "angle": 0,
+            "content": "7776"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.488,
+                0.181
+            ],
+            "angle": 0,
+            "content": "number in each self-attention layer. For example, taking the RoBERTa base as the backbone, there are 48 parameters that need to be learned. Notably, we only include layout information in the keys and queries but not in the values, ensuring that the text semantics are not corrupted."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
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+            ],
+            "angle": 0,
+            "content": "Properties of our LAGaBi: (1) It is easy to implement and can be adapted to any transformer-based model without changing its structure. (2) The position embedding layer is discarded, and there are very few parameters to learn, which can be done during the fine-tuning stage, so it is quite efficient. (3) It decouples layout and text understanding, allowing the potential of language models to be fully exploited in structured document understanding."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+                0.338,
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+            ],
+            "angle": 0,
+            "content": "4 Experiments"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.364,
+                0.229,
+                0.378
+            ],
+            "angle": 0,
+            "content": "4.1 Datasets"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.385,
+                0.489,
+                0.577
+            ],
+            "angle": 0,
+            "content": "Pre-training Dataset. Following LayoutLM (Xu et al., 2020), we also pre-train our model using the IIT-CDIP Test Collection 1.0 (Lewis et al., 2006), which is a large-scale dataset with over 11 million scanned document images. Only 1 million of them are used for fast pre-training. We pre-process each document page using Tesseract\\(^2\\), an open-source OCR engine, to retrieve the textual contents as well as their layouts. We normalize the coordinates of each token to integers in the range of 0 to 1000 and add an empty bounding box \\([0,0,0,0]\\) to the special tokens [CLS], [SEP], and [PAD]."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.578,
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+            ],
+            "angle": 0,
+            "content": "Fine-tuning Datasets. We evaluate our method on both monolingual (English) and multilingual document information extraction datasets listed below. FUNSD (Jaume et al., 2019) is a form dataset that uses forms to extract and organize textual information. It contains 199 documents, 149 of which are for training and 50 of which are for testing. CORD (Park et al., 2019) is a dataset for receipt key information extraction that includes 800, 100, and 100 receipts for training, validating, and testing, respectively. XFUND (Xu et al., 2021b) is a multilingual version of FUNSD with 8 languages, each language containing 199 instances (149 for training and 50 for testing) as FUNSD."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.815,
+                0.331,
+                0.83
+            ],
+            "angle": 0,
+            "content": "4.2 Implementation Details"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.835,
+                0.489,
+                0.898
+            ],
+            "angle": 0,
+            "content": "Our approach is model-agnostic and language-independent, which can be applied to a range of transformer-based models. In this paper, we have evaluated our method based on three kinds"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.883,
+                0.228
+            ],
+            "angle": 0,
+            "content": "of baselines: 1) monolingual models (BERT (Kenton and Toutanova, 2019) and RoBERTa (Liu et al., 2019)), 2) multilingual model (InfoXLM (Chi et al., 2020)), and 3) document understanding models (LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022)). Gaussian kernels will be included in the self-attention blocks of each model, which describe the relative spatial relationship between tokens."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.23,
+                0.885,
+                0.453
+            ],
+            "angle": 0,
+            "content": "Pre-training. We only conduct pre-training tasks for the two monolingual models: BERT+LAGaBi and RoBERTa+LAGaBi. We initialize the weight of them with the corresponding baselines, except the Gaussian kernels. The parameters of the Gaussian kernels, namely covariance matrixes and mean vectors, are randomly initialized. Both models are simply supervised by masked language modeling (MLM) loss during pre-training. Adam optimizer (Kingma and Ba, 2014) is adopted with a learning rate of \\(5e - 5\\), weight decay of \\(1e - 2\\) and \\((\\beta_{1},\\beta_{2}) = (0.9,0.999)\\). The batch size is set to 128 and all the two models are trained for 200,000 steps on 8 NVIDIA v100 32GB GPUs."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.456,
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+            ],
+            "angle": 0,
+            "content": "Fine-tuning. In this paper, we mainly focus on the document understanding task of semantic entity labeling, which aims at assigning each semantic entity a BIO label. We add a token-level classification layer upon the base models (including monolingual, multilingual, and document understanding models) to predict the BIO labels for this task. Word-level F1 score is adopted as the evaluation metric. The fine-tuning process takes 2000 steps using a batch size of 16 and the Adam optimizer with a learning rate of 5e-5 for FUNSD and 7e-5 for CORD and XFUND. Fine-tuning configurations for document understanding models follow their official releases. Hyper-parameter \\(\\alpha\\) is set to 4 for all experiments, which has been tuned on the CORD's val set."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.713,
+                0.725,
+                0.727
+            ],
+            "angle": 0,
+            "content": "4.3 Experimental Results"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.737,
+                0.866,
+                0.752
+            ],
+            "angle": 0,
+            "content": "4.3.1 Performance on monolingual datasets"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.759,
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+            ],
+            "angle": 0,
+            "content": "We first evaluate our method on the monolingual form and receipt understanding datasets. From the results shown in Table 1, we can observe that:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.807,
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+            ],
+            "angle": 0,
+            "content": "(1) The lightweight LAGaBi enables simple integration with language models, allowing them to effectively process structured documents. For example, without any pre-training on document data, RoBERTa+LAGaBi has achieved \\(84.84\\%\\) and \\(95.97\\%\\) F1 scores on FUNSD and CORD, surpassing baseline model RoBERTa by \\(18.38\\%\\) and"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.136,
+                0.904,
+                0.393,
+                0.918
+            ],
+            "angle": 0,
+            "content": "\\(^{2}\\)https://github.com/tesseract-ocr/tesseract"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
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+            ],
+            "angle": 0,
+            "content": "7777"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Model</td><td>#Parameters</td><td>Pre-training</td><td>Modality</td><td>FUNSD</td><td>CORD</td></tr><tr><td>BERT (Kenton and Toutanova, 2019)</td><td>110M</td><td>-</td><td>T</td><td>60.26</td><td>89.68</td></tr><tr><td>RoBERTa (Liu et al., 2019)</td><td>125M</td><td>-</td><td>T</td><td>66.48</td><td>93.54</td></tr><tr><td>BROS (Hong et al., 2022)</td><td>110M</td><td>11M</td><td>T+L</td><td>81.21</td><td>-</td></tr><tr><td>FormNet (Lee et al., 2022)</td><td>217M</td><td>11M</td><td>T+L</td><td>84.69</td><td>-</td></tr><tr><td>LiLT (Wang et al., 2022a)</td><td>131M</td><td>11M</td><td>T+L</td><td>88.41</td><td>96.07</td></tr><tr><td>BERT+LAGaBi(w/o pre-train)</td><td>110M+48</td><td>-</td><td>T+L</td><td>74.14 (+13.88)</td><td>93.44 (+3.76)</td></tr><tr><td>BERT+LAGaBi(w/ pre-train)</td><td>110M+48</td><td>1M</td><td>T+L</td><td>87.27 (+27.01)</td><td>95.82 (+6.14)</td></tr><tr><td>RoBERTa+LAGaBi(w/o pre-train)</td><td>125M+48</td><td>-</td><td>T+L</td><td>84.84 (+18.36)</td><td>95.97 (+2.43)</td></tr><tr><td>RoBERTa+LAGaBi(w/ pre-train)</td><td>125M+48</td><td>1M</td><td>T+L</td><td>89.15 (+22.67)</td><td>96.56 (+3.02)</td></tr><tr><td>LayoutLM (Xu et al., 2020)</td><td>160M</td><td>11M</td><td>T+L+I</td><td>79.27</td><td>94.31*</td></tr><tr><td>LayoutLMv2 (Xu et al., 2021a)</td><td>200M</td><td>11M</td><td>T+L+I</td><td>82.70</td><td>94.95</td></tr><tr><td>StrucTexT (Li et al., 2021c)</td><td>107M</td><td>11M</td><td>T+L+I</td><td>83.09</td><td>-</td></tr><tr><td>DocFormer (Appalaraju et al., 2021)</td><td>183M</td><td>11M</td><td>T+L+I</td><td>83.34</td><td>96.33</td></tr><tr><td>LayoutLMv3 (Huang et al., 2022)</td><td>133M</td><td>11M</td><td>T+L+I</td><td>90.29</td><td>96.56</td></tr><tr><td>LayoutLM+LAGaBi(w/o pre-train)</td><td>160M+48</td><td>-</td><td>T+L+I</td><td>87.77 (+8.10)</td><td>94.93(+0.62)</td></tr><tr><td>LayoutLMv2+LAGaBi(w/o pre-train)</td><td>200M+48</td><td>-</td><td>T+L+I</td><td>88.16 (+5.49)</td><td>97.05(+2.10)</td></tr><tr><td>LayoutLMv3+LAGaBi(w/o pre-train)</td><td>133M+48</td><td>-</td><td>T+L+I</td><td>91.00 (+0.71)</td><td>97.05 (+0.49)</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.391,
+                0.883,
+                0.435
+            ],
+            "angle": 0,
+            "content": "Table 1: Performance on FUNSD and CORD for monolingual structured document understanding. \"T/L/I\" denotes the \"text/layout/image\" modality. (+x) denotes the gain in F1 score compared to base model, while * show the result from our re-implementation. All the F1 scores in percentage (%) are reported."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.46,
+                0.489,
+                0.588
+            ],
+            "angle": 0,
+            "content": "\\(2.43\\%\\) respectively. RoBERTa+LAGaBi also outperforms several representative document understanding models such as LayoutLM (Xu et al., 2020) and LayoutLMv2 (Xu et al., 2021a). The results show that LAGaBi is a powerful method for capturing essential layout features, allowing language models to be easily extended to adapt to structured document understanding tasks."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.589,
+                0.489,
+                0.764
+            ],
+            "angle": 0,
+            "content": "(2) Pre-training brings profits. After pre-training on 1 million unlabeled document data, our method exhibits extra improvements. RoBERTa+LAGaBi with pre-train surpasses all other approaches except LayoutLMv3 (Huang et al., 2022). While there is still a minor difference on FUNSD between our model and LayoutLMv3, our method is significantly easier to implement, introducing only 48 extra learnable parameters to the vanilla Transformers structure, making it more computationally efficient and flexible."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.766,
+                0.49,
+                0.911
+            ],
+            "angle": 0,
+            "content": "(3) The LAGaBi could also be seamlessly coupled with other layout embedding and multimodal-based document understanding models, improving their performance even further. Performance improvements on LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a) and LayoutLmv3 (Huang et al., 2022)) are obvious, with F1 score gains of \\(8.10\\%\\) \\(5.49\\%\\) \\(0.71\\%\\) on FUNSD and \\(0.62\\%\\) \\(2.10\\%\\) \\(0.49\\%\\) on CORD. The process"
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.113,
+                0.589,
+                0.49,
+                0.911
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.46,
+                0.885,
+                0.556
+            ],
+            "angle": 0,
+            "content": "is particularly efficient since it only requires fine-tuning based on the published pre-trained weights rather than pre-training from scratch. Furthermore, by combining LAGaBi with the top-performing LayoutLMv3 model, we achieve new state-of-the-art results on both FUNSD and CORD datasets."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.574,
+                0.857,
+                0.59
+            ],
+            "angle": 0,
+            "content": "4.3.2 Performance on multilingual dataset"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.598,
+                0.885,
+                0.805
+            ],
+            "angle": 0,
+            "content": "Following the multi-lingual LayoutXLM (Xu et al., 2021b) and LiLT (Wang et al., 2022a), we also evaluate our method based on InfoXLM (Chi et al., 2020) on three sub-tasks: language-specific fine-tuning, multi-task fine-tuning, and zero-shot transfer learning. We first perform fine-tuning based on the Gaussian kernels with random initialization. For a fair comparison, following LiLT, we also adopt the pre-trained Gaussian kernels for further fine-tuning, and we employ the Gaussian kernels in RoBERTa+LAGaBi which have been pre-trained on 1M monolingual document data in Sec 4.3.1. Results on XFUND are shown in Table 2."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.808,
+                0.885,
+                0.919
+            ],
+            "angle": 0,
+            "content": "LAGaBi is also valid in multilingual scenarios, allowing multilingual language models to understand structured documents and outperform existing best-performing methods. LAGaBi can largely increase the performance of the multilingual language model InfoXLM on all three tasks, regardless of whether the Gaussian kernels are"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.521,
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+            ],
+            "angle": 0,
+            "content": "7778"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\">Task</td><td rowspan=\"2\">Model</td><td rowspan=\"2\">Pre-train Data size &amp; language</td><td rowspan=\"2\">FUNSD EN</td><td colspan=\"7\">XFUND</td><td rowspan=\"2\">Avg.</td></tr><tr><td>ZH</td><td>JA</td><td>ES</td><td>FR</td><td>IT</td><td>DE</td><td>PT</td></tr><tr><td rowspan=\"6\">Language-specific</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>87.74</td><td>77.61</td><td>61.05</td><td>67.43</td><td>66.87</td><td>68.14</td><td>68.18</td><td>70.47</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>88.68</td><td>78.65</td><td>62.30</td><td>70.15</td><td>67.51</td><td>70.63</td><td>70.08</td><td>72.07</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>89.24</td><td>79.21</td><td>75.50</td><td>79.02</td><td>80.82</td><td>82.22</td><td>79.03</td><td>80.56</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>89.38</td><td>79.64</td><td>79.11</td><td>79.53</td><td>83.76</td><td>82.31</td><td>82.20</td><td>82.51</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>89.38</td><td>84.01</td><td>78.06</td><td>82.80</td><td>84.40</td><td>84.95</td><td>83.25</td><td>83.78</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>89.65</td><td>84.73</td><td>77.51</td><td>83.86</td><td>85.19</td><td>83.89</td><td>83.47</td><td>84.06</td></tr><tr><td rowspan=\"6\">Zero-shot</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>41.44</td><td>30.23</td><td>30.55</td><td>37.10</td><td>27.67</td><td>32.86</td><td>39.36</td><td>38.24</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>44.08</td><td>36.03</td><td>31.02</td><td>40.21</td><td>28.80</td><td>35.87</td><td>45.02</td><td>41.19</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>60.19</td><td>47.15</td><td>45.65</td><td>57.57</td><td>48.46</td><td>52.52</td><td>53.90</td><td>55.61</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>61.52</td><td>51.84</td><td>51.01</td><td>59.23</td><td>53.71</td><td>60.13</td><td>63.25</td><td>60.61</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>47.91</td><td>46.13</td><td>48.98</td><td>54.84</td><td>47.40</td><td>53.23</td><td>58.82</td><td>55.08</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>39.88</td><td>35.77</td><td>44.82</td><td>54.45</td><td>46.72</td><td>51.64</td><td>56.37</td><td>51.73</td></tr><tr><td rowspan=\"6\">Multi-task</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.33</td><td>88.30</td><td>77.86</td><td>62.23</td><td>70.35</td><td>68.14</td><td>71.46</td><td>67.26</td><td>71.49</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>65.38</td><td>87.41</td><td>78.55</td><td>59.79</td><td>70.57</td><td>68.26</td><td>70.55</td><td>67.96</td><td>71.06</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.24</td><td>89.73</td><td>79.64</td><td>77.98</td><td>81.73</td><td>82.10</td><td>83.22</td><td>82.41</td><td>82.01</td></tr><tr><td>LiLT (2022a)</td><td>1M-Mono</td><td>85.74</td><td>90.47</td><td>80.88</td><td>83.40</td><td>85.77</td><td>87.92</td><td>87.69</td><td>84.93</td><td>85.85</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>86.67</td><td>90.90</td><td>86.49</td><td>81.74</td><td>86.44</td><td>87.74</td><td>88.18</td><td>86.72</td><td>86.89</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>86.35</td><td>92.00</td><td>86.86</td><td>81.50</td><td>87.00</td><td>87.96</td><td>87.58</td><td>87.24</td><td>87.22</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.442,
+                0.883,
+                0.501
+            ],
+            "angle": 0,
+            "content": "Table 2: The performance on FUNSD and XFUND with different settings, including language-specific fine-tuning (fine-tuning on X, testing on X), zero-shot transfer (fine-tuning on FUNSD, testing on X), and multi-task fine-tuning (fine-tuning on all 8 languages, testing on X). \"1M-Mono\" denotes 1 million monolingual (English) documents used for pre-training, while \"30M-Multi\" is the multilingual version. All the \\( F_1 \\) scores in percentage (\\%) are reported."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.524,
+                0.49,
+                0.846
+            ],
+            "angle": 0,
+            "content": "pre-trained or not. InfoXLM+LAGaBi using the pre-trained Gaussian kernels outperforms the top-performing method LiLT on both language-specific and multi-task fine-tuning tasks, with average F1 scores of \\(84.06\\%\\) and \\(87.22\\%\\), demonstrating the efficacy of LAGaBi in multilingual scenarios. On the zero-shot transfer learning task, LAGaBi fell slightly behind its counterparts. This may be due to the inherent gaps between different languages, such as differences in reading order and semantic density. For example, English usually uses spaces to separate words and has uneven word lengths, while Chinese appears as a tighter sequence with smaller semantic units (i.e., characters). Such layout knowledge learned from a specific language shows limited contributions to other languages. This phenomenon also proves the effectiveness of LAGaBi in modeling layouts, i.e. it actually has acquired the layout knowledge for a specific language after fine-tuning on the corresponding data."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.863,
+                0.293,
+                0.879
+            ],
+            "angle": 0,
+            "content": "4.4 Ablation Studies"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.888,
+                0.49,
+                0.919
+            ],
+            "angle": 0,
+            "content": "To investigate the impacts of our learnable Gaussian kernels and polar coordinates, we have con"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.524,
+                0.885,
+                0.783
+            ],
+            "angle": 0,
+            "content": "duced extensive ablation experiments based on several RoBERTa variants equipped with different layout encoding mechanisms (e.g. layout embedding layers, linear bias layers, and fixed/learnable Gaussian Kernels), and spatial relationships (e.g. distance, angle, and 2D-xy distance). All the variants are evaluated without further pre-training, and only fine-tuned for 2000 steps on FUNSD and CORD. Results on FUNSD's test set and CORD's validation set are shown in Table 3. From the result, we can observe that LAGaBi with learnable Gaussian kernels and polar coordinates (#8) can significantly outperform the baseline (#1) and the model with linear layout embedding layers (#2), indicating that models encoding layouts as attention biases are superior to layout embedding-based methods."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.791,
+                0.885,
+                0.919
+            ],
+            "angle": 0,
+            "content": "Impact of the Gaussian kernels. To study the effects of various methods for converting polar coordinates to attention biases, we compared linear layers (#3), fixed Gaussian kernels (#4), and learnable Gaussian kernels (#8). The results demonstrate that linear layers are far less effective than Gaussian kernels, which is likely due to the fact that the Gaussian kernels are more in line with human"
+        },
+        {
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+            "angle": 0,
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+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
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+            "angle": 0,
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+        },
+        {
+            "type": "image",
+            "bbox": [
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+                0.609,
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+            ],
+            "angle": 0,
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+        },
+        {
+            "type": "image",
+            "bbox": [
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+                0.087,
+                0.873,
+                0.267
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.281,
+                0.885,
+                0.325
+            ],
+            "angle": 0,
+            "content": "Figure 2: Visualization of the attention maps. The word-word attention scores are obtained by aggregating token-level attention in the last layer of transformers. We also annotate the position and order of each word in the original document page, and the attention scores are rounded to two decimal places for better visualization."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.126,
+                0.347,
+                0.48,
+                0.513
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Task</td><td>#</td><td>Ablation Strategy</td><td>FUNSD</td><td>CORD</td></tr><tr><td></td><td>1</td><td>RoBERTa(baseline)</td><td>66.48</td><td>92.29</td></tr><tr><td></td><td>2</td><td>+ embedding layers</td><td>69.59</td><td>92.67</td></tr><tr><td rowspan=\"2\">Impact of Gaussian</td><td>3</td><td>+ linear bias layers</td><td>76.32</td><td>93.00</td></tr><tr><td>4</td><td>+ fixed kernels</td><td>83.81</td><td>93.00</td></tr><tr><td rowspan=\"3\">Impact of Polar-Coor.</td><td>5</td><td>+ Euclidean distance</td><td>73.05</td><td>93.72</td></tr><tr><td>6</td><td>+ Angle</td><td>84.48</td><td>94.70</td></tr><tr><td>7</td><td>+ 2D-xy distance</td><td>79.85</td><td>94.21</td></tr><tr><td></td><td>8</td><td>+ LAGaBi</td><td>84.84</td><td>94.77</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.114,
+                0.522,
+                0.487,
+                0.552
+            ],
+            "angle": 0,
+            "content": "Table 3: Ablation studies on the effectiveness of the learnable Gaussian kernels and the polar coordinates."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.58,
+                0.49,
+                0.92
+            ],
+            "angle": 0,
+            "content": "intuition than linear layers. Learnable Gaussian kernels (#8) also achieve better performance than fixed Gaussian kernels whose mean is 0 and variance is 1 (#4), since the learnable Gaussian kernels enjoy better flexibility to adapt to different formats. Impact of the polar coordinates. Polar coordinates, which consist of two elements: distance and angle, is a typical technique for describing spatial relationships. We analyzed the effects of distance (#5) and angle (#6), as well as the classical 2-D relative horizontal and vertical distances proposed by TITL (Powalski et al., 2021) (#7). The results suggest that the model with angle information (#6) is more effective than the model with distance information (#5 and #7). We hypothesize that this is because angles are less impacted by size scaling than distances, but they are more sensitive to location changes. Furthermore, LAGaBi achieves much better performance than the model that employs horizontal and vertical distances, which further reveals the superiority of polar coordinates."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.35,
+                0.623,
+                0.365
+            ],
+            "angle": 0,
+            "content": "4.5 Analysis"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.371,
+                0.883,
+                0.516
+            ],
+            "angle": 0,
+            "content": "Impact of \\(\\alpha\\). Hyper-parameter \\(\\alpha\\) makes a tradeoff between semantic and layout contributions when computing pair-wise attention scores in our LABaBi, which is important. We conduct several experiments with different \\(\\alpha\\) settings to study the impact of \\(\\alpha\\) based on RoBERTa without any further pre-training. \\(F_{1}\\) scores on CORD's validation set are listed in Table 4, showing the model achieves its best performance when \\(\\alpha = 4\\)."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.524,
+                0.528,
+                0.871,
+                0.574
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>α =</td><td>0</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr><tr><td>CORD</td><td>92.29</td><td>92.82</td><td>93.08</td><td>93.46</td><td>94.77</td><td>94.39</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.508,
+                0.584,
+                0.883,
+                0.613
+            ],
+            "angle": 0,
+            "content": "Table 4: Impact of different \\(\\alpha\\) settings. All the scores are from CORD's validation set."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.63,
+                0.885,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Visualization analysis. We visualize the word-level attention maps of the baseline RoBERTa and our RoBERTa+LAGaBi. Due to space limitations, in this paper, we only show the attention maps of the first 8 words in the input sequence. The case in Figure 2 is from the test set of FUNSD. As shown in Figure 2, the RoBERTa incorrectly associates the character \"R.\" with all the words, while most other words are treated as unrelated. According to the attention map in the RoBERTa+LAGaBi, greater attention scores arise between \"R.\" and \"F.\", \"J.\" and \"D.\", all of which are placed in the signature area, whereas attention scores between remote irrelevant words such as \"DATE\" and \"STRU\" are zeros. This demonstrates that LAGaBi indeed learns more accurate semantic associations by incorporating layout information. More examples and detailed analysis can be seen in Appendix.A."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+                0.521,
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+            "angle": 0,
+            "content": "7780"
+        }
+    ],
+    [
+        {
+            "type": "title",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "5 Conclusion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.11,
+                0.49,
+                0.399
+            ],
+            "angle": 0,
+            "content": "In this paper, we propose a model-agnostic and language-independent method that leverages Layout Attention with Gaussian Biases to encode the relative spatial positions for structured document understanding (SDU). Specifically, we first model the inter-word spatial relationships using polar coordinates. Then the query-key attention scores are revised by the Gaussian biases that are related to their spatial relationships. Our method can be applied to a series of Transformer-based models with extremely few parameters, improving their performance for SDU tasks. Experiments based on six transformer-based SDU models and three monolingual/multilingual benchmarks fully demonstrate the effectiveness of our proposal. This research provides new ideas for structured document understanding tasks, which are expected to promote the efficient development of document intelligence."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.411,
+                0.22,
+                0.426
+            ],
+            "angle": 0,
+            "content": "Limitations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.437,
+                0.49,
+                0.646
+            ],
+            "angle": 0,
+            "content": "Despite the superior performance exhibited by LAGaBi, it does have some limitations. Firstly, in our experiments with the LayoutLM series that integrate multi-modal features, LAGaBi was only fine-tuned for validation without pre-training. We believe that leveraging multi-modal pre-training could further improve LAGaBi's performance based on LayoutLM, and this will be explored in future investigations. Secondly, although we have empirically demonstrated the effectiveness of polar coordinates and Gaussian distribution in layout learning, our motivation is driven by a simple intuition rather than rigorous mathematical proof."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.658,
+                0.287,
+                0.673
+            ],
+            "angle": 0,
+            "content": "Acknowledgements"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.683,
+                0.488,
+                0.715
+            ],
+            "angle": 0,
+            "content": "We sincerely thank all the anonymous reviewers for their valuable comments and suggestions."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.741,
+                0.214,
+                0.756
+            ],
+            "angle": 0,
+            "content": "References"
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+            "content": "A Appendix"
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+        {
+            "type": "text",
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+                0.883,
+                0.694
+            ],
+            "angle": 0,
+            "content": "Figure 3 in this section shows the attention distribution of four additional examples on FUNSD. From all the examples, it can be observed that the RoBERTa model typically treats each word in isolation, while RoBERTa+LAGaBi can learn the more accurate correlations between different words based on their layout relationships."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+                0.695,
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+            "angle": 0,
+            "content": "We also visualize the word-level attention maps of LayoutLMv3 and LayoutLMv3+LAGaBi. LayoutLMv3 is currently the top-performing method for structured document understanding, which utilizes the 2-D relative positions through linear attention biases. From the attention maps shown in Figure 4, we can observe that LayoutLMv3+LAGaBi refers more to layout information when modeling the inter-word semantic correlations, while LayoutLMv3 is relatively independent. For example, in the third sample of Figure 4, LAGaBi learns more dense associations among \"Tiers\", \"II.\", and \"&\" in the neighborhood than LayoutLMv3, which is in line with human intuition."
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+        {
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+            "bbox": [
+                0.287,
+                0.888,
+                0.71,
+                0.904
+            ],
+            "angle": 0,
+            "content": "Figure 3: Visualization of more examples based on RoBERTa."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.481,
+                0.928,
+                0.52,
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+            "content": "Attention Map of LayoutLMv3"
+        },
+        {
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3+LAGaBi"
+        },
+        {
+            "type": "image",
+            "bbox": [
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+                0.866,
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+            "bbox": [
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3"
+        },
+        {
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+            "bbox": [
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3+LAGaBi"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.626,
+                0.314,
+                0.868,
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3"
+        },
+        {
+            "type": "image",
+            "bbox": [
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+        },
+        {
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+            "bbox": [
+                0.696,
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3+LAGaBi"
+        },
+        {
+            "type": "image",
+            "bbox": [
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3"
+        },
+        {
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+            "angle": 0,
+            "content": "Attention Map of LayoutLMv3+LAGaBi"
+        },
+        {
+            "type": "image",
+            "bbox": [
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+            "bbox": [
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+            "angle": 0,
+            "content": "Figure 4: Visualization of examples base on LayoutLMv3."
+        },
+        {
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+]

2023/Beyond Layout Embedding_ Layout Attention with Gaussian Biases for Structured Document Understanding/fc2d1e06-8d86-4d7d-9ced-4589565dfec7_origin.pdf

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2023/Beyond Layout Embedding_ Layout Attention with Gaussian Biases for Structured Document Understanding/full.md

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+# Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding
+
+Xi Zhu, Xue Han, Shuyuan Peng, Shuo Lei, Chao Deng and Junlan Feng*
+
+JIUTIAN Team, China Mobile Research Institute, Beijing, China
+
+{zhuqian,hanxueai,pengshuyuan,leishuo,dengchao,fengjunlan}@chinamobile.com
+
+# Abstract
+
+Effectively encoding layout information is a central problem in structured document understanding. Most existing methods rely heavily on millions of trainable parameters to learn the layout features of each word from Cartesian coordinates. However, two unresolved questions remain: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are massive learnable parameters truly necessary for layout representation? In this paper, we address these questions by proposing Layout Attention with Gaussian Biases (LAGaBi): Firstly, we find that polar coordinates provide a superior choice over Cartesian coordinates as they offer a measurement of both distance and angle between word pairs, capturing relative positions more effectively. Furthermore, by feeding the distances and angles into 2-D Gaussian kernels, we model intuitive inductive layout biases, i.e., the words closer within a document should receive more attention, which will act as the attention biases to revise the textual attention distribution. LAGaBi is model-agnostic and language-independent, which can be applied to a range of transformer-based models, such as the text pre-training models from the BERT series and the LayoutLM series that incorporate visual features. Experimental results on three widely used benchmarks demonstrate that, despite reducing the number of layout parameters from millions to 48, LAGaBi achieves competitive or even superior performance. Our code is available on GitHub<sup>1</sup>.
+
+# 1 Introduction
+
+Structured document understanding (SDU) has gained significant research attention in the field of intelligent document processing (Park et al., 2019; Jaume et al., 2019; Han et al., 2023). It focuses on extracting layout structures and contents from scanned or digital documents, leading to enhanced
+
+performance in several downstream tasks like form comprehension and receipt understanding.
+
+Unlike conventional text understanding (Liu et al., 2019; Vaswani et al., 2017; Kenton and Toutanova, 2019), SDU goes beyond comprehending serialized text and requires the ability to interpret documents with diverse layouts (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). Documents with varying layouts often contain text fields positioned in different ways. To take advantage of existing pre-trained language models, early methods (Xu et al., 2020; Li et al., 2021a,c; Appalaraju et al., 2021; Chi et al., 2020) propose to directly add 2-D position embedding to the word embedding for each word as input for Transformer. The position embedding encode the 2-D absolute coordinates  $(x_0, y_0, x_1, y_1)$  of each word in the document through multi position encoding layers, where  $(x_0, y_0)$  represents the upper left point and  $(x_1, y_1)$  represents the lower right point of the bounding box for each word. Some researches (Powalski et al., 2021; Hong et al., 2022; Lee et al., 2022; Xu et al., 2021a; Huang et al., 2022) further proposed that absolute positions are inefficient for representing the spatial relationships between words. They employ relative positions between words to encode spatial relationships. For example, Hong et al.(2022) map the relative positions into embeddings, which are then multiplied with the semantic embedding of the word to calculate inter-word layout scores. This score is incorporated into the self-attention layers to combine semantic and layout features.
+
+Despite the significant progress made, we argue that current methods, whether based on absolute or relative positioning, heavily rely on a large number of trainable parameters for position embeddings from Cartesian coordinates, often comprising millions of parameters. This raises two unexplored questions: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are
+
+massive learnable layout parameters truly necessary? Concerning the former, various coordinate systems exist, including Cartesian, polar, and spherical coordinates, yet previous research has solely focused on Cartesian coordinates. Regarding the latter, it is intuitively expected that words closer within a document should receive more attention. However, this simple inductive bias may not be effectively learned solely through gradient updates.
+
+In this paper, we present a unified investigation of the two aforementioned problems. Regarding the choice of coordinate systems, we find that polar coordinates offer a more efficient representation than Cartesian coordinates for expressing relative positions. By computing the differences in distance and angle between two words in polar space, polar coordinates outperform their Cartesian counterparts by providing extra angle information. For layout learning, we discover that layout modeling can be achieved by a specific distribution: words closer in space receive higher layout scores, eliminating the need for extra position embeddings. Combining these two choices, we propose LAGaBi (Layout Attention with Gaussian Biases). LAGaBi formulates pairwise spatial relationships between tokens using the distance and angle in the polar coordinate system. Moreover, the distance and angle are fed into a 2-D Gaussian distribution to output a layout score. We choose the Gaussian distribution because it guarantees that the layout score decreases when either the distance or angle variables increase, making it the most commonly used distribution for this purpose. The layout score is then incorporated into the original self-attention as attention bias, resulting in a revised distribution that considers both text and layout features. We introduce trainable Gaussian kernels to better align the semantic and layout scores at different scales, adding just  $4 \times$  attention heads extra parameters. For instance, based on RoBERTa (Liu et al., 2019) with 12 attention heads, there are only 48 additional parameters that need to be learned for encoding layout features.
+
+Extensive experiments demonstrate that LAGaBi achieves remarkable performance on diverse SDU benchmarks, including FUNSD (Jaume et al., 2019), CORD (Park et al., 2019), and XFUND (Xu et al., 2021b), across both monolingual and multilingual scenarios. LAGaBi emerges as a versatile module that seamlessly integrates with transformer-based language models, such as BERT (Kenton and Toutanova, 2019), RoBERTa, and InfoXLM (Chi
+
+et al., 2020), empowering them to effectively process structured documents and achieve significant performance gains of up to 27.01 points. Additionally, LAGaBi can be incorporated into complex SDU models that leverage visual features, such as LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022), leading to further performance enhancements and establishing new state-of-the-art results.
+
+# 2 Related Works
+
+Significant progress has recently been made by using the Transformer-based pre-trained model (PTM) to learn the cross-modality interaction between textual and layout information, which has been demonstrated to be critical for structured document understanding (Xu et al., 2020; Huang et al., 2022; Powalski et al., 2021; Li et al., 2021a; Wang et al., 2022a). LayoutLM (Xu et al., 2020) modified the input of BERT (Kenton and Toutanova, 2019) by adding position embedding layers to encode word-level 2-D coordinates, while StructuralLM (Li et al., 2021a) proposed to encode segment-level positions. LiLT (Wang et al., 2022a) encoded text and layout using two different transformer layers separately and adopted bi-directional attention to fuse them. Besides, there are a series of works that use multi-modal transformers to model text, layout, and image simultaneously, such as SelfDoc (Li et al., 2021b), DocFormer (Appalaraju et al., 2021), StrucTexT (Li et al., 2021c), ERNIE-Layout (Peng et al., 2022), mmLayout (Wang et al., 2022b).
+
+Most of the above approaches encode the 2-D absolute positions, ignoring the critical relative spatial relationships between words that are essential to textual semantic understanding. Hong et al.(2022) proposed to encode the spatial relationships as relative position embeddings, which are then multiplied with the semantic embedding of the token to calculate inter-word layout scores. This score is incorporated into the self-attention to combine semantic and layout features. GeoLay-outLM (Luo et al., 2023) introduces geometric relations and brand-new geometric pre-training tasks in different levels for learning the geometric layout representation, whose geometric relations are largely dependent on some pre-defined manual rules. TITL (Powalski et al., 2021) encodes the relative positions between words in a simpler manner, and it adopts linear layers to convert the 2-D discrete distance (implemented through bucketing)
+
+![](images/35671b573bf6ab9b3d1aaa4a0135314602f2ac360e439889814a9b5af1808c19.jpg)  
+Layout Attention with Gaussian Biases  
+Figure 1: An illustration of our Layout Attention with Gaussian Biases (LAGaBi). For each query word, we first compute the spatial relationships of each keyword in a polar system centered on it. Then Gaussian kernels are employed to transform these spatial relationships into attention biases, which will revise the original query-key attention distribution. Such biases are shared across different Transformer layers. When using LAGaBi, we can encode the document layout structure without adding position embedding layers at the bottom of the network.
+
+between words into attention biases motivated by T5 (Raffel et al., 2020). Such relative attention biases have also been employed by LayoutLMv2 (Xu et al., 2021a) and LayoutLMv3 (Huang et al., 2022), yielding notable results. Different from them, we model the relative positions from a new perspective, i.e. introducing polar coordinates with distance and angle that are more efficient in representing relative spatial relationships. Furthermore, we employ the extremely lightweight Gaussian kernels to encode polar coordinates into attention biases, offering a more streamlined approach with fewer trainable parameters and aligning better with human intuition compared to linear layers.
+
+# 3 Methodology
+
+Structured documents typically contain both textual words and layouts, where textual words denote the main content of the document, while layouts record the organizational form of text. Optical character recognition (OCR) as a classical technique for image document parsing can recognize the text as well as its locations. Formally, given a document  $\mathcal{D}$ , OCR identifies the text words
+
+$\mathcal{W} = \{w_i\}_{i=1}^N$  and their associated layout positions  
+ $\mathcal{C} = \{c_i\}_{i=1}^N$ , where  $N$  is the number of words while  $c_i = [x_i^0, y_i^0, x_i^1, y_i^1]$  represents the left-top and right-bottom coordinates of the bounding box that contains the  $i_{th}$  word. An ideal model for document understanding should take both text words and their layout positions into consideration.
+
+The key to structured document comprehension, building upon the achievements of text understanding techniques such as Transformer (Vaswani et al., 2017), lies in effectively representing document layouts while remaining compatible with language representation. Therefore, there are two crucial problems that need to be solved: 1. How to model document layouts efficiently? 2. How to integrate the layouts into Transformer to guide textual semantic understanding of the document content?
+
+In this paper, we introduce a novel layout modeling method for structured document understanding, namely Layout Attention with Gaussian Biases (LAGaBi). As shown in Figure 1, it mainly consists of two parts: modeling relative positions with polar coordinates and layout attention with Gaussian biases. The former is responsible for modeling the 2-D relative positions between words by po
+
+lar coordinates which are capable of representing inter-word spatial relationships; The latter focuses on transforming polar coordinates into attention biases, which will modify the original semantic query-key attentions between words into a more suitable distribution that accurately captures the underlying layout structure of documents.
+
+# 3.1 Spatial Relationships with Polar Coordinates
+
+In a structured document, the relative spatial relationships between words are shown to be more important than their absolute coordinates, which can assist humans in better understanding pair-wise semantic dependencies. For example, tokens in the same line generally have stronger semantic associations with each other, while tokens that are farther away and on different lines are more difficult to form strong associations. Although some previous works (Powalski et al., 2021; Xu et al., 2021a; Li et al., 2021a) have proposed to model the relative distances, which acted as learnable attention biases, they still rely on learnable positional embeddings and only utilize the relative horizontal and vertical distances in Cartesian coordinate system.
+
+Different from previous works, we propose to capture inter-word spatial relationships through polar coordinates, in which both orientations and distances can be preserved. For each query token, we can build a polar coordinate system centered at its position, and calculate the polar coordinates (spatial relationships) of its keys. More concretely, the position of the query token is regarded as the reference point (pole), and the horizontal direction in the current Cartesian coordinate system is set as the reference direction (polar axis) following the most common reading habit, i.e., left-to-right and top-to-down. Formally, given a query token with its 2-D coordinates  $c_{i}$  as the pole, the polar coordinates  $\mathbf{u}_{ij} = (\rho_{ij},\theta_{ij})$  of the  $j_{th}$  key in this document page can be calculated as below:
+
+$$
+\rho_ {i j} = \sqrt {\left(x _ {j} - x _ {i}\right) ^ {2} + \left(y _ {j} - y _ {i}\right) ^ {2}} \tag {1}
+$$
+
+$$
+\theta_ {i j} = \tan^ {- 1} \left(\left(y _ {j} - y _ {i}\right) / \left(x _ {j} - x _ {i}\right)\right) \tag {2}
+$$
+
+where  $\rho_{ij} \in [0,1]$  and  $\theta_{ij} \in [-\pi / 2, \pi / 2]$  denote the distance and angle (orientation) from the  $i_{th}$  token to the  $j_{th}$  token respectively, and  $(x_i, y_i)$  are the normalized coordinates of the top-left point of the  $i_{th}$  bounding box. For instance, in Figure 1, when taking "REPORT" as the reference point, the
+
+spatial relationship between the words "FORM" and "REPORT" can be represented as a polar coordinate (0.064, 0), indicating a distance of 0.064 and an angle of 0 degrees, while that between the words "YEAR" and "REPORT" is (0.297, 1.432).
+
+# 3.2 Layout Attention with Gaussian Biases
+
+How to use the essential relative spatial relationships to guide the model to perceive layout information is a problem worth exploring. Inspired by ALiBi (Press et al., 2021) and T5 bias (Raffel et al., 2020) that encode the 1-D relative position information as attention biases upon the query-key scores instead of positional embedding, we propose to revise attention scores/distribution with 2-D attention biases that integrate spatial relationships. Specifically, the attention score in a single-head self-attention can be modified as:
+
+$$
+a _ {i j} = \frac {\exp \left(\mathbf {q} _ {i} \mathbf {k} _ {j} ^ {T} / \sqrt {d _ {k}} + \alpha (\mathbf {g} \left(\mathbf {u} _ {i j}\right) - 1)\right)}{\sum_ {j = 1} ^ {N} \exp \left(\mathbf {q} _ {i} \mathbf {k} _ {j} ^ {T} / \sqrt {d _ {k}} + \alpha (\mathbf {g} \left(\mathbf {u} _ {i j}\right) - 1)\right)} \tag {3}
+$$
+
+where  $\mathbf{q}_i$  is the  $i_{th}$  query vector,  $\mathbf{q}_i$  is the  $j_{th}$  key vector, and  $d_k$  is the dimension of the attention head.  $\mathbf{g}(\mathbf{u}_{ij})$  denotes the attention biases, which is derived from the 2-D Gaussian kernel with learnable parameters based on polar coordinates  $\mathbf{u}$  indicating spatial relationships. The Gaussian kernel ensures that words farther within the document are assigned a smaller layout score. Therefore, by incorporating a reversed term  $(\mathbf{g}(\mathbf{u}_{ij}) - 1)$ , we can significantly penalize the attention scores of query-key pairs that are farther, while making only slight revisions to the scores of closer pairs.  $\alpha$  is a hyper-parameter that makes a trade-off between semantic association and spatial dependency. It denotes how much the spatial relationship between the key and the query contributes to their semantic association. In particular, a convenient formula of  $\mathbf{g}(\mathbf{u})$  is:
+
+$$
+\mathbf {g} (\mathbf {u}) = e x p \left(- \frac {1}{2} (\mathbf {u} - \boldsymbol {\mu}) ^ {T} \boldsymbol {\Sigma} ^ {- 1} (\mathbf {u} - \boldsymbol {\mu})\right) \tag {4}
+$$
+
+where  $\boldsymbol{\Sigma}$  and  $\pmb{\mu}$  are learnable  $2\times 2$  and  $2\times 1$  covariance matrix and mean vector of a Gaussian kernel, respectively. We further restrict the covariances to have diagonal form, resulting in  $2\times 2$  parameters per kernel for each attention head. Note that the Gaussian kernels are different across different attention heads, but are shared across different self-attention layers. Thus, there is a total of  $2\times$ $2\times N_{\mathrm{heads}}$  learnable parameters for our attention biases, where  $N_{\mathrm{heads}}$  denotes the attention head
+
+number in each self-attention layer. For example, taking the RoBERTa base as the backbone, there are 48 parameters that need to be learned. Notably, we only include layout information in the keys and queries but not in the values, ensuring that the text semantics are not corrupted.
+
+Properties of our LAGaBi: (1) It is easy to implement and can be adapted to any transformer-based model without changing its structure. (2) The position embedding layer is discarded, and there are very few parameters to learn, which can be done during the fine-tuning stage, so it is quite efficient. (3) It decouples layout and text understanding, allowing the potential of language models to be fully exploited in structured document understanding.
+
+# 4 Experiments
+
+# 4.1 Datasets
+
+Pre-training Dataset. Following LayoutLM (Xu et al., 2020), we also pre-train our model using the IIT-CDIP Test Collection 1.0 (Lewis et al., 2006), which is a large-scale dataset with over 11 million scanned document images. Only 1 million of them are used for fast pre-training. We pre-process each document page using Tesseract $^2$ , an open-source OCR engine, to retrieve the textual contents as well as their layouts. We normalize the coordinates of each token to integers in the range of 0 to 1000 and add an empty bounding box  $[0,0,0,0]$  to the special tokens [CLS], [SEP], and [PAD].
+
+Fine-tuning Datasets. We evaluate our method on both monolingual (English) and multilingual document information extraction datasets listed below. FUNSD (Jaume et al., 2019) is a form dataset that uses forms to extract and organize textual information. It contains 199 documents, 149 of which are for training and 50 of which are for testing. CORD (Park et al., 2019) is a dataset for receipt key information extraction that includes 800, 100, and 100 receipts for training, validating, and testing, respectively. XFUND (Xu et al., 2021b) is a multilingual version of FUNSD with 8 languages, each language containing 199 instances (149 for training and 50 for testing) as FUNSD.
+
+# 4.2 Implementation Details
+
+Our approach is model-agnostic and language-independent, which can be applied to a range of transformer-based models. In this paper, we have evaluated our method based on three kinds
+
+of baselines: 1) monolingual models (BERT (Kenton and Toutanova, 2019) and RoBERTa (Liu et al., 2019)), 2) multilingual model (InfoXLM (Chi et al., 2020)), and 3) document understanding models (LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a), and LayoutLMv3 (Huang et al., 2022)). Gaussian kernels will be included in the self-attention blocks of each model, which describe the relative spatial relationship between tokens.
+
+Pre-training. We only conduct pre-training tasks for the two monolingual models: BERT+LAGaBi and RoBERTa+LAGaBi. We initialize the weight of them with the corresponding baselines, except the Gaussian kernels. The parameters of the Gaussian kernels, namely covariance matrixes and mean vectors, are randomly initialized. Both models are simply supervised by masked language modeling (MLM) loss during pre-training. Adam optimizer (Kingma and Ba, 2014) is adopted with a learning rate of  $5e - 5$ , weight decay of  $1e - 2$  and  $(\beta_{1},\beta_{2}) = (0.9,0.999)$ . The batch size is set to 128 and all the two models are trained for 200,000 steps on 8 NVIDIA v100 32GB GPUs.
+
+Fine-tuning. In this paper, we mainly focus on the document understanding task of semantic entity labeling, which aims at assigning each semantic entity a BIO label. We add a token-level classification layer upon the base models (including monolingual, multilingual, and document understanding models) to predict the BIO labels for this task. Word-level F1 score is adopted as the evaluation metric. The fine-tuning process takes 2000 steps using a batch size of 16 and the Adam optimizer with a learning rate of 5e-5 for FUNSD and 7e-5 for CORD and XFUND. Fine-tuning configurations for document understanding models follow their official releases. Hyper-parameter  $\alpha$  is set to 4 for all experiments, which has been tuned on the CORD's val set.
+
+# 4.3 Experimental Results
+
+# 4.3.1 Performance on monolingual datasets
+
+We first evaluate our method on the monolingual form and receipt understanding datasets. From the results shown in Table 1, we can observe that:
+
+(1) The lightweight LAGaBi enables simple integration with language models, allowing them to effectively process structured documents. For example, without any pre-training on document data, RoBERTa+LAGaBi has achieved  $84.84\%$  and  $95.97\%$  F1 scores on FUNSD and CORD, surpassing baseline model RoBERTa by  $18.38\%$  and
+
+<table><tr><td>Model</td><td>#Parameters</td><td>Pre-training</td><td>Modality</td><td>FUNSD</td><td>CORD</td></tr><tr><td>BERT (Kenton and Toutanova, 2019)</td><td>110M</td><td>-</td><td>T</td><td>60.26</td><td>89.68</td></tr><tr><td>RoBERTa (Liu et al., 2019)</td><td>125M</td><td>-</td><td>T</td><td>66.48</td><td>93.54</td></tr><tr><td>BROS (Hong et al., 2022)</td><td>110M</td><td>11M</td><td>T+L</td><td>81.21</td><td>-</td></tr><tr><td>FormNet (Lee et al., 2022)</td><td>217M</td><td>11M</td><td>T+L</td><td>84.69</td><td>-</td></tr><tr><td>LiLT (Wang et al., 2022a)</td><td>131M</td><td>11M</td><td>T+L</td><td>88.41</td><td>96.07</td></tr><tr><td>BERT+LAGaBi(w/o pre-train)</td><td>110M+48</td><td>-</td><td>T+L</td><td>74.14 (+13.88)</td><td>93.44 (+3.76)</td></tr><tr><td>BERT+LAGaBi(w/ pre-train)</td><td>110M+48</td><td>1M</td><td>T+L</td><td>87.27 (+27.01)</td><td>95.82 (+6.14)</td></tr><tr><td>RoBERTa+LAGaBi(w/o pre-train)</td><td>125M+48</td><td>-</td><td>T+L</td><td>84.84 (+18.36)</td><td>95.97 (+2.43)</td></tr><tr><td>RoBERTa+LAGaBi(w/ pre-train)</td><td>125M+48</td><td>1M</td><td>T+L</td><td>89.15 (+22.67)</td><td>96.56 (+3.02)</td></tr><tr><td>LayoutLM (Xu et al., 2020)</td><td>160M</td><td>11M</td><td>T+L+I</td><td>79.27</td><td>94.31*</td></tr><tr><td>LayoutLMv2 (Xu et al., 2021a)</td><td>200M</td><td>11M</td><td>T+L+I</td><td>82.70</td><td>94.95</td></tr><tr><td>StrucTexT (Li et al., 2021c)</td><td>107M</td><td>11M</td><td>T+L+I</td><td>83.09</td><td>-</td></tr><tr><td>DocFormer (Appalaraju et al., 2021)</td><td>183M</td><td>11M</td><td>T+L+I</td><td>83.34</td><td>96.33</td></tr><tr><td>LayoutLMv3 (Huang et al., 2022)</td><td>133M</td><td>11M</td><td>T+L+I</td><td>90.29</td><td>96.56</td></tr><tr><td>LayoutLM+LAGaBi(w/o pre-train)</td><td>160M+48</td><td>-</td><td>T+L+I</td><td>87.77 (+8.10)</td><td>94.93(+0.62)</td></tr><tr><td>LayoutLMv2+LAGaBi(w/o pre-train)</td><td>200M+48</td><td>-</td><td>T+L+I</td><td>88.16 (+5.49)</td><td>97.05(+2.10)</td></tr><tr><td>LayoutLMv3+LAGaBi(w/o pre-train)</td><td>133M+48</td><td>-</td><td>T+L+I</td><td>91.00 (+0.71)</td><td>97.05 (+0.49)</td></tr></table>
+
+Table 1: Performance on FUNSD and CORD for monolingual structured document understanding. "T/L/I" denotes the "text/layout/image" modality. (+x) denotes the gain in F1 score compared to base model, while * show the result from our re-implementation. All the F1 scores in percentage (%) are reported.
+
+$2.43\%$  respectively. RoBERTa+LAGaBi also outperforms several representative document understanding models such as LayoutLM (Xu et al., 2020) and LayoutLMv2 (Xu et al., 2021a). The results show that LAGaBi is a powerful method for capturing essential layout features, allowing language models to be easily extended to adapt to structured document understanding tasks.
+
+(2) Pre-training brings profits. After pre-training on 1 million unlabeled document data, our method exhibits extra improvements. RoBERTa+LAGaBi with pre-train surpasses all other approaches except LayoutLMv3 (Huang et al., 2022). While there is still a minor difference on FUNSD between our model and LayoutLMv3, our method is significantly easier to implement, introducing only 48 extra learnable parameters to the vanilla Transformers structure, making it more computationally efficient and flexible.  
+(3) The LAGaBi could also be seamlessly coupled with other layout embedding and multimodal-based document understanding models, improving their performance even further. Performance improvements on LayoutLM (Xu et al., 2020), LayoutLMv2 (Xu et al., 2021a) and LayoutLmv3 (Huang et al., 2022)) are obvious, with F1 score gains of  $8.10\%$ $5.49\%$ $0.71\%$  on FUNSD and  $0.62\%$ $2.10\%$ $0.49\%$  on CORD. The process
+
+is particularly efficient since it only requires fine-tuning based on the published pre-trained weights rather than pre-training from scratch. Furthermore, by combining LAGaBi with the top-performing LayoutLMv3 model, we achieve new state-of-the-art results on both FUNSD and CORD datasets.
+
+# 4.3.2 Performance on multilingual dataset
+
+Following the multi-lingual LayoutXLM (Xu et al., 2021b) and LiLT (Wang et al., 2022a), we also evaluate our method based on InfoXLM (Chi et al., 2020) on three sub-tasks: language-specific fine-tuning, multi-task fine-tuning, and zero-shot transfer learning. We first perform fine-tuning based on the Gaussian kernels with random initialization. For a fair comparison, following LiLT, we also adopt the pre-trained Gaussian kernels for further fine-tuning, and we employ the Gaussian kernels in RoBERTa+LAGaBi which have been pre-trained on 1M monolingual document data in Sec 4.3.1. Results on XFUND are shown in Table 2.
+
+LAGaBi is also valid in multilingual scenarios, allowing multilingual language models to understand structured documents and outperform existing best-performing methods. LAGaBi can largely increase the performance of the multilingual language model InfoXLM on all three tasks, regardless of whether the Gaussian kernels are
+
+<table><tr><td rowspan="2">Task</td><td rowspan="2">Model</td><td rowspan="2">Pre-train Data size &amp; language</td><td rowspan="2">FUNSD EN</td><td colspan="7">XFUND</td><td rowspan="2">Avg.</td></tr><tr><td>ZH</td><td>JA</td><td>ES</td><td>FR</td><td>IT</td><td>DE</td><td>PT</td></tr><tr><td rowspan="6">Language-specific</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>87.74</td><td>77.61</td><td>61.05</td><td>67.43</td><td>66.87</td><td>68.14</td><td>68.18</td><td>70.47</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>88.68</td><td>78.65</td><td>62.30</td><td>70.15</td><td>67.51</td><td>70.63</td><td>70.08</td><td>72.07</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>89.24</td><td>79.21</td><td>75.50</td><td>79.02</td><td>80.82</td><td>82.22</td><td>79.03</td><td>80.56</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>89.38</td><td>79.64</td><td>79.11</td><td>79.53</td><td>83.76</td><td>82.31</td><td>82.20</td><td>82.51</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>89.38</td><td>84.01</td><td>78.06</td><td>82.80</td><td>84.40</td><td>84.95</td><td>83.25</td><td>83.78</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>89.65</td><td>84.73</td><td>77.51</td><td>83.86</td><td>85.19</td><td>83.89</td><td>83.47</td><td>84.06</td></tr><tr><td rowspan="6">Zero-shot</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.70</td><td>41.44</td><td>30.23</td><td>30.55</td><td>37.10</td><td>27.67</td><td>32.86</td><td>39.36</td><td>38.24</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>68.52</td><td>44.08</td><td>36.03</td><td>31.02</td><td>40.21</td><td>28.80</td><td>35.87</td><td>45.02</td><td>41.19</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.40</td><td>60.19</td><td>47.15</td><td>45.65</td><td>57.57</td><td>48.46</td><td>52.52</td><td>53.90</td><td>55.61</td></tr><tr><td>LiLT (2022a)</td><td>11M-Mono</td><td>84.15</td><td>61.52</td><td>51.84</td><td>51.01</td><td>59.23</td><td>53.71</td><td>60.13</td><td>63.25</td><td>60.61</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>83.35</td><td>47.91</td><td>46.13</td><td>48.98</td><td>54.84</td><td>47.40</td><td>53.23</td><td>58.82</td><td>55.08</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>84.17</td><td>39.88</td><td>35.77</td><td>44.82</td><td>54.45</td><td>46.72</td><td>51.64</td><td>56.37</td><td>51.73</td></tr><tr><td rowspan="6">Multi-task</td><td>XLM-RoBERTa (2020)</td><td>-</td><td>66.33</td><td>88.30</td><td>77.86</td><td>62.23</td><td>70.35</td><td>68.14</td><td>71.46</td><td>67.26</td><td>71.49</td></tr><tr><td>InfoXLM (2020)</td><td>-</td><td>65.38</td><td>87.41</td><td>78.55</td><td>59.79</td><td>70.57</td><td>68.26</td><td>70.55</td><td>67.96</td><td>71.06</td></tr><tr><td>LayoutXLM (2021b)</td><td>30M-Mutli</td><td>79.24</td><td>89.73</td><td>79.64</td><td>77.98</td><td>81.73</td><td>82.10</td><td>83.22</td><td>82.41</td><td>82.01</td></tr><tr><td>LiLT (2022a)</td><td>1M-Mono</td><td>85.74</td><td>90.47</td><td>80.88</td><td>83.40</td><td>85.77</td><td>87.92</td><td>87.69</td><td>84.93</td><td>85.85</td></tr><tr><td>InfoXLM+LAGaBi</td><td>-</td><td>86.67</td><td>90.90</td><td>86.49</td><td>81.74</td><td>86.44</td><td>87.74</td><td>88.18</td><td>86.72</td><td>86.89</td></tr><tr><td>InfoXLM+LAGaBi</td><td>1M-Mono</td><td>86.35</td><td>92.00</td><td>86.86</td><td>81.50</td><td>87.00</td><td>87.96</td><td>87.58</td><td>87.24</td><td>87.22</td></tr></table>
+
+Table 2: The performance on FUNSD and XFUND with different settings, including language-specific fine-tuning (fine-tuning on X, testing on X), zero-shot transfer (fine-tuning on FUNSD, testing on X), and multi-task fine-tuning (fine-tuning on all 8 languages, testing on X). "1M-Mono" denotes 1 million monolingual (English) documents used for pre-training, while "30M-Multi" is the multilingual version. All the  $F_1$  scores in percentage (\%) are reported.
+
+pre-trained or not. InfoXLM+LAGaBi using the pre-trained Gaussian kernels outperforms the top-performing method LiLT on both language-specific and multi-task fine-tuning tasks, with average F1 scores of  $84.06\%$  and  $87.22\%$ , demonstrating the efficacy of LAGaBi in multilingual scenarios. On the zero-shot transfer learning task, LAGaBi fell slightly behind its counterparts. This may be due to the inherent gaps between different languages, such as differences in reading order and semantic density. For example, English usually uses spaces to separate words and has uneven word lengths, while Chinese appears as a tighter sequence with smaller semantic units (i.e., characters). Such layout knowledge learned from a specific language shows limited contributions to other languages. This phenomenon also proves the effectiveness of LAGaBi in modeling layouts, i.e. it actually has acquired the layout knowledge for a specific language after fine-tuning on the corresponding data.
+
+# 4.4 Ablation Studies
+
+To investigate the impacts of our learnable Gaussian kernels and polar coordinates, we have con
+
+duced extensive ablation experiments based on several RoBERTa variants equipped with different layout encoding mechanisms (e.g. layout embedding layers, linear bias layers, and fixed/learnable Gaussian Kernels), and spatial relationships (e.g. distance, angle, and 2D-xy distance). All the variants are evaluated without further pre-training, and only fine-tuned for 2000 steps on FUNSD and CORD. Results on FUNSD's test set and CORD's validation set are shown in Table 3. From the result, we can observe that LAGaBi with learnable Gaussian kernels and polar coordinates (#8) can significantly outperform the baseline (#1) and the model with linear layout embedding layers (#2), indicating that models encoding layouts as attention biases are superior to layout embedding-based methods.
+
+Impact of the Gaussian kernels. To study the effects of various methods for converting polar coordinates to attention biases, we compared linear layers (#3), fixed Gaussian kernels (#4), and learnable Gaussian kernels (#8). The results demonstrate that linear layers are far less effective than Gaussian kernels, which is likely due to the fact that the Gaussian kernels are more in line with human
+
+![](images/acbeb1cef02eb76200a1cf61c2bc39a5fa2106feb98842a9778acc11239fe0e9.jpg)  
+Figure 2: Visualization of the attention maps. The word-word attention scores are obtained by aggregating token-level attention in the last layer of transformers. We also annotate the position and order of each word in the original document page, and the attention scores are rounded to two decimal places for better visualization.
+
+![](images/0161785d95db7d0dfc41b3c7d76f5f37cd54d1afd9fc2ad28f21c9d1982ea513.jpg)
+
+![](images/6989db1cdeefe9b10f61d6b4d16edecf0e49d27bce976180af2b668cd3820c94.jpg)
+
+<table><tr><td>Task</td><td>#</td><td>Ablation Strategy</td><td>FUNSD</td><td>CORD</td></tr><tr><td></td><td>1</td><td>RoBERTa(baseline)</td><td>66.48</td><td>92.29</td></tr><tr><td></td><td>2</td><td>+ embedding layers</td><td>69.59</td><td>92.67</td></tr><tr><td rowspan="2">Impact of Gaussian</td><td>3</td><td>+ linear bias layers</td><td>76.32</td><td>93.00</td></tr><tr><td>4</td><td>+ fixed kernels</td><td>83.81</td><td>93.00</td></tr><tr><td rowspan="3">Impact of Polar-Coor.</td><td>5</td><td>+ Euclidean distance</td><td>73.05</td><td>93.72</td></tr><tr><td>6</td><td>+ Angle</td><td>84.48</td><td>94.70</td></tr><tr><td>7</td><td>+ 2D-xy distance</td><td>79.85</td><td>94.21</td></tr><tr><td></td><td>8</td><td>+ LAGaBi</td><td>84.84</td><td>94.77</td></tr></table>
+
+intuition than linear layers. Learnable Gaussian kernels (#8) also achieve better performance than fixed Gaussian kernels whose mean is 0 and variance is 1 (#4), since the learnable Gaussian kernels enjoy better flexibility to adapt to different formats. Impact of the polar coordinates. Polar coordinates, which consist of two elements: distance and angle, is a typical technique for describing spatial relationships. We analyzed the effects of distance (#5) and angle (#6), as well as the classical 2-D relative horizontal and vertical distances proposed by TITL (Powalski et al., 2021) (#7). The results suggest that the model with angle information (#6) is more effective than the model with distance information (#5 and #7). We hypothesize that this is because angles are less impacted by size scaling than distances, but they are more sensitive to location changes. Furthermore, LAGaBi achieves much better performance than the model that employs horizontal and vertical distances, which further reveals the superiority of polar coordinates.
+
+# 4.5 Analysis
+
+Impact of  $\alpha$ . Hyper-parameter  $\alpha$  makes a tradeoff between semantic and layout contributions when computing pair-wise attention scores in our LABaBi, which is important. We conduct several experiments with different  $\alpha$  settings to study the impact of  $\alpha$  based on RoBERTa without any further pre-training.  $F_{1}$  scores on CORD's validation set are listed in Table 4, showing the model achieves its best performance when  $\alpha = 4$ .
+
+Table 3: Ablation studies on the effectiveness of the learnable Gaussian kernels and the polar coordinates.  
+
+<table><tr><td>α =</td><td>0</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr><tr><td>CORD</td><td>92.29</td><td>92.82</td><td>93.08</td><td>93.46</td><td>94.77</td><td>94.39</td></tr></table>
+
+Table 4: Impact of different  $\alpha$  settings. All the scores are from CORD's validation set.
+
+Visualization analysis. We visualize the word-level attention maps of the baseline RoBERTa and our RoBERTa+LAGaBi. Due to space limitations, in this paper, we only show the attention maps of the first 8 words in the input sequence. The case in Figure 2 is from the test set of FUNSD. As shown in Figure 2, the RoBERTa incorrectly associates the character "R." with all the words, while most other words are treated as unrelated. According to the attention map in the RoBERTa+LAGaBi, greater attention scores arise between "R." and "F.", "J." and "D.", all of which are placed in the signature area, whereas attention scores between remote irrelevant words such as "DATE" and "STRU" are zeros. This demonstrates that LAGaBi indeed learns more accurate semantic associations by incorporating layout information. More examples and detailed analysis can be seen in Appendix.A.
+
+# 5 Conclusion
+
+In this paper, we propose a model-agnostic and language-independent method that leverages Layout Attention with Gaussian Biases to encode the relative spatial positions for structured document understanding (SDU). Specifically, we first model the inter-word spatial relationships using polar coordinates. Then the query-key attention scores are revised by the Gaussian biases that are related to their spatial relationships. Our method can be applied to a series of Transformer-based models with extremely few parameters, improving their performance for SDU tasks. Experiments based on six transformer-based SDU models and three monolingual/multilingual benchmarks fully demonstrate the effectiveness of our proposal. This research provides new ideas for structured document understanding tasks, which are expected to promote the efficient development of document intelligence.
+
+# Limitations
+
+Despite the superior performance exhibited by LAGaBi, it does have some limitations. Firstly, in our experiments with the LayoutLM series that integrate multi-modal features, LAGaBi was only fine-tuned for validation without pre-training. We believe that leveraging multi-modal pre-training could further improve LAGaBi's performance based on LayoutLM, and this will be explored in future investigations. Secondly, although we have empirically demonstrated the effectiveness of polar coordinates and Gaussian distribution in layout learning, our motivation is driven by a simple intuition rather than rigorous mathematical proof.
+
+# Acknowledgements
+
+We sincerely thank all the anonymous reviewers for their valuable comments and suggestions.
+
+# References
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+Peizhao Li, Jieuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, and Hongfu Liu. 2021b. Selfdoc: Self-supervised document representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5652-5660.  
+Yulin Li, Yuxi Qian, Yuechen Yu, Xiameng Qin, Chengquan Zhang, Yan Liu, Kun Yao, Junyu Han, Jingtuo Liu, and Errui Ding. 2021c. Structext: Structured text understanding with multi-modal transformers. In Proceedings of the 29th ACM International Conference on Multimedia, pages 1912-1920.  
+Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.  
+Chuwei Luo, Changxu Cheng, Qi Zheng, and Cong Yao. 2023. Geolayoutm: Geometric pre-training for visual information extraction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7092-7101.  
+Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, and Hwalsuk Lee. 2019. Cord: a consolidated receipt dataset for post-ocr parsing. In Workshop on Document Intelligence at NeurIPS 2019.  
+Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, et al. 2022. Ernie layout: Layout knowledge enhanced pretraining for visually-rich document understanding. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3744-3756.  
+Rafał Powalski, Łukasz Borchmann, Dawid Jurkiewicz, Tomasz Dwojak, Michal Pietruszka, and Gabriela Pałka. 2021. Going full-tilt boogie on document understanding with text-image layout transformer. In Document Analysis and Recognition-ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part II 16, pages 732–747. Springer.  
+Ofir Press, Noah A Smith, and Mike Lewis. 2021. Train short, test long: Attention with linear biases enables input length extrapolation. arXiv preprint arXiv:2108.12409.  
+Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485-5551.  
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.
+
+Jiapeng Wang, Lianwen Jin, and Kai Ding. 2022a. Lilt: A simple yet effective language-independent layout transformer for structured document understanding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7747-7757.  
+Wenjin Wang, Zhengjie Huang, Bin Luo, Qianglong Chen, Qiming Peng, Yinxu Pan, Weichong Yin, Shikun Feng, Yu Sun, Dianhai Yu, et al. 2022b. mmlayout: Multi-grained multimodal transformer for document understanding. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4877-4886.  
+Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, et al. 2021a. Layoutmv2: Multi-modal pre-training for visually-rich document understanding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2579-2591.  
+Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. 2020. Layoutm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1192-1200.  
+Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, and Furu Wei. 2021b. Layoutxlm: Multimodal pre-training for multilingual visually-rich document understanding. arXiv preprint arXiv:2104.08836.
+
+# A Appendix
+
+Figure 3 in this section shows the attention distribution of four additional examples on FUNSD. From all the examples, it can be observed that the RoBERTa model typically treats each word in isolation, while RoBERTa+LAGaBi can learn the more accurate correlations between different words based on their layout relationships.
+
+We also visualize the word-level attention maps of LayoutLMv3 and LayoutLMv3+LAGaBi. LayoutLMv3 is currently the top-performing method for structured document understanding, which utilizes the 2-D relative positions through linear attention biases. From the attention maps shown in Figure 4, we can observe that LayoutLMv3+LAGaBi refers more to layout information when modeling the inter-word semantic correlations, while LayoutLMv3 is relatively independent. For example, in the third sample of Figure 4, LAGaBi learns more dense associations among "Tiers", "II.", and "&" in the neighborhood than LayoutLMv3, which is in line with human intuition.
+
+![](images/903c95f39a22e8025aa128c2921706d4eb5dea5705036df1a9eae46fb0c72410.jpg)
+
+![](images/cd0d87129439097797a1b581314654b9cc2aa903bd591128a3dc41864422e2f2.jpg)
+
+![](images/a48aea16930ec3a40831fdd3f204d75e7a241cf5a3233735b1107d8cb997e330.jpg)
+
+![](images/979758abe48e19b4ff5042cf20dac36801785fd72895e1f1e16a6795a47a0aab.jpg)
+
+![](images/17a54380952e9988a04ac48bc19da9771ef735455aa5010c68b5d092e40db72e.jpg)
+
+![](images/581aa816021be15f2eb97a0778f907dd50520579506720f5921c5ae7541058fe.jpg)
+
+![](images/0ef7f90d6685ef9569087e356b5a3ce5fb011f82fcede01161803c37a13e7de0.jpg)
+
+![](images/6b6f21c23f9410218481326ec1a4777f4f1cbc25740442353057334816bd5448.jpg)
+
+![](images/a58d8e90bc57e3a0597bf487f6ce16e25314db5e6e4771743136eeb34d706d77.jpg)
+
+![](images/820c7896ab0eb269f67c07ebe992751282368009f57867b008502dfd37ab9d8d.jpg)  
+Figure 3: Visualization of more examples based on RoBERTa.
+
+![](images/2035bf1163d8e846625bb9b306572796c65963b734808ac7b2ae8c3b4a9e3ff1.jpg)
+
+![](images/fceaf9bac74f2f698ca4ee8c66d7d12f55feb79a24e94b18ac782eba85447594.jpg)
+
+![](images/26e9e2060390ca1cc62c0481b4dfcc165b4b7f55f6caf19a237c74bc06e0703c.jpg)
+
+![](images/3f5a18375e510a380ac7ad47340ad33d6b30b94bf4e6d2d8556d6ec4aaf9a52c.jpg)  
+Attention Map of LayoutLMv3
+
+![](images/5d9f824eceb838ebc52c2784600b6fc5bf7b2ec5c865b16150471efc1458d2e0.jpg)  
+Attention Map of LayoutLMv3+LAGaBi
+
+![](images/3ed0b884e3896841f2f11dd6adf40a3ca5894236d9f6167c3baeb3d19b0656ff.jpg)
+
+![](images/1a4b52ad62e69e18779fcd61f03e98ab7e93f2ba037e409afa4f0d10b2186f3d.jpg)  
+Attention Map of LayoutLMv3
+
+![](images/bc608cdffcacade1c58e0fdfe1c99304fecba16281c4da31a14abe520d44d95c.jpg)  
+Attention Map of LayoutLMv3+LAGaBi
+
+![](images/718e93874932acae5942ba24a5863d6d9f8b674e9b4a3822692cbb6424cf3b2b.jpg)
+
+![](images/3f9afb43d3edb32c3ca017587ed463d9a1e9b272e213895f18f63b6a4ded74b6.jpg)  
+Attention Map of LayoutLMv3
+
+![](images/3d058190ed60280d9f356dcce16503493053b3271a09d9101e856330565b56f0.jpg)  
+Attention Map of LayoutLMv3+LAGaBi
+
+![](images/935e44a5917362fb0ee7fb4fa2e7da2ee6b3f0484a6372769be032e5e44f4907.jpg)  
+Figure 4: Visualization of examples base on LayoutLMv3.
+
+![](images/f44a5ec858eaf4732acc05e6f71b38bc30be6bb65877755518a8710fc30d2d16.jpg)  
+Attention Map of LayoutLMv3
+
+![](images/61a05c058e9592a17b6f691c042d716c245c0f2db78bc0847b84398f0a25f6fe.jpg)  
+Attention Map of LayoutLMv3+LAGaBi

2023/Beyond Layout Embedding_ Layout Attention with Gaussian Biases for Structured Document Understanding/images.zip

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2023/Beyond Testers’ Biases_ Guiding Model Testing with Knowledge Bases using LLMs/c8dbec2f-8ae8-4d52-9d1b-2529ec7e7ec7_origin.pdf

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2023/Beyond Testers’ Biases_ Guiding Model Testing with Knowledge Bases using LLMs/full.md

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+# Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
+
+Chenyang Yang $^{1}$  Rishabh Rustogi $^{1}$  Rachel Brower-Sinning $^{2}$  Grace A. Lewis $^{2}$  Christian Kästner $^{1}$  Tongshuang Wu $^{1}$
+
+$^{1}$ Carnegie Mellon University  $^{2}$ Carnegie Mellon Software Engineering Institute
+
+# Abstract
+
+Current model testing work has mostly focused on creating test cases. Identifying what to test is a step that is largely ignored and poorly supported. We propose WEAKER, an interactive tool that supports requirements elicitation for guiding model testing.1 WEAKER uses large language models to generate knowledge bases and recommends concepts from them interactively, allowing testers to elicit requirements for further testing. WEAKER provides rich external knowledge to testers, and encourages testers to systematically explore diverse concepts beyond their own biases. In a user study, we show that both NLP experts and non-experts identified more, as well as more diverse concepts worth testing when using WEAKER. Collectively, they found more than 200 failing test cases for stance detection with zero-shot ChatGPT. Our case studies further show that WEAKER can help practitioners test models in real-world settings, where developers define more nuanced application scenarios (e.g., code understanding and transcript summarization) using LLMs.
+
+# 1 Introduction
+
+Despite being increasingly deployed in real-world products, ML models still suffer from falsehoods (Maynez et al., 2020), biases (Shah et al., 2020), and shortcuts (Geirhos et al., 2020), leading to usability, fairness, and safety issues in those products (Liang et al., 2023; Nahar et al., 2023). For example, toxicity detection models are used by social media platforms to flag or remove harmful content, but their biases amplify harm against minority groups (Sap et al., 2019). As standard benchmarks are often too coarse to expose these issues, recent work has proposed to test nuanced behaviors of ML models (Ribeiro et al., 2020; Goel et al., 2021; Ribeiro and Lundberg, 2022).
+
+![](images/779d7ec85274e12211a16cda4adbdade3673e328ff261e024da375d510379884.jpg)  
+Figure 1: The V-model (Sommerville, 2015), a widely used development process in many fields of engineering, adapted for testing models within a system. This layering and planning is also compatible with more agile development approaches, where the different activities may be iterated in different ways but are still linked.
+
+Such model testing of nuanced behaviors usually requires translating behavior expectations into test cases (input-output pairs). To enable such test case creation, prior work has taken inspiration from the long-established software testing approaches: For example, in their CheckList framework, Ribeiro et al. (2020) used templates to form minimal functionality test cases, which was inspired by unit tests. Morris et al. (2020)'s work on editing inputs (e.g., synonym swap) for testing model invariances is akin to metamorphic testing. To enable testing generative models, Ribeiro (2023) have also explored specifying properties that any correct output should follow, similar to property-based testing.
+
+However, prior work has focused on how to write test cases, not what tests to write. In software engineering, tests are fundamentally grounded in requirements and design work, as commonly expressed in the V-model (Sommerville, 2015, Figure 1). Ideally, each test can be traced back to a requirement and all requirements are tested. Software engineering research has long established the importance of requirements for development and testing, and studied many approaches for requirements elicitation (Van Lamsweerde, 2009).
+
+In comparison, little work has explicitly supported identifying what to test in model testing. Researchers and practitioners seem to rely mostly
+
+on intuition and generic domain knowledge (McCoy et al., 2019; Dhole et al., 2021), or debug a small set of issues initially identified through error analysis (Naik et al., 2018; Wu et al., 2019b). Such approaches are often shaped heavily by individual knowledge and biases (Rastogi et al., 2023; Lam et al., 2023), driving practitioners to focus on local areas of a few related concepts where they find problems, while neglecting the larger space (Ribeiro and Lundberg, 2022), as exemplified in Figure 2. For example, to test toxicity detection models, practitioners may identify and test (1) racism with handcrafted test cases and (2) robustness to paraphrasing with CheckList. However, they are likely to miss many concepts in the space (e.g., spreading misinformation as a way to spread toxicity) if they have never seen or worked on similar aspects, as often observed in the fairness literature (Holstein et al., 2019).
+
+In this work, we contribute the concept and method of requirements engineering ("what to test") for improving model testing. We connect testing to requirements with WEAKER, an interactive tool that supports requirements elicitation for guiding model testing. Our goal is to provide comprehensive external knowledge, encouraging testers to systematically explore diverse concepts beyond their biases (Figure 2), while balancing the completeness of requirements collection and the effort of practitioners exploring these requirements. WEAKER systematically generates knowledge bases (KB) by querying large language models (LLMs) with well-defined relations from ConceptNet, allowing testers to elicit requirements for any use cases (as opposed to being limited by prebuilt KBs). For example, in Figure 3, from a seed concept "online toxicity", the LLM generates a KB showing "spreading misinformation" with relation "done via." To not overwhelm users and encourage iterative exploration, WEAKER shows only a subset of the concepts from the KBs, balancing relevance and diversity, while allowing users to still explore more concepts on demand.
+
+We demonstrate the usefulness and generality of WEAKER through a user study and case studies. In the user study (§4), users identified more, as well as more diverse concepts worth testing when using WEAKER. Collectively, they found more than 200 failing test cases for stance detection with zero-shot ChatGPT (gpt-turbo-3.5). Our case studies (§5) further show that WEAKER can help practitioners
+
+![](images/c5b2ad27e5b5b36c54e22e2af92554bd243ed0e4f618b64abcdabd1c7f894c33.jpg)  
+Figure 2: Local exploitation (left) vs. global exploration (right). Most model testing is opportunistic in terms of what to test, often "hill-climbing" by searching near existing problems, with the risk of getting stuck in local areas. In contrast, WEAVER supports exploring concepts globally across the entire space.
+
+![](images/b9af1b495d71671acfcd228db11cdef10c8ddb521550f5db46422046aa8ba962.jpg)
+
+test models in real-world settings, from transcript summarization to code understanding.
+
+# 2 WEAVER
+
+Our key idea is to provide a knowledge base to support systematic exploration of domain concepts to guide model testing. This allows testers to consider requirements broadly, mitigating their biases as testers otherwise tend to opportunistically explore concepts in local areas (Figure 2). Our tool, WEAYER, has three primary building blocks: (1) An LLM-generated knowledge base for a given testing task, for encouraging more systematic and diverse requirement elicitation beyond individual biases; (2) a graph-inspired recommendation paradigm that prioritizes diverse yet relevant concepts for user inspection; and (3) an intuitive interface that can be easily paired with any test case creation methods, for supporting users to interactively navigate the knowledge base(s) for their own purposes. Below, we walk through the design choices and corresponding rationales.
+
+# 2.1 LLM-generated Knowledge Base
+
+To support testers to explore different concepts relevant to the problem, WEAVER needs to provide comprehensive knowledge beyond individual testers' biases. As such, we choose to power WEAVER using knowledge bases (KBs) generated by LLMs. As LLMs store diverse world knowledge that can be easily extracted through prompting (Cohen et al., 2023), they empower WEAVER to support a wide range of domains, tasks, and topics.
+
+We start knowledge base construction with a seed concept, i.e., a user-provided high-level term that can represent their tasks well. These seeds can be as simple as the task name and description, or can be more customized depending on user needs. Using the seed, we will then automatically query an
+
+$\mathsf{LLM}^2$  to build a partial knowledge base of related concepts (Wang et al., 2020; Cohen et al., 2023). Specifically, we iteratively prompt LLMs for entities or concepts that have different relations to the queried concept, using fluent zero-shot prompts paraphrased from well-established relations used in knowledge bases. For example, prompting LLMs with "List some types of online toxicity." can help us extract specific TypeOf online toxicity. By default, we use 25 relations from ConceptNet $^3$  (Speer et al., 2017), e.g., MotivatedBy and LocatedAt, and manually curated corresponding zero-shot templates. These relations prove to be reusable across many different domains in our studies, but users can also specify custom relations they want to explore (a complete list of prompts in Appendix A).
+
+As the KB is used to support exploration (explained more below), we initially pre-generate two layers of the KB, and iteratively expand the knowledge base based on user interactions.
+
+# 2.2 Recommend Diverse & Relevant Concepts
+
+A comprehensive KB comes at a higher cost of exploration—a single tester can easily get overwhelmed if presented with the entire KB. To assist users to navigate through the KB more efficiently, we employ the “overview first, details-on-demand” taxonomy (Shneiderman, 1996): We provide initial recommendations as starting points to user explorations. In particular, we strive to make recommendations that are diverse (so as to bring diverse perspectives to users) but still relevant (so users do not feel disconnected from their goals).
+
+The trade-off between relevant and diverse resonates with the common exploration-exploitation trade-off in information retrieval (Athukorala et al., 2016), and can be naturally translated to a graph problem: If we have all candidate concepts to form a fully-connected graph, where edge weights are distances between different concepts and node weights are the concepts' relevance to the queried concept, then our goal becomes to recommend a diverse subset where concepts have large distances
+
+![](images/877453003a6db36eb68ba2a288442f0f3f4c08c73f44a68e405b707eea4427e4.jpg)  
+Figure 3: WEAVER interface, where users can interactively explore concepts in the LLM-generated KB to elicit requirements for model testing.  $①$  : seed concept,  $②$  : recommended children concepts with relations (translated to a user-friendly form),  $③$  : options for users to expand a concept or  $④$  : get more recommendations on any concepts,  $⑤$  : editing, creating, or removing concepts manually,  $⑥$  : selecting important concepts for testing.
+
+between each other, while they are still relevant in the sense that they occur frequently in the context of the queried concept. Essentially, we aim to find a subgraph  $G'$  of size  $k$  that maximizes a weighted  $(\alpha > 0)$  sum of edge weights  $w_{E}$  (diversity) and node weights  $w_{V}$  (relevance):
+
+$$
+\arg \max _ {G ^ {\prime} \subset G, | G ^ {\prime} | = k} w _ {E} (G ^ {\prime}) + \alpha \cdot w _ {V} (G ^ {\prime})
+$$
+
+To build the graph, we measure concept differences with cosine distance between concept embeddings using SentenceBERT (Reimers and Gurevych, 2019), and measure relevance with the perplexity of sentence " $\{\text{concept}\}$  often occurs in the context of  $\{\text{queried\_concept}\}$ ", using GPT-2 (Radford et al., 2019). Since finding the optimal subgraph is computationally expensive, we apply the classic greedy peeling algorithm (Wormald, 1995) to approximate it in linear time. That is, we greedily remove nodes with the smallest weights (sum of node and all edge weights) one at a time until the graph size reaches  $k$  ( $= 10$  for initial recommendation, but grows with user expansion). We empirically show that the recommended concepts are of high quality and diverse in our evaluation.
+
+# 2.3 Interactive Interface for Exploration
+
+Besides the recommended starting points, we allow users to iteratively and interactively locate their concepts of interest. WEaver visualizes the knowledge base in a tree structure (Figure 3), a representation that is familiar to most ML practitioners.
+
+The knowledge base starts with  $①$  the seed concept users specify, with each recommended concept represented as  $②$  a node in the tree, accompanied by its relation to the parent concept. The interface allows users to identify concepts by  $③$  diving deeper and  $④$  exploring broadly before  $⑥$  selecting a concept to test. Alternatively, users can also distill personal knowledge by  $⑤$  creating concepts manually.
+
+To assist users in creating concrete test cases, WEAVER incorporates AdaTest (Ribeiro and Lundberg, 2022) as the default test case creation method, which uses LLMs to suggest test cases. However, the design of WEAVER is compatible with any other techniques to test models once requirements are identified (e.g., Zeno, Cabrera et al., 2023). The full interface including the AdaTest integration can be seen in Appendix B.
+
+# 3 Intrinsic Evaluation
+
+As the primary goal of WEAKER is to provide external knowledge to guide testing, it is important that the knowledge provided is comprehensive in the first place. Here, we quantitatively evaluate:
+
+Q.1 How comprehensive are the knowledge bases generated by WEAYER?
+
+Tasks, data, and metrics. We select four tasks for the evaluation: Hateful meme detection, Pedestrian detection, Stance detection for feminism, and Stance detection for climate change (task descriptions in Appendix C). These tasks cover diverse domains and modalities, and importantly, provide us with gold concepts that can be used to evaluate our LLM-generated KB. The first two tasks have been studied in prior work, and we directly use their ground-truth concepts collected from existing documents (Barzamini et al., 2022b) and user studies (Lam et al., 2023). For the last two tasks, we aggregate all concepts identified by 20 participants without using WEAKER as part of our user study (discussed later in §4), which we consider as our ground truth. Intuitively, such aggregation should help represent what concepts are generally deemed important. As shown in Table 1, the tasks have on average 144 ground-truth concepts. $^{4}$
+
+Independently, we generated a knowledge base for each task using WEAKER with default relations. We derived the seed concepts directly from the task names: (1) "hateful meme", (2) "pedestrian", (3) "feminism", and (4) "climate change."
+
+We evaluate the comprehensiveness of the generated knowledge using recall, i.e., the fraction of existing concepts that also appear in the KB. Since there are many phrasing variations of the same concept, we decide that a concept is in the KB if it appears exactly the same in the KB, or our manual check decides that it matches one of the 10 most similar concepts from the KB, as measured by the cosine distance (cf. §2.1). We established that the manual process is reliable by evaluating inter-rater reliability where two authors independently labeled a random sample of 50 concepts, finding substantial agreement ( $\kappa = 69.4\%$ ).
+
+We also evaluate the validity of the generated knowledge using precision, i.e., the fraction of KB edges that are valid. Note that because our ground truths are incomplete by nature (collected from dataset analysis and user study), KB edges that are not in the ground truths can still be valid. Following prior work (Cohen et al., 2023), we performed manual validation on sampled KB edges. We sampled 50 edges from each of the four generated KBs.
+
+Results Overall, our KBs cover  $91\%$  of ground-truth concepts on average (Table 1), with  $81\%$  of sampled generated edges being valid. Qualitatively, we found that there are two distinct types of concepts the KB failed to cover: First, there are some very specific concepts (e.g., old photo in hateful meme detection). Although the 2-layer KB does not cover them, it does often cover their hypernyms (e.g., photo). Therefore, these concepts can be discovered if users choose to explore deeper. Second, some concepts are interactions of two concepts (e.g., fossil fuel uses in developing countries in climate change stance detection). These can be identified by users manually, as both of their components (fossil fuel uses and developing countries) usually already exist in the KB.
+
+# 4 User Study
+
+Does WEAVER support effective requirements elicitation? We conduct a user study to evaluate:
+
+Q.2 To what degree does WEaver help users explore concepts faster?  
+Q.3 To what degree does WEaver help users explore concepts broadly?  
+Q.4 How much does WEAVER mitigate user biases when exploring concepts?
+
+We expect that our interaction design (§2.3) supports faster exploration (Q.2) and that the recommendations (§2.2) support broader and less biased
+
+<table><tr><td>Task</td><td>Recall</td><td>Precision</td><td># Concept</td></tr><tr><td>Hateful meme detection</td><td>93.1%</td><td>88.0%</td><td>101</td></tr><tr><td>Pedestrian detection</td><td>91.8%</td><td>74.0%</td><td>146</td></tr><tr><td>Stance detection for feminism</td><td>86.9%</td><td>84.0%</td><td>145</td></tr><tr><td>Stance det. for climate change</td><td>91.4%</td><td>76.0%</td><td>185</td></tr><tr><td>Average</td><td>90.6%</td><td>80.5%</td><td>144</td></tr></table>
+
+Table 1: Knowledge bases generated by WEAVER cover  ${90.6}\%$  of existing concepts on average.
+
+exploration (Q.3 and Q.4).
+
+# 4.1 Study Design
+
+Conditions. We design an IRB-approved user study as a within-subject controlled experiment, where participants test models in two conditions: A treatment condition, where users use WEAVER to find concepts for testing, and a control condition, where users add the concepts manually while they explore test cases. In both conditions, users have access to AdaTest's LLM-based test case suggestions (cf. §2.3). In essence, the control interface is a re-implementation of AdaTest with WEAVER's interface and interaction experience.
+
+Tasks and models. We select two tasks of similar difficulty for our user study: Stance detection for feminism, and stance detection for climate change. They are accessible to participants from different backgrounds. We had participants test the performance of zero-shot ChatGPT (OpenAI, 2022) for both tasks, as we observed that it easily outperformed any available fine-tuned models on Huggingface—the latter failed at simple test cases (full prompts in Appendix A).
+
+Procedure. We recruited 20 participants (graduate students with varying ML/NLP experience, details in Appendix D.1) for a 90-minute experiment session. We started by walking through the study instructions and asked them to try WEAYER in an interactive tutorial session. Then participants tested the two aforementioned stance detection models for 30 minutes each, one in the treatment condition and one in control condition. To mitigate learning effects, we use a Latin square design (Box, 2009) with four groups, counterbalancing (1) which condition a participant encounters first, and (2) which model is tested first. Within each session, they were first asked to perform model testing for 25 minutes, and then identify (select or create) concepts worth future testing for 5 minutes. The first phase
+
+of model testing is designed to ground participants in what concepts are worth testing. The second phase of concept exploration is designed to approximate a longer time of model testing. This final design was derived from an earlier pilot study, where we observed that writing test cases for each concept took more time than identifying interesting concepts (WEAVER's objective). In the end, participants filled out a post-study survey (details in Appendix D.2). Participants were compensated for their time.
+
+Metrics and analysis. We use two measurements to approximate participants' exploration procedure: (1) the number of concepts they explore (representing exploration speed, Q.2), and (2) the number of distinct concepts they explore (Q.3).
+
+Specifically, for distinctiveness, we want to distinguish the local vs. global exploration patterns (cf. Figure 2), which requires us to locate clusters of similar concepts, or concepts that only differ in granularity. Quantitative, this is reflected through inter-relevance between concepts, e.g., rising sea level should be considered close to sea surface temperature increase but distinct from waste management. To find a set of distinct concept clusters, we again measure the concept distance using SentenceBERT, and run Hierarchical Clustering (Ward Jr, 1963) on all available concepts collectively selected or created by our 20 user study participants, which, as argued in §3, forms a representative set of what end users may care about for a given task. Note that we do not use all concepts from our KB for clustering as it would influence the ground truth. Hierarchical clustering allows us to choose concept clusters that have similar granularities using a single distance threshold. Empirically, we use the threshold of 0.7, which produces reasonably distinct clusters for both tasks (41 and 46 clusters for feminism and climate change respectively with an average size of 6.1 concepts).
+
+![](images/ce7ffd94e4ade9ad7247ec6156ea41e997014418078ee15bff7c911615a0aaec.jpg)  
+Figure 4: Participants with WEAKER identified more concepts and hit more clusters.
+
+![](images/579cbacdbfd05810bf24218c05579c696c58df73bc61df152e043537da2eb1b5.jpg)  
+Figure 5: Participants without WEAKER converged to hitting previously identified concept clusters as they identified more concepts. The gap between the two groups widened over the process.
+
+As such, distinctiveness is represented by the number of hit cluster in each user's exploration.
+
+We analyze both measurements with a repeated measures ANOVA analysis, which highlights the significance of our test condition (whether participants use WEAKER) while considering the potential impact from other independent variables. In our analysis, we test to what degree our tool, the task, and the order (tool first or tool last) explain variance in the outcome variables. Detailed analysis results can be found in Appendix D.3.
+
+# 4.2 Results
+
+WEAVER helps users identify more concepts (Q.2). We first observe that with WEAVER, participants identified  $57.6\%$ $(p < 0.01)$  more concepts (Figure 4). This is likely because users can more easily explore a wider range of concepts with the external knowledge base, as confirmed by participant P6: "... (KB) gives ideas beyond what I had in mind so I explored a wider base of concepts."
+
+We also observe that bug-finding is relatively independent of concept exploration. On average, participants found around 11 failing test cases (0.44 per minute), regardless of whether they used WEAKER or not. Since testing is orthogonal, participants who explore more concepts will find more failing test cases. We expect that if participants test longer, those with WEAKER will find more bugs while others will run out of concepts to test.
+
+![](images/4d8806e78e9fba5f2ffdc8be4d0e3dd6fd90f8a77817f58332e9edea66e9f11c.jpg)  
+Figure 6: We project the SentenceBERT embeddings of concepts explored by two participants (P15 and P16) into a 2D space, using t-SNE (van der Maaten and Hinton, 2008). Without WEAVER, participants explore less space, as they performed more local exploitation than global exploration.
+
+WEAVER helps users cover more concept clusters (Q.3). More interestingly, we observe that with WEAVER, participants not only found more concepts but also covered  $47.7\%$ $(p < 0.01)$  more clusters in the space, i.e., they also explored more diverse concepts (Figure 4). This aligns with the survey responses, where  $80\%$  of participants agree that WEAVER helps them test the model more holistically and  $76\%$  of participants agree that WEAVER helps them find more diverse model bugs. We conjecture that this is because users with WEAVER explore more concepts not only in quantity (Q.2) but also in diversity, which is confirmed by many participants, e.g., "... (KB) encouraged me to explore more areas of the domain than I would have otherwise considered" (P10).
+
+Looking at their exploration trajectory (Figure 5), we see evidence indicating that WEAKER enables users to continuously discover distinct concepts. In contrast, participants in the control condition converged to hitting previously identified concept clusters as they identified more concepts. We observed that without WEAKER, participants tended to refine existing concepts later in the exploration, as exploring new distinct areas becomes increasingly difficult.
+
+These contrasting trajectories eventually lead to different exploration results. As reflected in Figure 6, the participant without WEAVER performed noticeably more local exploitation, finding highly related concepts in local areas, whereas WEAVER helped the other participant explore more diverse
+
+Figure 7: Visualizing the identified concepts for feminism by their corresponding clusters, we see participants in different conditions had different focuses in model testing. This shows that WEAKER suggests concepts complementary to human intuitions. In each test, the red strike-through labels are the wrong model predictions and the green ones are user-specified ground-truths.  
+![](images/920fd22c465277c07d10d95b694d315dd54e79017e6a6a0a85ce2b995ca9e6f8.jpg)  
+[Violence]: Verbal abuse of women is not as bad as physical violence. None against  
+[Sexual Harassment]: Sexual harassment in the workplace against women should not be tolerated. Sexual harassment against men is not a big deal. #MeToo favor against  
+[STEM]: Women are underrepresented in STEM due to a lack of access to education and resources. none favor  
+[Admission]: It's not important to ensure that admissions processes are free of gender bias and discrimination.
+
+concepts globally, without losing the ability to dive deeper into a few local areas.
+
+WEAVER shifts users towards a different concept distribution (Q.4). We also observe that participants collectively explored different concepts with WEAVER, as shown in Figure 7. Some concepts (e.g., violence) are much more explored by participants with WEAVER. This suggests that WEAVER helps mitigate participants' own biases and explore new concepts, as supported by participant P13: "... (KB) gives some inspiration in concepts I would not have otherwise thought of..."
+
+That said, WEaver also brings in its own biases, e.g. participants with WEaver rarely explored concepts like STEM compared to those without, possibly because they were too heavily anchored by the suggested concepts. This indicates that humans and knowledge bases are complementary – future work can support humans to better exploit their prior knowledge while exploring diverse concepts.
+
+# 5 Case Studies
+
+Using two case studies, we demonstrate that WEAVER can help practitioners test their own models and find various bugs in real-world settings, and has the potential to provide support beyond post-hoc model testing. In the studies, we provided sufficient supports to the practitioners, including
+
+integrating WEAKER into their natural evaluation environment (Jupyter Notebook), joining each user to explore their models for approximately three hours, and offering feedback and discussions whenever necessary (e.g., as practitioners brainstormed their seed concepts).
+
+Case selection. We approached researchers in our contact network to identify projects that were actively developing and improving models to be integrated into software products, such that (1) model testing has real stakes for them, and (2) the model needs to meet real-world requirements of a product, not just succeed on a standard benchmark.
+
+We ended up recruiting practitioners working on two projects that matched our scope to try WEaver. The first practitioner (C1) is building a pipeline for knowledge transfer, where they prompt an LLM to summarize content from transcripts into instructions. The second practitioner (C2) is building an IDE plugin to help developers understand unfamiliar code, developing LLM prompts to generate text summary for code segments. While these are two distinct scenarios, their shared challenge is that for practitioners working on novel tasks, it is often non-trivial to perform prompt engineering, especially because they do not have appropriate datasets for evaluating their prompts.
+
+The case studies were IRB-approved and participants were compensated for their time.
+
+Weaver supports quick and effective iterations. Both C1 and C2 started with a seed concept and then refined the seed concept at least once based on their observations. For example, C2 first tried seed concepts "challenges for summarizing a code script" and "reasons why people look for code summary", finding the recommended concepts generic and not their major concerns. They self-reflected through the process and identified the key scenario their plugin wants to support: (1) it targets novice programmers and (2) the most important application domain is data visualization. After this, they tried the seed concept "specific challenges that novice programmers might have in comprehending data visualization code" and found recommended concepts much more helpful.
+
+WEAVER helps practitioners find new model bugs by augmenting their existing analyses. While we did not have participants rate each concept they explored, based on their think-aloud reflection, we note that they were able to find
+
+many helpful concepts in a short amount of time. Even though practitioners have been working on their (LLM-backed) models for a while, they both obtained new insights into their models. First, WEAVER helped them observe issues they did not consider before. For example, in the seven concepts C1 tested, they found that the resulting instructions are always chronological even when there are detours in the input and steps reordering is desired. Second, WEAVER also helped them turn their prior, often fuzzy knowledge of problems or requirements into concrete testable concepts. For example, C1 turned their vague notion "useful summaries should not take transcripts literal" into concrete theories, including "behind the transcript, there is a hidden thought process important for identifying key action steps." Third, they were able to confirm model deficiencies they already suspected through systematic tests (e.g., "transcript summaries are often too verbose"). Similarly, C2 tested seven concepts and found "different parameters for customization" and "when to use different data visualization APIs" particularly novel and insightful.
+
+Notably, while C1 used AdaTest for testing models on different concepts, C2 reused test cases from their existing datasets, showing WEAKER's flexibility with different test case creation techniques. That C2 still discovered new insights within their own dataset demonstrates WEAKER's capability for encouraging nuanced testing following requirements.
+
+WEAVER is useful beyond testing models after the-fact. While we mostly position WEAVER as a model testing tool, we find that its support for requirements elicitation supports the entire model development cycle (cf. the V-model, Fig. 1).
+
+Although practitioners sometimes found it initially challenging to define seed concepts, they found the process itself valuable. For example, C2 eventually settled on "specific challenges that novice programmers might have in comprehending [domain] code"; they self-reflected how finding a good seed nudged them to state their goal explicitly for the first time. For them, this reflection happened too late to radically redesign their product, but it shows that WEAKER has the potential to support early-stage requirements engineering both for products and models. Meanwhile, C1 was inspired by concepts identified with WEAKER on model improvement. They experimented with different changes to prompts, encoding context for concepts they found challenging (e.g., step ordering).
+
+# 6 Related Work
+
+Requirements elicitation. Requirements engineering has been extensively studied (Van Lamsweerde, 2009). Despite many calls for the importance of requirements in ML (e.g., Rahimi et al., 2019; Vogelsang and Borg, 2019), requirements in ML projects are often poorly understood and documented (Nahar et al., 2022), which means that testers can rarely rely on existing requirements to guide their testing. Requirements elicitation is usually a manual and laborious process (e.g., interviews, focus groups, document analysis, prototyping), but the community has long been interested in automating parts of the process (Meth et al., 2013), e.g., by automatically extracting domain concepts from unstructured text (Shen and Breaux, 2022; Barzamini et al., 2022a). We rely on the insight that LLMs contain knowledge for many domains that can be extracted as KBs (Wang et al., 2020; Cohen et al., 2023), and apply this idea to requirements elicitation.
+
+Model evaluation, testing, and auditing. Recent work on ML model evaluation (e.g., Ribeiro et al., 2020; Goel et al., 2021; Röttger et al., 2021; Yang et al., 2022) has pivoted from i.i.d. traditional accuracy evaluation to nuanced evaluation of model behaviors. As surveyed in our prior work (Yang et al., 2023), this line of research uses various test creation techniques, including slicing, perturbations, and template-based generation. While providing many useful tools, these approaches often assume an existing list of requirements and rarely engage with the question of what to test. Existing research relied mostly on the knowledge of particular researchers, resulting in incomplete and biased requirements. For example, Ribeiro et al. (2020) explicitly state that their list of requirements in CheckList is not exhaustive and should be augmented by users with additional ones that are task-specific. Through LLM-assisted requirements elicitation, WEAVER helps users identify what to test systematically.
+
+Various alternative methods have been proposed for identifying what to test. For example, error analysis (e.g., Naik et al., 2018; Wu et al., 2019a) and slice discovery (Eyuboglu et al., 2022) can help identify issues in existing datasets, but datasets are often incomplete and biased (Rogers, 2021), and can even be missing for emerging LLM applications where no dataset has been
+
+pre-collected. Dataset-agnostic approaches like adaptive testing (Ribeiro and Lundberg, 2022; Gao et al., 2022) help users iteratively ideate concepts abstracted from generated test cases, but, as we confirmed, users tend to explore only local areas. These approaches engage in bottom-up style elicitation, which is reactive and may fare poorly with distribution shift. In contrast, WEAVER engages in top-down style elicitation, a more proactive process grounded in an understanding of the problem and domain.
+
+Furthermore, algorithmic auditing (Metaxa et al., 2021) elicits concerns from people with different backgrounds, usually on fairness issues, to avoid being limited by the ideas of a single tester. However, it can be challenging to recruit, incentivize, and scaffold the auditors (Deng et al., 2023). In a way, WEAKER might complement such work by providing diverse requirements for individual testers or crowd auditors.
+
+# 7 Discussion and Conclusion
+
+In this work, we propose WEAKER, a tool that uses knowledge bases to guide model testing, helping testers consider requirements broadly. Thorough user studies and case studies, we show that WEAKER supports users to identify more, as well as more diverse concepts worth testing, can successfully mitigating users' biases, and can support real-world applications. Beyond being a useful testing tool, the underlying concept of WEAKER have interesting implications on ML model testing and development, which we detail below.
+
+Model testing in the era of LLMs. Throughout our user studies and case studies, we focused on testing "models" achieved by prompting LLMs. Here, we would like to highlight the importance of requirements in such cases. LLMs are increasingly deployed in different applications, and traditional model evaluations are becoming less indicative. With these models trained on massive web text, it is unclear what should be considered as "in-distribution evaluation data." Instead, the evaluation objectives heavily depend on what practitioners need, which should be reflected through well-documented requirements.
+
+Meanwhile, as most practitioners are not NLP experts, they face challenges articulating how and what they should test about their prompted models (Zamfirescu-Pereira et al., 2023). As their use cases become more nuanced, it is also less likely for
+
+them to find pre-existing collections on important concepts. As such, enabling each individual to identify what-to-test is essential. We hope WEAKER can be used for democratizing rigorous testing, just as LLMs democratized access to powerful models. Still, currently WEAKER relies purely on practitioners to identify requirements worth testing, which may result in mis-matched requirement granularity (cf. §3). Future work can explore more complex structures that can represent knowledge (e.g., from KBs to knowledge graphs), and advanced recommendation mechanisms for practitioners to find the best requirements to explore first.
+
+Though we position WEAKER to ground model testing in requirements, we expect it to be useful also in other development stages (cf. §5). For example, we expect that it can help developers think about high-level goals and success measures for their products (Rahimi et al., 2019; Passi and Barocas, 2019), to guide development early on. For example, building on the observation that requirement-based testing may help practitioners perform prompt engineering, we envision that future practitioners can use WEAKER for rapid prototyping, where they identify unique requirements, pair them with corresponding test cases, and achieve better overall performance either through ensembled prompts (Pitis et al., 2023) or prompt pipelines (Wu et al., 2022). Moreover, elicited model requirements themselves can serve as descriptions and documentation, which can foster collaboration and coordination in interdisciplinary teamwork (Nahar et al., 2022; Subramonyam et al., 2022). Notably, we believe WEAKER can support such iterations because it is built to be lightweight. In prior research, requirements engineering has sometimes been criticized to be too slow and bureaucratic, making developers less willing to dedicate time to this step. In contrast, WEAKER allows developers to easily adjust their exploration directions (through seeds and interactions), which makes it feasible to be integrated into more agile and iterative development of ML products where requirements are evolving quickly.
+
+# Limitations
+
+Availability of domain knowledge in LLMs. LLMs encode a vast amount of knowledge, but may not include very domain-specific knowledge for specialized tasks, very new tasks, or tasks where
+
+relevant information is confidential. Our technical implementation fundamentally relies on extracting knowledge from LLMs and will provide subpar guidance if the model has not captured relevant domain knowledge. Conceptually our approach to guide testing with domain knowledge would also work with other sources of the knowledge base, whether manually created, extracted from a text corpus (Shen and Breaux, 2022; Barzamini et al., 2022a), or crowdsourced (Metaxa et al., 2021).
+
+Impacts from biases in LLMs. WEAKER uses LLMs to build knowledge bases such that users can elicit diverse requirements. However, LLMs themselves are found to be biased, sometimes untruthful, and can cause harm (Nadeem et al., 2021; Kumar et al., 2023). Therefore, users should carefully interpret results from WEAKER in high-stake applications.
+
+Threats to validity in human-subject evaluations. Every study design has tradeoffs and limitations. In our evaluation, we intentionally combined multiple different kinds of user studies to triangulate results.
+
+First, we conducted a user study as a controlled experiment. While the results are very specific and created in somewhat artificial settings and must be generalized with care (limited external validity), the study design can enact a high level of control to ensure high confidence in the reliability of the findings in the given context with statistical techniques (high internal validity). For example, regarding external validity, results may not generalize easily to other tasks that require different amounts of domain understanding or are differently supported by the chosen test case creation technique, and our participant population drawn from graduate students with a technical background may not equally generalize to all ML practitioners. There are also some threats to internal validity that remain, for example, despite careful control for ordering and learning effects with a Latin square design and assuring that the four groups were balanced in experience ('years of ML experience' and 'NLP expertise' asked in the recruitment survey before assignment), we cannot control for all possible confounding factors such as prior domain knowledge, gender, and motivation. In addition, we rely on clustering and similarity measures among concepts for our dependent variables, which build on well-established concepts but may not always
+
+align with individual subjective judgment.
+
+Second, we conducted case studies in real-world settings with practitioners (high external validity) but can naturally not control the setting or conduct repeated independent observations (limited internal validity). With only two case studies, generalizations must be made with care.
+
+This tradeoff of external and internal validity is well understood (Siegmund et al., 2015). Conducting both forms of studies allows us to perform some limited form of triangulation, increasing confidence as we see similar positive results regarding WEAKER's usefulness for discovering diverse concepts.
+
+Subjectivity in human judgments. All model testing requires judgment whether a model's prediction for a test example is correct. We noticed that user study participants and sometimes also case study practitioners struggled with determining whether model output for a specific test example was a problem, and multiple raters may sometimes disagree. For our purposes, we assume it is the tester's responsibility of identifying which model outputs they consider problematic, and we do not question any provided labels. This, however, reminds us that like data annotation (Santy et al., 2023), any model testing process will likely bring in testers' biases, as they get to decide what is right and what is wrong. In practice, a broader discussion among multiple stakeholders may be required to identify what model behavior is actually expected and a decomposition of model testing using requirements might be helpful to foster such engagement.
+
+# Ethics Statement
+
+Research Reproducibility. While our experiments are mostly conducted on the closed API (text-davinci-003) provided by OpenAI, none of the conceptual contributions of our paper relate to specific models or APIs. The concrete evaluation results depend on how humans interact with specific models, but the approach can be used with other models. Indeed, our extra experiments with llama-2-13b-chat on the climate change task show that the generated concepts from open-source models achieve substantial levels of recall (83% vs. 91% originally). This supports that the idea behind WEAKER is reproducible. While users may see somewhat different KBs through different runs of the same/different LLMs, they get similar chances of seeing useful concepts, receive a similar level of
+
+support on requirement elicitation (our core contribution), and will be able to yield similar model testing effectiveness.
+
+Human-subject Experiments. Our studies had been approved by our IRB before it was conducted, as is standard practice for human-subject experiments. We recruited all participants through emails, and all of them are graduate students with varying ML/NLP experience (see details in Appendix D.1). The participants were compensated for their time (\(20 per hour). As part of testing, they may write or review text that is abusive, dangerous, hateful, or offensive—they were made aware of this fact and could end participation at any time.
+
+# Acknowledgements
+
+This work was supported in part by the National Science Foundation (#2131477) and gift funds from Adobe, Oracle, and Google. Work by Brower-Sinning and Lewis was funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center (DM23-2019).
+
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+
+# A Complete List of Used Prompts
+
+# Prompts for expanding knowledge bases
+
+{context}
+
+{list_prompt} Pay attention to the context above. Summarize in a JSON list.
+
+'''json
+
+Example list prompts
+
+TYPEOF: List  $\{\mathsf{N}\}$  types of {concept}.
+
+PARTOF: List  $\{\mathsf{N}\}$  parts or aspects of {concept}.
+
+HASPROPERTY: List  $\{\mathsf{N}\}$  descriptions of {concept}.
+
+USEDFOR: List {N} things {concept} could be used for.
+
+ATLOCATION: List {N} locations {concept} could appear in.
+
+CAUSES: List  $\{\mathsf{N}\}$  consequences of {concept}.
+
+MOTIVATEDBY: List {N} motivations behind { concept}.
+
+OBSTRUCTEDBY: List {N} things, entities, or people against {concept}.
+
+MANNEROF: List  $\{\mathsf{N}\}$  ways to do {concept}.
+
+LOCATEDNEAR: List {N} things that often locates near {concept}.
+
+CAPABLEOF: List  $\{\mathsf{N}\}$  things that {concept} is capable of.
+
+HASSUBEVENT: List  $\{\mathsf{N}\}$  subevents of {concept}.
+
+HASPREREQUISITE: List {N} things that happen before {concept}.
+
+DESIRES: List  $\{\mathsf{N}\}$  things that {concept} desires.
+
+CREATEDBY: List  $\{\mathsf{N}\}$  creators of {concept}.
+
+SYMBOLOF:List  $\{N\}$  symbols of {concept}
+
+CAUSESDESIRe: List  $\{N\}$  desires caused by { concept}.
+
+MADEOF: List  $\{\mathsf{N}\}$  materials of {concept}.
+
+RECEIVESACTION: List  $\{\mathsf{N}\}$  actions that can be done to {concept}.
+
+DESIREDBY: List  $\{\mathsf{N}\}$  entities or people that desire {concept}.
+
+CREATES: List  $\{\mathsf{N}\}$  things that {concept} creates.
+
+CAUSEDBY: List  $\{\mathsf{N}\}$  things that cause {concept}.
+
+DONEBY: List {N} entities or people that can do {concept}.
+
+DESIReCAUSEDBY: List  $\{N\}$  things that cause desire of {concept}.
+
+DONETO: List {N} entities or people that { concept} can be done to.
+
+RELATEDTO: List {N} concepts related to {concept }.
+
+Example contexts prompts
+
+TYPEOF: {concept} is a type of {parent_concept}.
+
+PARTOF: {concept} is a part of {parent_concept}.
+
+HASPROPERTY: {parent_concept} is described as { concept}.
+
+USEDFOR: {parent_concept} is used for {concept}.
+
+ATLOCATION: {parent_concept} locates at {concept}.
+
+CAUSES: {parent_concept} causes {concept}.
+
+MOTIVATEDBY: {parent_concept} is motivated by { concept}.
+
+OBSTRUCTEDBY: {parent_concept} is obstructed by {concept}.
+
+MANNEROF: {concept} is a way to do {parent_concept}.
+
+LOCATEDNEAR: {concept} locates near {parent_concept}.
+
+CAPABLEOF: {parent_concept} is capable of { concept}.
+
+HASSUBEVENT: {concept} happens during {parent_concept}.
+
+HASPREREQUISITE: {concept} happens before {parent_concept}.
+
+DESIRES: {parent_concept} desires {concept}.
+
+CREATEDBY: {concept} creates {parent_concept}.
+
+SYMBOLOF: {concept} is a symbol of {parent_concept}.
+
+CAUSESDESIRe: {parent_concept} causes desire of {concept}.
+
+MADEOF: {parent_concept} is made of {concept}.
+
+RECEIVESACTION: {parent_concept} receives action of {concept}.
+
+DESIREDBY: {concept} desires {parent_concept}.
+
+CREATES: {parent_concept} creates {concept}.
+
+CAUSEDBY: {concept} causes {parent_concept}.
+
+DONEBY: {concept} does {parent_concept}.
+
+DESIReCAUSEDBY: {concept} causes desire of { parent_concept}.
+
+DONETO: {parent_concept} is done to {concept}.
+
+RELATEDTO: {concept} is related to {parent_concept}.
+
+```yaml
+# Prompts for the tested models in the user study
+# Stance detection on feminism
+Carefully classify a sentence's stance on feminism. The labels are \"favor\", \"against\” or \"none\". Only reply with the label.
+Sentence: {example}
+# Stance detection on climate change
+Carefully classify a sentence's stance on combating climate change. The labels are \"favor\", \"against\” or \"none\". Only reply with the label.
+Sentence: {example}
+```
+
+The path contexts in our full prompt are used to mitigate the issue of polysemy and deviation when users explore deeper in the graph.
+
+# SURVEY QUESTIONS
+
+- Rate whether you agree with the statement: Concept knowledge graph helps me find more diverse model bugs. Write a justification for your rating.  
+- Rate whether you agree with the statement: Concept knowledge graph helps me find more important model bugs. Write a justification for your rating.  
+- Rate whether you agree with the statement: Concept knowledge graph helps me test the model more holistically. Write a justification for your rating.  
+- Rate whether you agree with the statement: I want to use the tool with knowledge graph to test the model I build/use in the future.. Write a justification for your rating.  
+- If you want to use the tool in the future, what is the model and task you want to use it for?  
+- Feedback on how to improve the tool in the future.
+
+Figure 8: Post-study survey questions.
+
+# B User Interface
+
+In Figure 9, we show the complete user interface.
+
+# C Task Descriptions
+
+Hateful meme detection. Hateful meme detection (Kiela et al., 2020) requires classifying an image (meme with text) as hateful or non-hateful. This task is challenging in that it requires multimodal reasoning in order to classify the original meme and its confounders correctly
+
+Pedestrian detection. Pedestrian detection (Zhang et al., 2017) requires detecting and localizing pedestrians in images. Though it is one of the longest-standing problems in computer vision, people still observe generalizability issues in existing detectors (Hasan et al., 2021).
+
+Stance detection. Stance detection requires classifying texts as either being in favor of, against, or neutral toward the given target (Mohammad et al., 2016). The task is crucial for understanding the public's perception of given targets We selected two targets for our evaluation: feminism and climate change, which are previously explored for Tweets (Mohammad et al., 2016).
+
+# D User Study
+
+# D.1 Study Design Details
+
+Participants. We recruited 20 participants (graduate students with varying ML/NLP experience). Among them,  $70\%$  have worked on ML for more than three years;  $80\%$  rate themselves as at least somewhat familiar with NLP;  $60\%$  are working on a project using NLP at the time of the study. We randomly assigned participants to the four experimental groups. The four groups are comparable in terms of ML/NLP experience and familiarity. The participants were compensated $20 per hour.
+
+# D.2 Post-study Survey
+
+We share our survey questions (Figure 8) and users' responses (Table 2).
+
+# D.3 Quantitative Analysis
+
+We show the ANOVA analysis results in Table 3.
+
+# E Additional Data on Running WEAKER
+
+It takes 30 seconds on average to generate a default KB from scratch (with around 500 concepts) on our machine (Precision 3650 workstation, with Intel(R) Xeon(R) W-1350 CPU and 32GB memory). When users explore the KB interactively and expand a node, the query takes 8 seconds on average. The wait can be greatly reduced via pre-fetching (i.e., expanding the node on display in the background before the actual query), which has been implemented in WEAKER.
+
+# F WEAKER with Open-source LLMs
+
+We conducted an extra experiment to evaluate whether open-source LLMs can also generate comprehensive KBs. We generated another KB on the climate change task, using 11ama-2-13b-chat with 4-bit quantization. We found that the generated KB achieved comparable levels of recall (83% vs. 91% with text-davinci-003).
+
+![](images/4f7be0b9410f2ce9561c0d1235510e4e52a54b1048f04cdf295a850114099409.jpg)  
+Figure 9: Complete user interface for WEaver. On the right is our re-implemented version of AdaTest to assist users to create test cases.
+
+![](images/569357e5fd6454a4d9f69d4fce703b3258d07a50fda682221aace27941f0852f.jpg)
+
+![](images/52003c3c5c048c588f162c49b2aa2d56604a0256f72df7db82d5ce36717a05c5.jpg)  
+Table 2: Participants' responses in the post-study survey.
+
+<table><tr><td>Number of found concepts</td><td>ges</td></tr><tr><td>Interv.: Used WEAKER?</td><td>0.307**</td></tr><tr><td>Order: Tool in first task?</td><td>0.008</td></tr><tr><td>Task number</td><td>0.000</td></tr><tr><td>Number of hit clusters</td><td>ges</td></tr><tr><td>Interv.: Used WEAKER?</td><td>0.408**</td></tr><tr><td>Order: Tool in first task?</td><td>0.039</td></tr><tr><td>Task number</td><td>0.006</td></tr><tr><td>**p&lt;0.01</td><td>N=40</td></tr></table>
+
+Table 3: User study ANOVA analysis results. We measure effect size with ges (generalized Eta-Squared measure of effect size).

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+        "text": "Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization",
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+        "text": "Shoujie Tong $^{1,2*}$  Heming Xia $^{1,2*}$  Damai Dai $^{1,4}$  Runxin Xu $^{1,4}$  Tianyu Liu $^{3}$   \nBinghuai Lin $^{5}$  Yunbo Cao $^{5}$  Zhifang Sui $^{1,4}$",
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+        "text": "$^{1}$  National Key Laboratory for Multimedia Information Processing, Peking University  $^{2}$  School of Software & Microelectronics, Peking University  $^{3}$  Alibaba Group  $^{4}$  School of Computer Science, Peking University  $^{5}$  Tencent Cloud AI  $\\{\\text{tong}\\}@\\text{stu.pku.edu.cn}$ $\\{\\text{xiaheming,daidamai,tianyu0421,szf}\\}@\\text{pku.edu.cn}$ $\\{\\text{runxinxu}\\}@\\text{gmail.com}$ $\\{\\text{binghuailin,yunbocao}\\}@\\text{tencent.com}$",
+        "bbox": [
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+        "type": "text",
+        "text": "Abstract",
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+        "text": "Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and thus diminish performance. This paper presents Bi-Drop, a fine-tuning strategy that selectively updates model parameters using gradients from various sub-nets dynamically generated by dropout. The sub-net estimation of Bi-Drop is performed in an in-batch manner, so it overcomes the problem of hysteresis in sub-net updating, which is possessed by previous methods that perform asynchronous sub-net estimation. Also, Bi-Drop needs only one mini-batch to estimate the sub-net so it achieves higher utility of training data. Experiments on the GLUE benchmark demonstrate that Bi-Drop consistently outperforms previous fine-tuning methods. Furthermore, empirical results also show that Bi-Drop exhibits excellent generalization ability and robustness for domain transfer, data imbalance, and low-resource scenarios.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "In recent years, Natural Language Processing (NLP) has achieved significant progress due to the emergence of large-scale Pretrained Language Models (PLMs) (Devlin et al., 2018; Liu et al., 2019; Raffel et al., 2019; Clark et al., 2020). For downstream tasks, compared with training from scratch, fine-tuning pretrained models can usually achieve efficient adaptation and result in better performance. Despite the great success, fine-tuning methods still face challenges in maintaining generalization performance on downstream tasks - they tend to run into the overfitting issue when the training data is limited (Phang et al., 2018; Devlin et al., 2018; Lee et al., 2020).",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To improve the generalization ability of finetuning methods, many regularization techniques have been proposed (Chen et al., 2020; Aghajanyan",
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+        "img_path": "images/381214e8a1bf9893e687d2b6c2dd321f014152a40b11e2c555128f5de304cef6.jpg",
+        "image_caption": [
+            "Figure 1: Comparisons between Bi-Drop and previous methods. Unlike previous methods that require multiple mini-batches of data to asynchronously determine the sub-net to optimize, Bi-Drop has a synchronous sub-net selection strategy with a higher data utility."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "et al., 2021; Wu et al., 2021; Xu et al., 2021; Yuan et al., 2022), such as sub-net optimization strategies like Child-Tuning  $D$  (Xu et al., 2021) and DPS (Zhang et al., 2022). Child-Tuning  $D$  selects a static sub-net for updating based on parameter importance estimated by Fisher Information (FI). As an improved variant of Child-Tuning  $D$ , DPS dynamically decides the sub-net to be updated by estimating FI with multiple mini-batches of data. Although these FI-based methods achieve better generalization ability than vanilla fine-tuning, they still have two limitations: (1) hysteresis in sub-net updating: the sub-net preference is estimated with the model parameters in previous iterations and may be incompatible with the current update step; and (2) insufficient utility of training data: FI estimation requires cumulative gradients through multiple mini-batches, so these methods cannot fit in situations with data scarcity.",
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+    {
+        "type": "text",
+        "text": "In this paper, we delve deeper into adaptive subnet optimization strategies and propose Bi-Drop, a FI-free strategy for fine-tuning pretrained language models. Unlike Fisher information estimation, which requires cumulative gradients of minibatches, Bi-Drop only relies on information in a single mini-batch to select the parameters to update. Specifically, Bi-Drop utilizes gradient information",
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+    {
+        "type": "page_footnote",
+        "text": "*Equal contributions",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "page_number",
+        "text": "5214",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5214-5227 December 6-10, 2023 ©2023 Association for Computational Linguistics",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "from different sub-nets dynamically generated by dropout in each mini-batch. As illustrated in Figure 1, within a single training step of Bi-Drop, a minibatch will go through the forward pass multiple times and, due to the randomness introduced by dropout, yield various distinct sub-nets. We then apply a parameter selection algorithm with perturbation and scaling factors to stabilize the gradient updates. With this synchronous parameter selection strategy, Bi-Drop can selectively update model parameters according to the information from only the current mini-batch, and thus mitigate overfitting with a high utility of training data.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Extensive experiments on the GLUE benchmark demonstrate that Bi-Drop shows remarkable superiority over the state-of-the-art fine-tuning regularization methods, with a considerable margin of 0.53  $\\sim$  1.50 average score. Moreover, Bi-Drop consistently outperforms vanilla fine-tuning by 0.83  $\\sim$  1.58 average score across various PLMs. Further analysis indicates that Bi-Drop attains superb generalization ability for domain transfer and task transfer, and is robust for data imbalance and low-resource scenarios.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To sum up, our contributions are three-fold:",
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+        "list_items": [
+            "- We propose Bi-Drop, a step-wise sub-net optimization strategy that adaptively selects the updated sub-net based on the current minibatch data. Compared with prior FI-based methods, Bi-Drop derives a more stable and robust training trajectory with simultaneous sub-net update and high utility of training data.",
+            "- With extensive experiments on various PLMs, we demonstrate that Bi-Drop achieves consistent and remarkable superiority over prior fine-tuning regularization methods.",
+            "Further analysis shows that Bi-drop attains superb generalization ability for domain transfer and task transfer, and is robust for data imbalance and low-resource scenarios."
+        ],
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Pretrained Language Models In recent years, the field of natural language processing (NLP) has witnessed significant advancements due to the development of large-scale pretrained language models (PLMs). The introduction of BERT (Devlin",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "table",
+        "img_path": "images/1ff341212c3fc36ede17e113f62a3b84052bf1b10dc75d25054f89604d51428a.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Models</td><td>Sub-net Granularity</td><td>Hyste- resis</td><td>Data Depend.</td></tr><tr><td>Child-TuningD</td><td>task-wise</td><td>Yes</td><td>Yes</td></tr><tr><td>DPS</td><td>cycle-wise</td><td>Yes</td><td>Yes</td></tr><tr><td>Bi-Drop</td><td>step-wise</td><td>No</td><td>No</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Table 1: Comparison of Bi-Drop with prior FI-based sub-net optimization methods. \"cycle-wise\" refers to the granularity that includes multiple mini-batches of data.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "et al., 2018) sparked a continuous emergence of various pre-trained models, including RoBERTa (Liu et al., 2019), ELECTRA (Clark et al., 2020), XLNet (Yang et al., 2019), GPT-2 (Radford et al., 2019), and GPT-3 (Brown et al., 2020), which have brought remarkable improvements in model structures and scales. Until now, fine-tuning is still one of the most popular approaches to adapting large pretrained language models to downstream tasks.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Regularization Methods for Fine-tuning Large-scale PLMs are prone to over-fitting (Phang et al., 2018; Devlin et al., 2018) and exhibit inadequate generalization ability when fine-tuned with limited training data (Aghajanyan et al., 2021; Mahabadi et al., 2021), resulting in degraded performance. To tackle this issue, various regularization techniques have been suggested to enhance the generalization capacity of models, including advanced dropout alternatives (Wan et al., 2013; Wu et al., 2021), applying adversarial perturbations (Aghajanyan et al., 2021; Wu et al., 2022; Yuan et al., 2022) and constrained regularization methods (DauméIII, 2007; Chen et al., 2020). In recent years, Child-tuning (Xu et al., 2021) and DPS (Zhang et al., 2022) propose to estimate parameter importance based on Fisher Information (FI) and selectively optimize a sub-net during fine-tuning to mitigate overfitting.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "FI-based methods have a strong dependence on the training data and exhibit hysteresis in sub-net updating. As shown in Table 1, compared with prior FI-based methods, Bi-Drop introduces a stepwise sub-net optimization strategy that adaptively selects the sub-net to be updated based on the current mini-batch. It is worth noting that, as a model-agnostic technique, Bi-Drop is orthogonal to most previous fine-tuning methods, which could further boost the model's performance.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "page_footnote",
+        "text": "$^{1}$ Our code is available at https://github.com/tongshoujie/Bi-Drop",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "page_number",
+        "text": "5215",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "image",
+        "img_path": "images/874689c9145fb4cf78d240b43a79eb64d4c5dac25a186a3715433a8155b69780.jpg",
+        "image_caption": [
+            "Figure 2: An overall illustration of Bi-Drop. Bi-Drop splits each training step into three sub-steps: (1) Multiple forward propagations: each mini-batch sample goes through the forward pass multiple times (denoted as  $k$ ) with dropout; (2) sub-net selection: an advanced strategy is adopted to select the sub-net to be updated based on the gradients of distinct sub-nets generated by dropout; (3) Parameter updating: only the parameters of the selected sub-net are updated to mitigate overfitting."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3 Methodology",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3.1 Background",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "We first introduce the paradigm of sub-net optimization by giving general formulations of the backpropagation during the vanilla fine-tuning and CHILD-TUNING  $D$ . We denote the parameters of the model at  $t$ -th iteration as  $\\theta_{t} = \\{\\theta_{t,i}\\}_{i=1}^{n}$ , where  $\\theta_{t,i}$  represent the  $i$ -th element of  $\\theta_{t}$  at the  $t$ -th training iteration.  $\\theta_{0}$  denotes the parameter matrix of the pre-trained model. The vanilla fine-tuning adopts Stochastic Gradient Descent (SGD) to all the model parameters, formally:",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {\\theta} _ {t + 1} = \\boldsymbol {\\theta} _ {t} - \\eta \\frac {\\partial \\mathcal {L} \\left(\\boldsymbol {\\theta} _ {t}\\right)}{\\partial \\boldsymbol {\\theta} _ {t}} \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathcal{L}$  represents the training loss within a batch;  $\\eta$  is the learning rate. Instead of fine-tuning the entire network, CHILD-TUNING $_D$  proposes to only optimize a subset of parameters (i.e., the sub-net). It first adopts the Fisher Information (FI) to estimate the relative importance of the parameters for a specific downstream task, which can be formulated as:",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nF \\left(\\boldsymbol {\\theta} _ {0}\\right) = \\sum_ {j = 1} ^ {| \\mathcal {D} |} \\left(\\frac {\\partial \\mathcal {L} \\left(\\boldsymbol {\\theta} _ {0}\\right)}{\\partial \\boldsymbol {\\theta} _ {0}}\\right) ^ {2} \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            213,
+            741,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {M} _ {C T _ {D}} = F (\\boldsymbol {\\theta} _ {0}) > \\operatorname {s o r t} (F (\\boldsymbol {\\theta} _ {0})) _ {p} \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            173,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mathcal{D}$  is the training data,  $F(\\theta_0)$  denotes the fisher information matrix of the pretrained parameters;  $\\mathrm{sort}(\\cdot)_p$  represents the highest value of  $p$  percentile in  $F(\\theta_0)$  after sorting in ascending order;  $M_{CT_D}$  is a mask matrix that is the same-sized as  $\\theta_0$ . During fine-tuning, CHILD-TUNING $_D$  only optimizes the selected sub-net in  $M_{CT_D}$ :",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {\\theta} _ {t + 1} = \\boldsymbol {\\theta} _ {t} - \\eta \\frac {\\partial \\mathcal {L} (\\boldsymbol {\\theta} _ {t})}{\\partial \\boldsymbol {\\theta} _ {t}} \\boldsymbol {M} _ {C T _ {D}} \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3.2 Bi-Drop",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "As introduced in Section 3.1, CHILD-TUNING $_D$  only optimizes an unchanged sub-net during fine-tuning and ignores the update of other parameters, which may degrade the model's performance on downstream tasks. In this section, we offer a detailed introduction to our proposed method, BiDrop, which selects the updated parameters adaptively at each fine-tuning step. Specifically, BiDrop splits each training step into three sub-steps: (1) multiple forward propagations, (2) sub-net selection, and (3) parameter updating. We provide a pseudo-code of Bi-Drop in Algorithm 1.",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3.2.1 Multiple Forward Propagations",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Instead of prior FI-based methods that require accumulated gradients to measure the parameter importance, Bi-Drop leverages distinct sub-nets generated by dropout to select the sub-net to be updated. Inspired by Wu et al. (2021), given the training data  $\\mathcal{D} = \\{(x_i,y_i)\\}_{i=1}^m$ , at each training step, we feed  $x_i$  to the model multiple times in the forward pass with different dropouts, and obtain their gradients correspondingly:",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {g} _ {t} ^ {(j)} = \\frac {\\partial \\mathcal {L} \\left(\\boldsymbol {\\theta} _ {t} ^ {(j)}\\right)}{\\partial \\boldsymbol {\\theta} _ {t} ^ {(j)}}, \\quad j = 1, 2, \\dots , k \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+            809,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "where  $\\pmb{\\theta}_t^{(j)}$  and  $\\mathbf{g}_t^{(j)}$  represents the parameters of the  $j$ -th forward pass and its corresponding gradients.  $k$  denotes the number of forward passes, i.e., the number of distinct sub-nets with different dropouts.",
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+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "5216",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Algorithm 1 Bi-Drop for Adam Optimizer",
+        "text_level": 1,
+        "bbox": [
+            115,
+            83,
+            436,
+            99
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Require:  $\\theta_0$ : initial pretrained weights;  $\\mathcal{L}(\\pmb{\\theta})$ : stochastic objective function with parameters  $\\pmb{\\theta}$ ;  $\\beta_{1},\\beta_{2}\\in [0,1)$ : exponential decay rates for the moment estimates;  $\\eta$ : learning rate;",
+        "bbox": [
+            115,
+            103,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1: initialize timestep  $t \\gets 0$ , first moment vector  $\\mathbf{m}_0 \\gets \\mathbf{0}$ , second moment vector  $\\mathbf{v}_0 \\gets \\mathbf{0}$",
+            "2: while not converged do",
+            "3:  $t\\gets t + 1$  //Multipleforwardpropagations",
+            "4:  $\\mathbf{g}_t^{(j)}\\gets \\frac{\\partial\\mathcal{L}(\\pmb{\\theta}_t^{(j)})}{\\partial\\pmb{\\theta}_t^{(j)}},\\quad j = 1,2,\\dots,k$  // Sub-net selection",
+            "5:  $M_{t} \\gets \\text{SelectSubNetwork}(\\mathbf{g}_{t})$  // Gradients Updating",
+            "6:  $\\mathbf{g}_t\\gets \\mathbf{g}_t\\odot M_t$",
+            "7:  $\\mathbf{m}_t\\gets \\beta_1\\cdot \\mathbf{m}_{t - 1} + (1 - \\beta_1)\\cdot \\mathbf{g}_t$",
+            "8:  $\\mathbf{v}_t\\gets \\beta_2\\cdot \\mathbf{v}_{t - 1} + (1 - \\beta_2)\\cdot \\mathbf{g}_t^2$",
+            "9:  $\\hat{\\mathbf{m}}_t\\gets \\mathbf{m}_t / (1 - \\beta_1^t)$",
+            "10:  $\\hat{\\mathbf{v}}_t\\gets \\mathbf{v}_t / (1 - \\beta_2^t)$  // Update weights",
+            "11:  $\\mathbf{w}_t\\gets \\mathbf{w}_{t - 1} - \\eta \\cdot \\hat{\\pmb{m}}_t / (\\sqrt{\\hat{\\pmb{v}}_t} +\\epsilon)$",
+            "12: end while",
+            "13: return  $\\mathbf{w}_t$"
+        ],
+        "bbox": [
+            119,
+            168,
+            485,
+            466
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3.2.2 Sub-net Selection",
+        "text_level": 1,
+        "bbox": [
+            114,
+            495,
+            310,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "In this subsection, we introduce our sub-net selection strategy, which estimates the relevant importance of parameters based on the gradients of distinct sub-nets generated by different dropouts. Concretely, our strategy is based on two estimation factors: the perturbation factor and the scaling factor.",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Perturbation Factor We propose the perturbation factor, which estimates the importance of parameters according to their stability with different dropouts in the forward pass. We point out that various sub-nets generated by dropout can be viewed as adversarial perturbations to the vanilla model. The perturbation factor is formalized as follows:",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {\\mu} _ {t} = \\frac {1}{k} \\sum_ {j = 1} ^ {k} \\mathbf {g} _ {t} ^ {(j)} \\tag {6}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {F} _ {\\text {p e r}} \\left(\\boldsymbol {\\theta} _ {t}\\right) = \\left| \\boldsymbol {\\mu} _ {t} \\right| \\cdot \\left[ \\sum_ {j} \\left(\\mathbf {g} _ {t} ^ {(j)} - \\boldsymbol {\\mu} _ {t}\\right) ^ {2} \\right] ^ {- \\frac {1}{2}} \\tag {7}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            166,
+            808,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $\\mu_t$  is the average gradients of parameters.  $\\mathbf{F}_{\\mathrm{per}}$  measures the stability of parameters by both considering the mean and variance of gradients with adversarial perturbations, i.e. sub-nets with",
+        "bbox": [
+            112,
+            854,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "consistently larger gradients and smaller variances are more favorable by this factor.",
+        "bbox": [
+            507,
+            84,
+            880,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Scaling Factor We further propose the scaling factor as a regularization term. This factor measures the ratio of the average parameter gradients to the original parameters. Parameters whose gradient scale is much smaller than the original parameters will not be updated, which is similar in spirit to gradient clipping.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {F} _ {\\mathrm {s c a}} \\left(\\boldsymbol {\\theta} _ {t}\\right) = \\left| \\boldsymbol {\\mu} _ {t} \\right| \\cdot \\left| \\boldsymbol {\\theta} _ {t} \\right| ^ {- 1} \\tag {8}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            605,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3.2.3 Parameter Updating",
+        "text_level": 1,
+        "bbox": [
+            507,
+            297,
+            732,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Following prior work (Xu et al., 2021; Zhang et al., 2022), we derive a step-wise mask matrix  $M_{t}$  filtered by selecting the highest value of  $p$  percentile measured by the aforementioned two estimation factors.",
+        "bbox": [
+            507,
+            319,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {F} _ {\\text {f i n a l}} \\left(\\boldsymbol {\\theta} _ {t}\\right) = \\mathbf {F} _ {\\text {p e r}} \\left(\\boldsymbol {\\theta} _ {t}\\right) \\cdot \\mathbf {F} _ {\\text {s c a}} \\left(\\boldsymbol {\\theta} _ {t}\\right) \\tag {9}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            589,
+            419,
+            882,
+            434
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {M} _ {t} = \\mathbf {F} _ {\\text {f i n a l}} (\\boldsymbol {\\theta} _ {t}) > \\operatorname {s o r t} \\left(\\mathbf {F} _ {\\text {f i n a l}} (\\boldsymbol {\\theta} _ {t}))\\right) _ {p} \\tag {10}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            571,
+            449,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Then, we utilize  $M_t$  to update the sub-net which consists of important parameters at each training step. We denote the formulation by simply replacing Eq.4 with our step-wise mask matrix  $M_t$ :",
+        "bbox": [
+            507,
+            479,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\boldsymbol {\\theta} _ {t + 1} = \\boldsymbol {\\theta} _ {t} - \\eta \\boldsymbol {\\mu} _ {t} \\cdot \\boldsymbol {M} _ {t} \\tag {11}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "4 Experiments",
+        "text_level": 1,
+        "bbox": [
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+            608,
+            653,
+            624
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "4.1 Datasets",
+        "text_level": 1,
+        "bbox": [
+            507,
+            637,
+            621,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "GLUE Benchmark Following previous work (Lee et al., 2020; Dodge et al., 2020; Zhang et al., 2021), we conduct extensive experiments on the GLUE benchmark, including natural language inference (RTE, QNLI, MNLI), paraphrase and similarity (MRPC, STS-B, QQP), linguistic acceptability (CoLA), and sentiment classification (SST-2). We include the detailed statistics and metrics of the datasets in Appendix A. Since the test results are only accessible online with limited submission times, we follow prior studies (Phang et al., 2018; Lee et al., 2020; Aghajanyan et al., 2021; Dodge et al., 2020; Xu et al., 2021; Zhang et al., 2022) that fine-tune the pretrained model on the training set and report the results on the development sets using the last checkpoint.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "5217",
+        "bbox": [
+            480,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "table",
+        "img_path": "images/b34c5bd45f7826f384c861923edc9849f777fe954f59f590f25d6105f0254a6d.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>Δ</td></tr><tr><td>Vanilla</td><td>63.76(65.55)</td><td>90.41(91.91)</td><td>70.97(74.01)</td><td>89.70(90.58)</td><td>78.71(80.51)</td><td>0.00</td></tr><tr><td>Weight Decay</td><td>63.70(65.53)</td><td>90.89(91.98)</td><td>72.24(74.37)</td><td>89.66(90.22)</td><td>79.12(80.53)</td><td>+0.41(0.02)</td></tr><tr><td>Top-K Tuning</td><td>63.89(65.37)</td><td>91.09(92.05)</td><td>72.84(75.17)</td><td>89.64(90.83)</td><td>79.37(80.86)</td><td>+0.66(0.35)</td></tr><tr><td>Mixout</td><td>64.05(65.41)</td><td>91.19(92.05)</td><td>72.32(75.52)</td><td>89.89(90.42)</td><td>79.36(80.85)</td><td>+0.65(0.34)</td></tr><tr><td>RecAdam</td><td>64.09(65.35)</td><td>90.88(91.89)</td><td>72.48(74.30)</td><td>89.67(90.62)</td><td>79.28(80.54)</td><td>+0.57(0.03)</td></tr><tr><td>ChildTuningD</td><td>64.25(65.81)</td><td>91.19(92.20)</td><td>72.35(76.53)</td><td>90.18(90.88)</td><td>79.49(81.36)</td><td>+0.78(0.85)</td></tr><tr><td>R3F</td><td>64.88(66.33)</td><td>91.55(92.28)</td><td>72.42(74.37)</td><td>89.70(90.58)*</td><td>79.64(80.89)</td><td>+0.93(0.38)</td></tr><tr><td>DPSmix</td><td>64.40(66.21)</td><td>91.05(92.17)</td><td>72.88(75.81)</td><td>90.47(91.02)</td><td>79.70(81.30)</td><td>+0.99(0.79)</td></tr><tr><td>R-Drop</td><td>64.83(66.79)</td><td>91.76(92.17)</td><td>73.12(76.53)</td><td>90.14(90.42)</td><td>79.96(81.48)</td><td>+1.25(0.97)</td></tr><tr><td>Bi-Drop</td><td>64.94(66.69)</td><td>91.79(92.68)</td><td>73.79(77.61)</td><td>90.59(91.04)</td><td>80.26(82.01)</td><td>+1.55(1.50)</td></tr></table>",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Table 2: Comparison between Bi-Drop with prior fine-tuning methods. We report the mean (max) results of 10 random seeds. The best results are bold. Note that since R3F is not applicable to regression, the result on STS-B (marked with *) remains the same as vanilla. Bi-Drop achieves the best performance compared with other methods.",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "NLI Datasets We also evaluate the generalization ability of Bi-Drop on several Natural Language Inference (NLI) tasks, including SNLI (Bowman et al., 2015), MNLI (Williams et al., 2018), MNLI-M (Williams et al., 2018) and SICK (Marelli et al., 2014). We report all results by Accuracy on the development sets consistent with GLUE.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.2 Baselines",
+        "text_level": 1,
+        "bbox": [
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+            461,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Besides the vanilla fine-tuning method, we mainly compare Bi-Drop with the following baselines:",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Mixout (Lee et al., 2020) is a fine-tuning technique that stochastically replaces the parameters with their pretrained weight based on the Bernoulli distribution. R3F (Aghajanyan et al., 2021) is a fine-tuning strategy motivated by trust-region theory, which injects noise sampled from either a normal or uniform distribution into the pre-trained representations. R-Drop (Wu et al., 2021) minimizes the bidirectional KL-divergence to force the output distributions of two sub-nets sampled by dropout to be consistent with each other. Child-Tuning $_D$  (Xu et al., 2021) selects the task-relevant parameters as the sub-net based on the Fisher information and only updates the sub-net during fine-tuning. DPS (Zhang et al., 2022) is a dynamic sub-net optimization algorithm based on Child-Tuning $_D$ . It estimates Fisher information with multiple minibatches of data and selects the sub-net adaptively during fine-tuning.",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "For reference, we also show other prior finetuning techniques in our main experimental results, such as Weight Decay (DauméIII, 2007), Top-K Tuning (Houlsby et al., 2019) and RecAdam (Chen et al., 2020).",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.3 Experiments Setup",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "We conduct our experiments based on the Hugging-Face transformers library  ${}^{2}$  (Wolf et al., 2020) and follow the default hyper-parameters and settings unless noted otherwise. We report the averaged results over 10 random seeds. Other detailed experimental setups are presented in Appendix B.",
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+    {
+        "type": "text",
+        "text": "4.4 Results on GLUE",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Comparison with Prior Methods We compare Bi-Drop with various prior fine-tuning methods based on  $\\mathrm{BERT}_{\\mathrm{large}}$  and report the mean (and max) scores on GLUE benchmark in Table 2, following Lee et al. (2020) and Xu et al. (2021). The results indicate that Bi-Drop yields the best average performance across all tasks, showing its effectiveness. Moreover, the average of the maximum scores attained by Bi-Drop is superior to that of other methods, providing further evidence of the effectiveness of Bi-Drop. We also conducted the same experiment on RobertaLarge, and the details can be found in Appendix E.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Comparison with Vanilla Fine-tuning We show the experimental results of six widely used large-scale PLMs on the GLUE Benchmark in Table 3. The results show that Bi-Drop outperforms vanilla fine-tuning consistently and significantly across all tasks performed on various PLMs. For instance, Bi-Drop achieves an improvement of up to 1.58 average score on  $\\mathrm{BERT}_{\\mathrm{base}}$  and 1.35 average score on Robertabase. The results highlight the universal effectiveness of Bi-Drop in enhancing the fine-tuning performance of PLMs. Additionally, because BiDrop forward-propagate twice, we present an additional study of the baseline with doubled batch",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "2https://github.com/huggingface/transformers",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "5218",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/f614f9a977bb1886a9601dc22d8de2ea389288c509cea739e63c65784cddecee.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Method</td><td colspan=\"5\">BERTbase</td><td colspan=\"5\">Robertabase</td></tr><tr><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td></tr><tr><td>Vanilla</td><td>57.67</td><td>90.38</td><td>69.57</td><td>89.35</td><td>76.74</td><td>59.45</td><td>91.94</td><td>76.28</td><td>90.60</td><td>79.57</td></tr><tr><td>Bi-Drop</td><td>60.76</td><td>91.01</td><td>71.73</td><td>89.78</td><td>78.32</td><td>61.28</td><td>92.40</td><td>78.99</td><td>91.00</td><td>80.92</td></tr><tr><td rowspan=\"2\">Method</td><td colspan=\"5\">BERTlarge</td><td colspan=\"5\">Robertalarge</td></tr><tr><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td></tr><tr><td>Vanilla</td><td>63.76</td><td>90.41</td><td>70.97</td><td>89.70</td><td>78.71</td><td>66.01</td><td>92.56</td><td>84.51</td><td>92.05</td><td>83.78</td></tr><tr><td>Bi-Drop</td><td>64.94</td><td>91.79</td><td>73.79</td><td>90.50</td><td>80.26</td><td>68.03</td><td>92.95</td><td>86.10</td><td>92.36</td><td>84.86</td></tr><tr><td rowspan=\"2\">Method</td><td colspan=\"5\">DeBERTlarge</td><td colspan=\"5\">ELECTRALarge</td></tr><tr><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td></tr><tr><td>Vanilla</td><td>65.18</td><td>92.32</td><td>85.60</td><td>91.64</td><td>83.69</td><td>70.02</td><td>92.94</td><td>88.37</td><td>91.91</td><td>85.81</td></tr><tr><td>Bi-Drop</td><td>66.91</td><td>92.88</td><td>86.72</td><td>92.33</td><td>84.71</td><td>71.29</td><td>93.68</td><td>88.98</td><td>92.61</td><td>86.64</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "table",
+        "img_path": "images/7ce4c84a7fe7557b2a69be0984a06c65e630ab4c4d32a009551e786d69575da2.jpg",
+        "table_caption": [
+            "Table 3: Comparison between Bi-Drop and vanilla fine-tuning applied to six widely-used large-scale PLMs. We report the mean results of 10 random seeds. Average scores on all tasks are underlined. The best results are bold. It shows that Bi-Drop yields consistent improvements across all tasks among different PLMs."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Datasets</td><td colspan=\"5\">SNLI</td><td colspan=\"5\">MNLI</td></tr><tr><td>Vanilla</td><td>CTD</td><td>R-Drop</td><td>DPS</td><td>Bi-Drop</td><td>Vanilla</td><td>CTD</td><td>R-Drop</td><td>DPS</td><td>Bi-Drop</td></tr><tr><td>MNLI</td><td>64.53</td><td>64.16</td><td>64.51</td><td>64.70</td><td>66.88</td><td>75.78</td><td>76.27</td><td>76.85</td><td>75.48</td><td>76.63</td></tr><tr><td>MNLI-m</td><td>66.11</td><td>66.30</td><td>66.59</td><td>67.29</td><td>68.49</td><td>77.31</td><td>77.30</td><td>77.93</td><td>77.45</td><td>77.98</td></tr><tr><td>SNLI</td><td>83.37</td><td>83.53</td><td>83.59</td><td>82.95</td><td>83.62</td><td>70.87</td><td>70.76</td><td>71.48</td><td>71.56</td><td>71.63</td></tr><tr><td>SICK</td><td>52.59</td><td>53.73</td><td>53.59</td><td>55.89</td><td>54.28</td><td>53.27</td><td>55.35</td><td>54.21</td><td>54.81</td><td>54.93</td></tr><tr><td>Avg</td><td>61.08</td><td>61.40</td><td>61.56</td><td>62.63</td><td>63.22</td><td>67.15</td><td>67.80</td><td>67.87</td><td>67.94</td><td>68.18</td></tr><tr><td>Δavg</td><td>-</td><td>↑0.32</td><td>↑0.48</td><td>↑1.55</td><td>↑2.14</td><td>-</td><td>↑0.65</td><td>↑0.72</td><td>↑0.79</td><td>↑1.03</td></tr></table>",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Table 4: Evaluation for out-of-domain generalization. The models are trained on MNLI/SNLI and tested on out-of-domain data. Average scores are computed excluding in-domain results (underlined). The best results are bold. Bi-Drop can better maintain the out-of-domain generalization ability of the model.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "size in Appendix D.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "4.5 Out-of-Domain Generalization",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "We further evaluate the generalization ability of Bi-Drop on a widely used experimental setting in prior research (Aghajanyan et al., 2021; Xu et al., 2021; Zhang et al., 2022): out-of-domain generalization. In detail, we finetune  $\\mathrm{BERT}_{\\text {large }}$  with different strategies on  $5k$  subsampled MNLI and SNLI datasets respectively, and directly evaluate the fine-tuned model on other NLI datasets. The experimental results in Table 4 illustrate that Bi-Drop outperforms vanilla fine-tuning and prior methods across various datasets. Specifically, compared with other fine-tuning methods on SNLI, Bi-Drop demonstrates consistent and substantial improvement, with an average score increase of 2.14. In particular, Bi-Drop achieves an improvement of 2.35 on MNLI task and 2.38 on MNLI-m task. For models trained on MNLI, Bi-Drop also consistently outperforms prior methods, with an average improvement of 1.03 score. The experimental results",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "indicate that Bi-Drop encourages the model to learn deeper and more generic semantic features and alleviate superficial biases of specific tasks, which improves the model's generalization ability.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "4.6 Task Generalization",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "We also evaluate the generalization ability of finetuned models following the experimental setting of Aghajanyan et al. (2021) and Xu et al. (2021), which freezes the representations of the model finetuned on one task and only trains the linear classifier on the other task. Specifically, we finetune  $\\mathrm{BERT}_{\\mathrm{large}}$  among one task selected among MRPC, CoLA, and RTE and then transfer the model to the other two tasks. Figure 3 shows that Bi-Drop consistently outperforms vanilla fine-tuning when the fine-tuned model is transferred to other tasks. In particular, Bi-Drop improves by 3.50 and 3.28, when models trained on MRPC and RTE respectively are evaluated on CoLA. The results further verify the conclusion that Bi-Drop helps models learn more generalizable representations, com",
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+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "5219",
+        "bbox": [
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+        "page_idx": 5
+    },
+    {
+        "type": "image",
+        "img_path": "images/dd33905fdd777bcacbdab7c60d16e4a908b144638f0b57131777cffc35dbd799.jpg",
+        "image_caption": [
+            "Figure 3: Evaluation for task generalization. The model is fine-tuned on a specific task among MRPC, CoLA, RTE and transferred to the other two tasks. Bi-Drop can be more generalizable."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/67de11bf44d994a0e46c84841d3b3e43c21ced99fb95e20e9e119647ef9fc504.jpg",
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/58307480d36b35eb933e0a80ecbfa039246e32f1311c3bac9a9f0198c9c876a6.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/7d4e647ee224e3cc8affbbd6d424fa43b711baca39a5eeaed099c3258d9cea29.jpg",
+        "image_caption": [
+            "Figure 4: Results of Bi-Drop across four experimental settings. Each method includes a violin plot for 10 random runs. Compared with other methods, the shorter and thicker violin plot of Bi-Drop proves its better stability."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "pared with the vanilla fine-tuning approach.",
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+    },
+    {
+        "type": "text",
+        "text": "5 Analysis",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "5.1 Stability to Random Seeds",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "We further investigate the stability properties of fine-tuned models. Figure 4 shows the output distributions of models with four experimental settings and across 10 random seeds. The results demonstrate that Bi-Drop outperforms other strategies in terms of average performance, and also exhibits greater stability by achieving more consistent results across 10 random seeds with lower variance.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "5.2 Robustness Analysis",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Recent research has brought to light that the vanilla fine-tuning approach is prone to deception and vulnerability in many aspects. In this study, we assess the robustness of Bi-Drop by designing evaluation tasks that focus on two common scenarios, aiming to examine its ability to withstand various forms of perturbations while maintaining its robustness.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Robustness to Label Noise Due to the inherent limitations of human annotation, widely-used large-scale datasets inevitably contain a certain amount of incorrect labels (Vasudevan et al., 2022). To",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "investigate the robustness of Bi-Drop to label noise, we conduct simple simulation experiments on RTE, MRPC, and CoLA by randomly corrupting a predetermined fraction of labels with erroneous values. We evaluate the robustness of various fine-tuning methods trained on noisy data. The results shown in the left panel of Table 5 demonstrate that Bi-Drop achieves consistent superiority to other fine-tuning methods on noisy data. Furthermore, we conducted a comparison of model performance degradation across varying noise ratios. We calculated the degradation and presented it in brackets, as compared with Table 2. It indicates that Bi-Drop has the smallest performance drop compared with other fine-tuning methods. These results collectively demonstrate that Bi-Drop is more robust to label noise than prior methods.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Robustness to Data Imbalance Minority class refers to the class that owns insufficient instances in the training set. In this section, we strive to explore the robustness of diverse fine-tuning approaches for the minority class by carrying out experiments on synthetic RTE, MRPC, and CoLA datasets. The experimental results are illustrated in the right panel of Table 5, which shows that Bi-Drop significantly outperforms other fine-tuning methods. Bi-Drop",
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+    },
+    {
+        "type": "page_number",
+        "text": "5220",
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+    },
+    {
+        "type": "table",
+        "img_path": "images/986a2d74989499b997d60091fb54cbe3b90fd508b144a9e952e71384b06ad2d8.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>Avg</td></tr><tr><td colspan=\"5\">Noise Ratio: 5%</td></tr><tr><td>vanilla</td><td>61.39</td><td>90.24</td><td>69.78</td><td>73.80 (↓ 1.25)</td></tr><tr><td>CTD</td><td>61.66</td><td>90.39</td><td>71.47</td><td>74.51 (↓ 1.77)</td></tr><tr><td>R-Drop</td><td>62.33</td><td>91.21</td><td>73.16</td><td>75.57 (↓ 1.00)</td></tr><tr><td>Bi-Drop</td><td>62.29</td><td>91.85</td><td>73.57</td><td>75.90 (↓ 0.94)</td></tr><tr><td colspan=\"5\">Noise Ratio: 10%</td></tr><tr><td>vanilla</td><td>59.35</td><td>89.78</td><td>68.51</td><td>72.55 (↓ 2.50)</td></tr><tr><td>CTD</td><td>61.56</td><td>89.88</td><td>69.78</td><td>73.72 (↓ 2.56)</td></tr><tr><td>R-Drop</td><td>61.12</td><td>91.10</td><td>71.36</td><td>74.53 (↓ 2.04)</td></tr><tr><td>Bi-Drop</td><td>61.65</td><td>91.12</td><td>71.96</td><td>74.91 (↓ 1.93)</td></tr><tr><td colspan=\"5\">Noise Ratio: 15%</td></tr><tr><td>vanilla</td><td>58.96</td><td>88.10</td><td>68.14</td><td>71.73 (↓ 3.32)</td></tr><tr><td>CTD</td><td>59.99</td><td>88.10</td><td>68.88</td><td>72.32 (↓ 3.96)</td></tr><tr><td>R-Drop</td><td>60.80</td><td>89.26</td><td>69.55</td><td>73.20 (↓ 3.36)</td></tr><tr><td>Bi-Drop</td><td>60.08</td><td>89.73</td><td>70.88</td><td>73.56 (↓ 3.28)</td></tr></table>",
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+    },
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+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>Avg</td></tr><tr><td colspan=\"5\">Reduction Ratio: 70%</td></tr><tr><td>Vanilla</td><td>81.12</td><td>78.88</td><td>27.71</td><td>62.57 ( - )</td></tr><tr><td>CTD</td><td>83.10</td><td>79.50</td><td>28.14</td><td>63.58 (↑ 1.01)</td></tr><tr><td>R-Drop</td><td>81.37</td><td>76.46</td><td>32.61</td><td>63.48 (↑ 0.91)</td></tr><tr><td>Bi-Drop</td><td>81.88</td><td>80.17</td><td>37.66</td><td>66.57 (↑ 4.00)</td></tr><tr><td colspan=\"5\">Reduction Ratio: 60%</td></tr><tr><td>Vanilla</td><td>83.30</td><td>83.98</td><td>39.32</td><td>68.87 ( - )</td></tr><tr><td>CTD</td><td>85.78</td><td>84.55</td><td>37.65</td><td>69.33 (↑ 0.46)</td></tr><tr><td>R-Drop</td><td>83.55</td><td>85.07</td><td>43.51</td><td>70.71 (↑ 1.84)</td></tr><tr><td>Bi-Drop</td><td>84.33</td><td>85.35</td><td>49.62</td><td>73.10 (↑ 4.23)</td></tr><tr><td colspan=\"5\">Reduction Ratio: 50%</td></tr><tr><td>Vanilla</td><td>86.07</td><td>87.40</td><td>45.27</td><td>72.91 ( - )</td></tr><tr><td>CTD</td><td>87.98</td><td>87.64</td><td>50.78</td><td>75.47 (↑ 2.56)</td></tr><tr><td>R-Drop</td><td>86.04</td><td>88.89</td><td>55.73</td><td>76.89 (↑ 3.98)</td></tr><tr><td>Bi-Drop</td><td>86.73</td><td>89.61</td><td>58.27</td><td>78.20 (↑ 5.29)</td></tr></table>",
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+        "table_caption": [
+            "Table 5: Left: Robustness to label noise. The noise ratio is the percentage of training instances whose labels are transferred to incorrect labels. Right: Robustness to data imbalance. We reduce the number of instances labeled 1 by  $70\\% /60\\% /50\\%$  in the training set and test the accuracy of instances labeled 1 (as the minority class) in the validation set. Bi-Drop can maintain more robust representations compared with other fine-tuning methods."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Datasets</td><td colspan=\"4\">0.5K</td><td colspan=\"4\">1K</td></tr><tr><td>Vanilla</td><td>CTD</td><td>DPS</td><td>Bi-Drop</td><td>Vanilla</td><td>CTD</td><td>DPS</td><td>Bi-Drop</td></tr><tr><td>CoLA</td><td>36.23</td><td>40.77</td><td>41.87</td><td>44.78</td><td>48.92</td><td>51.63</td><td>52.89</td><td>54.76</td></tr><tr><td>MRPC</td><td>81.33</td><td>81.95</td><td>83.29</td><td>83.18</td><td>83.90</td><td>84.62</td><td>85.03</td><td>85.44</td></tr><tr><td>RTE</td><td>58.87</td><td>59.27</td><td>59.16</td><td>60.04</td><td>62.17</td><td>63.85</td><td>64.92</td><td>66.96</td></tr><tr><td>STS-B</td><td>82.60</td><td>82.79</td><td>83.41</td><td>85.19</td><td>85.91</td><td>86.92</td><td>87.26</td><td>87.53</td></tr><tr><td>SST-2</td><td>86.11</td><td>88.19</td><td>89.08</td><td>89.38</td><td>89.95</td><td>89.88</td><td>90.44</td><td>90.41</td></tr><tr><td>QNLI</td><td>78.76</td><td>79.32</td><td>79.27</td><td>80.48</td><td>82.49</td><td>83.65</td><td>83.86</td><td>84.35</td></tr><tr><td>QQP</td><td>71.88</td><td>73.43</td><td>74.22</td><td>74.61</td><td>77.65</td><td>78.57</td><td>78.79</td><td>79.01</td></tr><tr><td>MNLI</td><td>46.44</td><td>45.74</td><td>47.63</td><td>50.79</td><td>56.55</td><td>60.96</td><td>59.47</td><td>61.25</td></tr><tr><td>Avg</td><td>67.78</td><td>68.93</td><td>69.74</td><td>71.06</td><td>73.44</td><td>75.01</td><td>75.33</td><td>76.21</td></tr><tr><td>Δavg</td><td>-</td><td>↑1.15</td><td>↑1.96</td><td>↑3.28</td><td>-</td><td>↑1.57</td><td>↑1.89</td><td>↑2.77</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 6: Comparison between Bi-Drop and prior sub-net optimization strategies with varying low-resource scenarios (0.5K, 1K). We report the results of 10 random seeds and the best results are bold. Bi-Drop performs better than other methods in low-resource scenarios.",
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+        "type": "text",
+        "text": "achieves up to 4.00, 4.23, and 5.29 average score improvements on  $30\\%$ ,  $40\\%$ , and  $50\\%$  reduction ratios respectively, outperforming other fine-tuning methods at lower reduction ratios and showcasing its robustness towards the minority class.",
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+    {
+        "type": "text",
+        "text": "5.3 Performance in Low-Resource Scenarios",
+        "text_level": 1,
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+        "type": "text",
+        "text": "As illustrated in Section 1 and 2, compared with prior FI-based sub-net optimization methods that have a strong dependence on the training data, BiDrop proposes a step-wise sub-net selection strategy, which chooses the optimized parameters with the current mini-batch. In this section, we conduct extensive experiments to analyze how this dependency affects the performance of models. Concretely, we adopt various fine-tuning methods on",
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+    {
+        "type": "text",
+        "text": "$\\mathrm{BERT}_{\\mathrm{large}}$  with a limited amount of training data. The results are illustrated in Table 6. As the data amount decreases from  $1.0\\mathrm{K}$  to  $0.5\\mathrm{K}$ , the average improvement score of Child-Tuning  $_D$  over vanilla fine-tuning decreases from 1.57 to 1.15, while its improved variant DPS maintains a relatively stable improvement. But Bi-Drop improves the average improvement score from 2.77 to 3.28. The results indicate the superiority of Bi-Drop over prior methods in low-resource scenarios.",
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+    {
+        "type": "text",
+        "text": "5.4 Ablation Study",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "To evaluate the effectiveness of our proposed finetuning strategy, we conduct an ablation study in Table 7. The results show that both our sub-net selection strategy and gradient averaging strategy",
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+    {
+        "type": "page_number",
+        "text": "5221",
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+    {
+        "type": "table",
+        "img_path": "images/82db6f2fc5fd3873520f6adcebf91b0fef23f964a10e2b5203e581679e448483.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>Δ</td></tr><tr><td>Bi-Drop (gavg + ESS)</td><td>64.94</td><td>91.79</td><td>73.79</td><td>90.59</td><td>80.26</td><td>+1.55</td></tr><tr><td>gavg + Perturbation Factor</td><td>64.71</td><td>91.67</td><td>73.21</td><td>90.36</td><td>79.99</td><td>+1.28</td></tr><tr><td>gavg + Scaling Factor</td><td>64.38</td><td>91.63</td><td>72.96</td><td>90.41</td><td>79.85</td><td>+1.14</td></tr><tr><td>gavg + RSS</td><td>64.24</td><td>90.96</td><td>71.73</td><td>90.25</td><td>79.30</td><td>+0.59</td></tr><tr><td>gavg</td><td>63.82</td><td>91.26</td><td>71.16</td><td>89.81</td><td>79.01</td><td>+0.30</td></tr><tr><td>Vanilla</td><td>63.76</td><td>90.41</td><td>70.97</td><td>89.70</td><td>78.71</td><td>0.00</td></tr></table>",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Table 7: Ablation results. ESS represents our Effective Sub-net Selection strategy using both factors Perturbation and Scaling. RSS stands for Random Sub-net Selection strategy. Both our sub-net selection strategy and gradient averaging strategy are effective.",
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+    {
+        "type": "text",
+        "text": "contribute to the performance improvement of BiDrop.",
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+    },
+    {
+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "In this work, we propose a new sub-net optimization technique for large-scale PLMs, named BiDrop, which leverages the gradients of multiple sub-nets generated by dropout to select the updated parameters. Extensive experiments on various downstream tasks demonstrate that Bi-Drop achieves consistent and remarkable improvements over vanilla fine-tuning and prior excellent approaches by a considerable margin, across various model architectures. Further analysis indicates the generalizability and robustness of Bi-Drop over transferring, data imbalance and low-resource experiments.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "7 Limitations",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "We propose a novel and effective fine-tuning method, Bi-Drop, which achieves a considerable performance improvement in downstream tasks. However, similar to some previous studies(Jiang et al., 2020; Aghajanyan et al., 2021; Wu et al., 2021), Bi-Drop requires multiple forward propagations, which makes its training time efficiency not good enough compared with the vanilla fine-tuning method.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Acknowledgements",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "This paper is supported by the National Key Research and Development Program of China 2020AAA0106700 and NSFC project U19A2065.",
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+    {
+        "type": "text",
+        "text": "Appendix",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "A GLUE Benchmark Datasets",
+        "text_level": 1,
+        "bbox": [
+            114,
+            112,
+            394,
+            128
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "In this paper, we conduct experiments on datasets in GLUE benchmark (Wang et al., 2019). The statistical information of the GLUE benchmark is shown in Table 8.",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/44c19b502c5acf069e74de0ab5ed29590b5798000136cfceb9d8d9a3dddf522a.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dataset</td><td>#Train</td><td>#Dev</td><td>Metrics</td></tr><tr><td colspan=\"4\">Single-sentence Tasks</td></tr><tr><td>CoLA</td><td>8.5k</td><td>1.0k</td><td>Matthews Corr</td></tr><tr><td>SST-2</td><td>67k</td><td>872</td><td>Accuracy</td></tr><tr><td colspan=\"4\">Inference</td></tr><tr><td>RTE</td><td>2.5k</td><td>277</td><td>Accuracy</td></tr><tr><td>QNLI</td><td>105k</td><td>5.5k</td><td>Accuracy</td></tr><tr><td>MNLI</td><td>393k</td><td>9.8k</td><td>Accuracy</td></tr><tr><td colspan=\"4\">Similarity and Paraphrase</td></tr><tr><td>MRPC</td><td>3.7k</td><td>408</td><td>F1</td></tr><tr><td>STS-B</td><td>5.7k</td><td>1.5k</td><td>Spearman Corr</td></tr><tr><td>QQP</td><td>364k</td><td>40k</td><td>F1</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Table 8: Statistics and metrics of eight datasets used in this paper form GLUE benchmark.",
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+    },
+    {
+        "type": "text",
+        "text": "B Experimental Details",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "In this paper, we fine-tune different large pretrained language models with Bi-Drop, including BERT<sub>BASE</sub> <sup>3</sup>, BERT<sub>LARGE</sub> <sup>4</sup>, RoBERTa<sub>BASE</sub> <sup>5</sup>, RoBERTa<sub>LARGE</sub> <sup>6</sup>, DeBERTa<sub>LARGE</sub> <sup>7</sup>, and ELECTRA<sub>LARGE</sub> <sup>8</sup>. The training epochs/steps, batch size, and warmup steps are listed in Table 9.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "For the glue dataset, our maximum length is set as 128. We use grid search for learning rate from  $\\{1\\mathrm{e} - 5,2\\mathrm{e} - 5,\\dots ,1\\mathrm{e} - 4\\}$ . For Bi-Drop, we use grid search for dropout rate from  $\\{0.05,0.1\\}$ . The number of forward passes is fixed to two  $(k = 2)$ . We conduct all the experiments on a single A40 GPU.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "C Hyper-Parameter Analysis",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Bi-Drop uses two dropout techniques. In order to analyze the impact of the dropout rate on the experimental results, a simple analysis experiment was done here. In order to make the comparison fair, all the parameters except the dropout rate are kept the same in the experiment. For simplicity, the dropout values are the same twice. The experimental results are shown in 5. It can be seen that different datasets have different preferences for dropout values. CoLA and RTE achieve the best results when the dropout value is 0.05; while MRPC achieves the best results when the dropout value is 0.1; STSB is insensitive to the dropout value until the dropout value is less than 0.1.",
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/b8311c05816e0f0ebcf9cc01216fb55c5c6e25b04c2178d11bee761898a614a4.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/0609d070c281c03fa421a20cb1d41bf430416ebc01f5548c0cf1c2a29f33d067.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/42c40b77085fb1977e89073bb7380ed782e00e29dd85774a01dc8efaf2b9bfb3.jpg",
+        "image_caption": [
+            "Figure 5: The effect of dropout rate on experimental results"
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/e69eaa05ba5b842037e6a6faf46043155e8d37e392f4314595480062ee221fee.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "D Batch Size Doubled Training",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "We implement Bi-Drop by repeating the input data twice and forward-propagating twice. This is similar to doubling the batch size at each step. The difference is that half of the data is the same as the other half, and directly doubling the batch size, the data in the same mini-batch is all different. So for a fair comparison, we experimented with directly doubling the batch size. So for a fair comparison, we experimented with directly doubling the batch size. The experimental results are shown in Table 10, results show that directly doubling the batch size has basically no improvement, and Bi-Drop is significantly better than directly doubling the batch size.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "E Comparison with Prior Methods on Roberta large",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "We compare Bi-Drop with various prior fine-tuning methods based on  $\\mathrm{BERT}_{\\mathrm{large}}$  and report the mean",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "page_footnote",
+        "text": "3https://huggingface.co/bert-base-uncased/ tree/main 4https://huggingface.co/bert-large-cased/tree/ main 5https://huggingface.co/roberta-base/tree/ main 6https://huggingface.co/roberta-large/tree/ main 7https://huggingface.co/microsoft/ deberta-large/tree/main 8https://huggingface.co/google/ electra-large-discriminator/tree/main",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "page_number",
+        "text": "5225",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "table",
+        "img_path": "images/6f028938470dde92569d9672825b8d1acd5b017a25fb7495393521400736142d.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Dataset</td><td>Batch Size</td><td>Epochs/Steps</td><td>Warmup Ratio/Steps</td></tr><tr><td>BERT</td><td>all</td><td>16</td><td>3 epochs</td><td>10%</td></tr><tr><td rowspan=\"4\">Roberta</td><td>CoLA</td><td>16</td><td>5336 steps</td><td>320 steps</td></tr><tr><td>RTE</td><td>16</td><td>2036 steps</td><td>122 steps</td></tr><tr><td>MRPC</td><td>16</td><td>2296 steps</td><td>137 steps</td></tr><tr><td>STS-B</td><td>16</td><td>3598 steps</td><td>214steps</td></tr><tr><td rowspan=\"4\">ELECTRA</td><td>CoLA</td><td>32</td><td>3 epochs</td><td>10%</td></tr><tr><td>RTE</td><td>32</td><td>10 epochs</td><td>10%</td></tr><tr><td>MRPC</td><td>32</td><td>3 epochs</td><td>10%</td></tr><tr><td>STS-B</td><td>32</td><td>10 epochs</td><td>10%</td></tr><tr><td rowspan=\"4\">DeBERTa</td><td>CoLA</td><td>32</td><td>6 epochs</td><td>100 steps</td></tr><tr><td>RTE</td><td>32</td><td>6 epochs</td><td>50 steps</td></tr><tr><td>MRPC</td><td>32</td><td>6 epochs</td><td>10 steps</td></tr><tr><td>STS-B</td><td>32</td><td>4 epochs</td><td>100 steps</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "table",
+        "img_path": "images/e9daa61562c7822621e1f7861b98918fbb8bc97587fd38735e294466d908a6d7.jpg",
+        "table_caption": [
+            "Table 9: Hyperparameters settings for different pretrained models on variant tasks. These settings are reported in their official repository for best practice."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td></tr><tr><td>Vanilla</td><td>57.67</td><td>90.38</td><td>69.57</td><td>89.35</td><td>76.74</td></tr><tr><td>Vanilla(double bsz)</td><td>57.69</td><td>90.08</td><td>69.85</td><td>89.55</td><td>76.79</td></tr><tr><td>Bi-Drop</td><td>60.76</td><td>91.01</td><td>71.73</td><td>89.78</td><td>78.32</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "Table 10: Comparison of Bi-Drop and directly doubling the batch size. Bi-Drop is significantly better than directly doubling the batch size.",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "(and max) scores on GLUE benchmark in Table 11, following Lee et al. (2020) and Xu et al. (2021).",
+        "bbox": [
+            112,
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+            498
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "page_number",
+        "text": "5226",
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "table",
+        "img_path": "images/97d1803c8a3b734a717d4098652fd02bb4c7ee79cfb8590c2a8b4188d0b4482c.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>CoLA</td><td>MRPC</td><td>RTE</td><td>STS-B</td><td>Avg</td><td>Δ</td></tr><tr><td>Vanilla</td><td>66.01(68.03)</td><td>92.56(93.66)</td><td>84.51(86.28)</td><td>92.05(92.22)</td><td>83.78(85.05)</td><td>0.00</td></tr><tr><td>ChildTuningD</td><td>66.82(68.21)</td><td>92.67(93.58)</td><td>85.89(87.72)</td><td>92.36(92.53)</td><td>84.44(85.51)</td><td>+0.66(0.46)</td></tr><tr><td>DPSmix</td><td>66.86(68.53)</td><td>92.51(93.89)</td><td>85.37(87.72)</td><td>92.47(92.73)</td><td>84.30(85.72)</td><td>+0.52(0.67)</td></tr><tr><td>R-Drop</td><td>67.26(69.63)</td><td>92.47(93.62)</td><td>85.44(87.63)</td><td>92.42(92.58)</td><td>84.40(85.87)</td><td>+0.62(0.82)</td></tr><tr><td>Bi-Drop</td><td>68.03(70.89)</td><td>92.95(94.66)</td><td>86.10(88.09)</td><td>92.36(92.58)</td><td>84.86(86.56)</td><td>+1.08(1.51)</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "text",
+        "text": "Table 11: Comparison between Bi-Drop with prior fine-tuning methods. We report the mean (max) results of 10 random seeds. The best results are bold. Note that since R3F is not applicable to regression, the result on STS-B (marked with *) remains the same as vanilla. Bi-Drop achieves the best performance compared with other methods.",
+        "bbox": [
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+        ],
+        "page_idx": 13
+    },
+    {
+        "type": "page_number",
+        "text": "5227",
+        "bbox": [
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+        ],
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+    }
+]