Add Batch 9471e399-66ef-49f1-a174-d5f7d8fd3b94 data

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2025/Dialectal Coverage And Generalization in Arabic Speech Recognition/full.md

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+# Dialectal Coverage And Generalization in Arabic Speech Recognition
+
+Amirbek Djanibekov $^{1*}$ , Hawau Olamide Toyin $^{1*}$   
+Raghad Alshalan $^{2}$ , Abdullah Alitr $^{2}$ , Hanan Aldarmaki $^{1}$
+
+$^{1}$ Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE  $^{2}$ STC, Riyadh, Saudi Arabia
+
+{amirbek.djanibekov,hawau.toyin,hanan.aldarmaki}@mbzuai.ac.ae {rsalshalan,aalatir}@stc.com.sa
+
+# Abstract
+
+Developing robust automatic speech recognition (ASR) systems for Arabic requires effective strategies to manage its diversity. Existing ASR systems mainly cover the modern standard Arabic (MSA) variety and few high-resource dialects, but fall short in coverage and generalization across the multitude of spoken variants. Code-switching with English and French is also common in different regions of the Arab world, which challenges the performance of monolingual Arabic models. In this work, we introduce a suite of ASR models optimized to effectively recognize multiple variants of spoken Arabic, including MSA, various dialects, and code-switching. We provide open-source pre-trained models that cover data from 17 Arabic-speaking countries, and fine-tuned MSA and dialectal ASR models that include at least 11 variants, as well as multi-lingual ASR models covering embedded languages in code-switched utterances. We evaluate ASR performance across these spoken varieties and demonstrate both coverage and performance gains compared to prior models.
+
+# 1 Introduction
+
+The advent of large self-supervised acoustic models has transformed speech technology, enabling transfer learning and improving performance for both high-resource and low-resource languages. Prominent examples of such models include various versions of wav2vec (Schneider et al., 2019; Baevski et al., 2020), HuBERT (Hsu et al., 2021), and SpeechT5 (Ao et al., 2022), which have predominantly been trained on English datasets. Their multi-lingual variants, e.g. XLS-R (Babu et al., 2022) with 53 and 128 languages, in addition to models that include both self-supervised and supervised pre-training, such as Whisper (Radford et al., 2023) with approximately hundred supported languages, MMS (Pratap et al., 2024) with thousands
+
+of languages, and UniSpeech (Wang et al., 2021), illustrate the potential for cross-lingual transfer learning for more inclusive ASR. Yet, while these models indeed show great potential for transfer learning to new languages, even those unseen in training, they remain suboptimal for some target languages. A case in point is the Arabic Text and Speech Transformer (ArTST), a model pre-trained exclusively on Arabic, which has demonstrated superior performance for Modern Standard Arabic (MSA), surpassing larger multilingual models like Whisper and MMS in benchmark tests, in addition to establishing a new state-of-the-art performance compared to previous efforts for Arabic ASR. This highlights the advantage of monolingual pre-training when large amounts of unlabeled data for the target language are available. While the model showed some potential for dialectal coverage, it was trained and validated exclusively on MSA data, which questions its applicability for spoken dialectal variants of Arabic. Evaluations on code-switched data showed poor performance of ArTST compared to multilingual models (Kadaoui et al., 2024), demonstrating the delicate trade-off between monolingual and multilingual optimization. Arabic is a pluricentric language (Schuppler et al., 2024), diverse in regional variations, and models trained on MSA frequently struggle to adapt to these variations. This limitation is particularly acute given that many Arabic dialects are underrepresented and considered low-resource in speech technology research. Consequently, there is a need for optimized ASR systems that embrace, rather than overlook, the linguistic diversity of the Arabic-speaking world.
+
+In light of these challenges, we conduct various investigations aimed at understanding and enhancing the dialectal diversity and performance of Arabic ASR systems. We focus on four inquiries aimed at optimizing potential strategies for integrating dialectal variation into ASR systems. First, we mea
+
+![](images/94a3a03932fa8c7277e5bced2c5e84871a56f4b563ca6380bf40450e207f3934.jpg)  
+Figure 1: The architecture of SpeechT5/ArTST, which contains an encoder-decoder module and six modal specific pre/post-nets. During self-supervised pre-training (left), quantized tokens are shared across speech and text modalities. Hidden states and latent units are mixed up and used as the inputs of the cross-attention module in the decoder. The fine-tuning stage for ASR is shown on the right. Refer to Ao et al. (2022) for more details.
+
+![](images/d428b7bdb7ec322c31c44c887b7e00221f35e74a96616bb821f65761491847b8.jpg)
+
+sure the impact of incorporating a broad collection of Arabic dialects during the model's pre-training phase. We hypothesize that a wider dialectal foundation could improve the model's performance across various dialects in the fine-tuning stage. Second, we quantify the comparative effectiveness of dialect-specific fine-tuning versus a more holistic, multi-dialectal fine-tuning strategy. The third question examines the model's capacity for zero-shot transfer to dialects not explicitly included in fine-tuning. Finally, we evaluate the model on code-switched utterances, and examine the effect of multilingual pre-training and fine-tuning on both monolingual and code-switched datasets. Our key findings from experiments spanning over 17 variants of spoken Arabic are: (1) pre-training with more data and wider dialectal coverage improves performance across most dialectal variants, including MSA, (2) multi-dialectal fine-tuning improves performance for low-resource dialects, but may not be optimal for high-resource dialects, (3) multi-dialectal pretraining and fine-tuning has higher potential for zero-shot transfer to unseen dialects, and (4) multilingual pre-training and fine-tuning greatly boosts performance on code-switching, while negatively impacting monolingual performance due to language interference. Our pretraining checkpoints and joint models were trained exclusively on open-source data and are released as open-source, openweights models. All scripts required to reproduce our results including model training, evaluation, and checkpoints, are publicly available<sup>1</sup>.
+
+# 2 Related Work
+
+Arabic speech recognition research has a long history, but the majority of this research has focused on Modern Standard Arabic (MSA), the formal variant predominant in news broadcasts and official communications. A review article covering peer-reviewed publications between 2011 and 2021 estimates that  $89\%$  of papers on Arabic ASR cover MSA, and only a quarter cover some dialectal variant of Arabic (Dhouib et al., 2022). Recent research in end-to-end ASR for Arabic, as presented in Hussein et al. (2022), demonstrates the potential of contemporary deep learning techniques for decoding spoken Arabic, but it was also limited to MSA. Large-scale multilingual ASR models, such as Whisper (Radford et al., 2023) and MMS (Pratap et al., 2024), cover many languages within their scope, including Arabic. They utilize language embeddings or adapters to enhance language coverage and performance within the same model, but their performance across languages vary considerably. For instance, while Whisper demonstrates strong zero-shot capabilities on MSA, its zero-shot accuracy drops substantially on dialects, and additional finetuning on dialectal data is needed to improve performance (Waheed et al., 2023). Recent work has shown that smaller, Arabic-specific student models distilled from large models like Whisper can achieve comparable or even superior performance, especially on dialectal data, with good generalization to unseen dialects (Waheed et al., 2024). Specialized Arabic models like ArTST (Toyin et al., 2023), primarily pre-trained on MSA, have shown
+
+competitive results on MSA tasks and even outperformed larger multilingual models on some mixed-dialect datasets like QASR (Mubarak et al., 2021). However, due to its monolingual pretraining, the model was shown to perform poorly in code-switched Arabic-English speech (Al Ali and Aldarmaki, 2024). This illustrates that strong MSA performance is not a reliable predictor for dialectal or code-switching capabilities, with a substantial gap persisting between SOTA MSA and dialectal performance. The development of diverse datasets such as QASR (multi-dialectal broadcast news) (Mubarak et al., 2021), SADA (Saudi Arabic) (Alharbi et al., 2024), ArZen (Egyptian-English code-switching) (Al-Sabbagh, 2024), and Mixat (Emirati-English code-switching) (Al Ali and Aldarmaki, 2024), and other public datasets covering various dialects and code-switched instances presents an opportunity for improving the generalization of ASR systems to diverse spoken varieties.
+
+# 3 Methodology
+
+Based on prior work, we start with the premise that monolingual training is more suitable for maximizing performance in Arabic ASR. However, the current Arabic SOTA models have limited coverage of spoken varieties and struggle with code-switching due to their monolingual training. Our objective is to maximize performance while also widening the coverage to include MSA, regional dialects, and instances of code-switching. To that end, we start with an Arabic-centric ASR model, ArTST (Toyin et al., 2023), as the foundation for our investigation. Figure 1 illustrates the high-level architecture of ArTST for self-supervised pre-training and fine-tuning. This model is based on the SpeechT5 approach (Ao et al., 2022), and supports multi-modal fine-tuning. The first version of the model was pre-trained on the MGB2 (Ali et al., 2016) dataset, which consists of newswire data, mainly in MSA, with a small subset of dialectal variants. In this work, we attempt to understand the factors that enable both high performance and wide coverage; we explore the following questions:
+
+1. Is pretraining on dialectal data beneficial for improving down-stream dialectal performance, and would it negatively impact MSA performance?  
+2. Is it better to finetune ASR models jointly on multiple dialects or fine-tune on a specific target dialect?
+
+3. Can we achieve reasonable zero-shot performance on unseen dialects?  
+4. Can we optimize performance in code-switched utterances using multilingual pretraining?  
+5. What is the effect of multilingual pretraining and finetuning on monolingual Arabic performance? (i.e. language interference).
+
+The remaining sections detail our experimental settings and findings of these questions.
+
+# 3.1 Terminology
+
+For the rest of the paper, we will refer to Arabic variants using abbreviations. The categories below are based on regions and countries, and do not reflect any official classification of dialectal families:
+
+MSA: Modern Standard Arabic. This is a common official variant of Arabic used in news, books, and education. CA: Classical Arabic. This is an old variant of Arabic found on religious texts and old books. It resembles MSA, but also contains outdated lexical items and structures.
+
+GLF: A broad category of dialects spoken in the Arabian Peninsula, in particular the Gulf region, which, in our data sources, include SAU: Saudi, KUW: Kuwait, UAE, OMA: Oman, QAT: Qatar, IRA: Iraq, and YEM: Yemen.
+
+LEV: Levantine dialects, which, in our data sources, include SYR: Syria, JOR: Jordan, LEB: Lebanon, and PAL: Palestine.
+
+NOR: North African dialects, including EGY: Egypt, TUN: Tunisia, MOR: Morocco, ALG: Algeria, MAU: Mauritania, and SUD: Sudanese.
+
+# 3.2 Pre-Training Data & Settings
+
+To examine the effect of pre-training data coverage on downstream performance, we pre-trained ArTST from scratch² on both MSA and dialectal data. We sourced our data from various datasets, including: MGB2 (Ali et al., 2016), QASR (Mubarak et al., 2021) MGB3 (Ali et al., 2017), MGB5 (Ali et al., 2019), ClArTTS (Kulkarni et al., 2023), ASC (Halabi et al., 2016), and Common Voice (Ardila et al., 2020), SADA (Alharbi et al., 2024), and others. We also used MADAR (Bouamor et al., 2018) and NADI (Abdul-Mageed et al., 2023) text
+
+<table><tr><td>Dataset</td><td>Dialect</td><td>Hours</td><td>Words</td></tr><tr><td>QASR</td><td>MSA</td><td>2000 hrs</td><td>13.33 M</td></tr><tr><td>MGB2</td><td>MSA</td><td>1000 hrs</td><td>7.31 M</td></tr><tr><td>MGB3[ASR]</td><td>EGY</td><td>2.83 hrs</td><td>18.93 K</td></tr><tr><td>MGB5[ASR]</td><td>MOR</td><td>6.74 hrs</td><td>56.97 K</td></tr><tr><td>SADA (Alharbi et al., 2024)</td><td>SAU</td><td>418 hrs</td><td>3.25 M</td></tr><tr><td>Mixat (Al Ali and Aldarmaki, 2024)</td><td>UAE</td><td>15 hrs</td><td>57.94 K</td></tr><tr><td>TARIC-SLU (Mdhaffar et al., 2024)</td><td>TUN</td><td>8 hrs</td><td>72.00 K</td></tr><tr><td rowspan="4">ParallelCorp (Almeman et al., 2013)</td><td>MSA</td><td>32 hrs</td><td>30.66K</td></tr><tr><td>GLF</td><td>32 hrs</td><td>27.26K</td></tr><tr><td>LEV</td><td>32 hrs</td><td>18.43K</td></tr><tr><td>EGY</td><td>32 hrs</td><td>48.56K</td></tr><tr><td rowspan="13">MASC (Al-Fetyani et al., 2021)</td><td>MSA</td><td>612.28 hrs</td><td>3.80 M</td></tr><tr><td>SAU</td><td>452.24 hrs</td><td>301.92 K</td></tr><tr><td>SYR</td><td>211.33 hrs</td><td>1.06 M</td></tr><tr><td>EGY</td><td>175.36 hrs</td><td>1.03 M</td></tr><tr><td>JOR</td><td>42.21 hrs</td><td>330.83 K</td></tr><tr><td>LEB</td><td>25.20 hrs</td><td>155.76 K</td></tr><tr><td>IRA</td><td>17.37 hrs</td><td>121.12 K</td></tr><tr><td>TUN</td><td>12.17 hrs</td><td>34.34 K</td></tr><tr><td>Multiple</td><td>10.57 hrs</td><td>80.08 K</td></tr><tr><td>UAE</td><td>9.87 hrs</td><td>6.42 K</td></tr><tr><td>MOR</td><td>8.60 hrs</td><td>58.38 K</td></tr><tr><td>PAL</td><td>6.17 hrs</td><td>45.35 K</td></tr><tr><td>KUW</td><td>4.04 hrs</td><td>32.37 K</td></tr></table>
+
+Table 1: Summary of Dataset Statistics for Fine-Tuning: Hours of Audio, Word Counts, and Associated Dialects. Multiple is mix of several dialects not necessary from the listed dialects (no information from the source).
+
+datasets for pretraining. In our experiments, we compare the following:
+
+- v1: This variant is as described in Toyin et al. (2023), pretrained only on MSA.  
+- v2: In this variant, we use a mixture of MSA and dialectal data in pretraining.  
+- v3: In this variant, we use a mixture of MSA, dialectal, and multilingual data in pretraining.
+
+See Table 12 in Appendix A for details of all the datasets used in pre-training.
+
+# 3.3 Dialectal Fine-Tuning
+
+The datasets we use for dialectal fine-tuning are shown in Table 1. We collected as many open-source data as needed to maximize coverage of dialects. Note that, for MGB5 and MGB3, as the data is based on YouTube videos, many of the originally referenced videos are no longer available, so at the time of our experiments, only 2.5 hours of training were available for MGB3 and 2 hours for MGB5. Furthermore, multi dialectal datasets, such as MASC (Al-Fetyani et al., 2021), have unbalanced representation of dialects. The high-resource dialects in our collection include SAU, SYR, EGY, and MSA; each has at least 200 hours of transcribed
+
+![](images/2ddee320a12dcd94dbfc5c1e98f4b6749823c15821c9317ce2ceab5400e6664c.jpg)  
+Figure 2: Distribution of dialectal speech data in pretraining and fine-tuning. MSA data are not shown.
+
+ASR data. UAE, MOR, JOR, LEB, IRA, and TUN have a medium amount of fine-tuning data between 10 and 50 hours. KUW and PAL are low-resource dialects with less than 10 hours of transcribed data in total. Finally, we left ALG, YEM, and SUD from the MASC dataset for zero-shot testing.
+
+Figure 2 illustrates the distribution of dialectal data we use for pre-training and fine-tuning our dialectal model. We exclude MSA from the figure as it has disproportionately more data than all dialects.
+
+# 3.4 Multi-Lingual Fine-Tuning
+
+In addition to the above dialectal data, we used English, French, and Spanish sets for multi-lingual fine-tuning and code-switching experiments described in section 8. English and French are commonly spoken in various Arabic-speaking countries, and to a lesser extent, Spanish is spoken in some parts of North Africa. More details about the datasets used in these experiments are provided in section 8.
+
+# 3.5 Experimental Settings
+
+For partitioning the data into training, development, and test sets, we adhered to the official splits provided with each dataset. We followed the data preparation and training methodology established in the original ArTST implementation. For comprehensive details regarding the model architecture and data preprocessing, readers are directed to Toyin et al. (2023).
+
+Computational Details The pre-training was executed on a cluster of 4 A100 GPUs over a duration of 14 to 21 days for each model. We used Adam optimizer with a learning rate of  $2 \times 10^{-4}$ , spanning 335K updates, and a warm-up phase of 64K updates. The maximum speech token length was set at 250K (equivalent to 15.625 seconds). Each
+
+fine-tuning experiment was run on one A100 GPU over a duration of 7 days (MGB2, QASR, MASC, SADA) or 2 days for smaller sets (MGB3, MGB5, etc.). We used Adam optimizer with a tri-stage scheduler with learning rate of  $6 \times 10^{-5}$ . The total computational budget for all experiments is estimated to be  $\sim 6000$  GPU-hours.
+
+Normalization Prior to model training, we implemented the same data normalization steps outlined in Tojin et al. (2023). In addition, we applied post-prediction normalization steps before calculating Word Error Rates (WER), following standard practices in Arabic ASR. All reported results reflect post-normalization performance. The normalization script, sourced from a publicly available GitHub repository<sup>3</sup>, performs orthographic standardization of Alef, Yaa, and Taa characters.
+
+# 4 Effect of Pre-Training Data
+
+We first examine the effect of pre-training on downstream ASR performance. As described in section 3.2, we compare a model pre-trained mainly on MSA (ArTST v1), and our multi-dialectal version (henceforth v2). Note that pre-training does not utilize aligned speech and text; it incorporates unaligned speech and text data for self-supervised learning. For these experiments, we use the same finetuning data, and only vary the pretraining sets.
+
+# 4.1 Benchmarking MSA Performance
+
+We first report results on benchmark datasets to compare the performance of both models against the state-of-the-art. MGB2 is the main benchmark for MSA speech recognition. We evaluated the performance of ArTST v1 and v2 fine-tuned in MGB2, compared to existing SOTA models, in Table 2. The results show that incorporating dialectal data in pretraining does not negatively affect MSA performance, as we achieve the bestWER of  $12.48\%$  and  $12.39\%$  , with and without LM fusion.
+
+# 4.2 Benchmarking Dialectal Performance
+
+Tables 3 and 4 show the performance of the models on the dialectal MGB3 (Egyptian) and MGB5 (Moroccan) benchmarks. Each of these benchmarks contain multiple references as dialectal speech has no standard spelling. We report the average and multi-reference WER for our model variants, and compare against the best model in each challenge,
+
+<table><tr><td>System</td><td>WER(%)</td><td>CER(%)</td></tr><tr><td colspan="3">From (Hussein et al., 2022):</td></tr><tr><td>HMM-DNN</td><td>15.80</td><td>—</td></tr><tr><td>E2E, CTC + LM</td><td>16.90</td><td>—</td></tr><tr><td>E2E, Attention + LM</td><td>13.40</td><td>—</td></tr><tr><td>E2E, CTC, Attention + LM</td><td>12.50</td><td>—</td></tr><tr><td>ArTST v1 + LM (Toyin et al., 2023)</td><td>12.78</td><td>6.33</td></tr><tr><td>v2</td><td>12.49</td><td>6.44</td></tr><tr><td>v2 + LM</td><td>12.39</td><td>6.51</td></tr></table>
+
+Table 2: Comparing our performance against models reported in Hussein et al. (2022) and Toyin et al. (2023), which include the best performing models previously reported on MGB2.  
+
+<table><tr><td>System</td><td>Adaptation</td><td>Fine-Tuning</td><td>AV-WER</td><td>MR-WER</td></tr><tr><td>Aalto</td><td>MGB2</td><td>MGB3</td><td>37.50</td><td>29.30</td></tr><tr><td>Whisper</td><td>ComVoice 
+Fleurs 
+Covost2</td><td>MGB3</td><td>39.04</td><td>24.92</td></tr><tr><td>MMS</td><td>BibleTrans 
+NewTestamentRec</td><td>MGB3</td><td>100.04</td><td>99.92</td></tr><tr><td>v1</td><td>MGB2</td><td>MGB3</td><td>37.08</td><td>29.39</td></tr><tr><td>v2</td><td>MGB2</td><td>MGB3</td><td>33.20</td><td>25.28</td></tr></table>
+
+Table 3:WER(%) on MGB3 Egyptian ASR. Aalto is the best system in the MGB3 challenge (Ali et al., 2017)  
+
+<table><tr><td>System</td><td>Adaptation</td><td>Fine-Tuning</td><td>AV-WER</td><td>MR-WER</td></tr><tr><td>RDI-CU</td><td>MGB2</td><td>MGB5</td><td>59.40</td><td>37.60</td></tr><tr><td>Whisper</td><td>ComVoice 
+Fleurs 
+Covost2</td><td>MGB5</td><td>164.13</td><td>227.34</td></tr><tr><td>MMS</td><td>BibleTrans 
+NewTestamentRec</td><td>MGB5</td><td>111.89</td><td>102.30</td></tr><tr><td>v1</td><td>MGB2</td><td>MGB5</td><td>49.39</td><td>27.95</td></tr><tr><td>v2</td><td>MGB2</td><td>MGB5</td><td>48.91</td><td>28.02</td></tr></table>
+
+Table 4: WER(%) on Moroccan ASR. RDI-CU is the best system in the MGB5 challenge (Ali et al., 2019)
+
+as well as the SOTA model in each benchmark. Each model is first fine-tuned on MSA, then fine-tuned again on the target MGB train sets. We also report the results of the large multilingual models: Whisper (Radford et al., 2023) and MMS (Pratap et al., 2024), fine-tuned on the same set. We refer to the Arabic data the models are previously fine-tuned on as 'Adaptation' data. Starting with MSA data before fine-tuning on the target dialect has previously been established as an effective strategy for dialectal ASR (Ali et al., 2017).
+
+In MGB3, dialectal pretraining (v2) results in about  $4\%$  absolute reduction in WER, establishing a new SOTA result on this benchmark. Smaller improvement in terms of Average WER is observed
+
+<table><tr><td rowspan="2">Dataset</td><td colspan="4">Zero-Shot</td><td colspan="2">Fine-Tuning</td></tr><tr><td>Whisper</td><td>MMS</td><td>v1</td><td>v2</td><td>v1</td><td>v2</td></tr><tr><td>TARIC-SLU (TUN)</td><td>138.14</td><td>93.54</td><td>107.56</td><td>106.46</td><td>14.70</td><td>14.80</td></tr><tr><td>ParallelCorp (MULT)</td><td>99.17</td><td>83.16</td><td>128.72</td><td>141.92</td><td>9.57</td><td>9.31</td></tr><tr><td>SADA (SAU)</td><td>82.16</td><td>78.28</td><td>39.41</td><td>29.77</td><td>39.24</td><td>29.91</td></tr><tr><td>MASC</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SAU</td><td>48.39</td><td>65.30</td><td>61.23</td><td>58.72</td><td>27.40</td><td>27.33</td></tr><tr><td>SYR</td><td>26.65</td><td>33.21</td><td>21.99</td><td>18.37</td><td>18.64</td><td>17.42</td></tr><tr><td>EGY</td><td>41.73</td><td>66.04</td><td>50.87</td><td>47.17</td><td>38.47</td><td>36.43</td></tr><tr><td>JOR</td><td>28.65</td><td>54.63</td><td>61.23</td><td>34.97</td><td>19.72</td><td>21.08</td></tr><tr><td>LEB</td><td>40.95</td><td>64.58</td><td>35.65</td><td>42.66</td><td>30.01</td><td>28.05</td></tr><tr><td>IRA</td><td>41.69</td><td>59.33</td><td>50.46</td><td>48.03</td><td>31.10</td><td>34.64</td></tr><tr><td>TUN</td><td>47.45</td><td>60.58</td><td>50.37</td><td>46.67</td><td>19.26</td><td>18.52</td></tr><tr><td>MOR</td><td>65.87</td><td>80.84</td><td>78.92</td><td>66.87</td><td>47.59</td><td>49.40</td></tr><tr><td>PAL</td><td>53.20</td><td>83.72</td><td>77.94</td><td>73.53</td><td>55.88</td><td>53.53</td></tr><tr><td>KUW</td><td>36.00</td><td>81.71</td><td>64.74</td><td>52.02</td><td>50.29</td><td>46.24</td></tr></table>
+
+for MGB5, where there is no clear advantage observed using our dialectal version. This difference is likely a result of our pre-training having a lot more Egyptian than Moroccan data (see Figure 2).
+
+# 4.3 Zero-Shot & Fine-Tuning Results
+
+To further quantify the effect of dialectal pretraining, we evaluate the performance of our model across different datasets. We first fine-tune models on MSA using MGB2 dataset. We test the model performance on dialects directly (zero-shot) and with dialectal fine-tuning. The results are shown in Table 5. On average, we see improvements in performance in both zero-shot and fine-tuning experiments using dialectal pretraining (v2) compared to MSA-centric pretraining (v1). We also see that both models perform better than Whisper and MMS in zero-shot settings in most cases. There are some exceptions, such as in KUW, where Whisper performs better than all other models, including the fine-tuned models, but in most cases v2 performs best. This underscores the advantage of monolingual models compared to multilingual performance, as observed in Toyin et al. (2023) and Radford et al. (2023). In addition, the results underscore the importance of dialectal coverage in pretraining: the cases where v2 performs worse than v1 are all dialects for which pretraining data are limited, such as TUN (no pretraining data) and JOR (smallest dialect size in pretraining).
+
+# 5 Joint Models & Dialect ID
+
+So far, models were first fine-tuned on MSA, followed by additional fine-tuning on each target dialect. This results in a separate model per dialect, which incurs memory costs and may have practical limitations as it requires advance knowledge of the dialect ID for deploying the correct model.
+
+In this section, we assess the relative effectiveness of individual dialectal fine-tuning compared with joint dialect fine-tuning, where we train a single model for all dialects. To that end, we joined multiple dialectal train sets from MASC, as shown in Table 6. We excluded ALG, YEM, SUD for zero-shot evaluation. The resulting joint corpus consists of 12 dialects including MSA, with approximately 1,501 hours in total. We fine-tuned a single joint model using this data.
+
+Table 5: WER (%) in zero-shot and fine-tuning settings. We compare zero-shot performance of Whisper, MMS, ArTST v1, and Our dialectal pretraining (v2). ArTST v1 and v2 are finetuned on MGB2 (MSA), whereas Whisper and MMS are finetuned/pretrained with multi-lingual data, including Arabic.  
+
+<table><tr><td>Dialect</td><td>Hours</td><td>Words</td><td>Source</td></tr><tr><td>MSA</td><td>612.28 hrs</td><td>3.80 M</td><td>MASC</td></tr><tr><td>SAU</td><td>452.24 hrs</td><td>301.92 K</td><td>SADA, MASC</td></tr><tr><td>SYR</td><td>211.33 hrs</td><td>1.06 M</td><td>MASC</td></tr><tr><td>EGY</td><td>175.36 hrs</td><td>1.03 M</td><td>MGB3, MASC</td></tr><tr><td>JOR</td><td>42.21 hrs</td><td>330.83 K</td><td>MASC</td></tr><tr><td>LEB</td><td>25.20 hrs</td><td>155.76 K</td><td>MASC</td></tr><tr><td>IRA</td><td>17.37 hrs</td><td>121.12 K</td><td>MASC</td></tr><tr><td>TUN</td><td>12.17 hrs</td><td>34.34 K</td><td>TARIC-SLU, MASC</td></tr><tr><td>UAE</td><td>9.87 hrs</td><td>6.42 K</td><td>Mixat, MASC</td></tr><tr><td>MOR</td><td>8.60 hrs</td><td>58.38 K</td><td>MASC</td></tr><tr><td>PAL</td><td>6.17 hrs</td><td>45.35 K</td><td>MASC</td></tr><tr><td>KUW</td><td>4.04 hrs</td><td>32.37 K</td><td>MASC</td></tr></table>
+
+Table 6: Datasets used to train the joint models.
+
+<table><tr><td>Approach</td><td colspan="2">Zero-Shot</td><td>Fine-Tuning</td><td>No Dialect ID</td><td>Dialect Forcing</td><td>Dialect Inference</td></tr><tr><td>Fine-Tuning Data</td><td>MGB2</td><td>QASR</td><td>MGB2→Target</td><td colspan="3">Joint Mutli-Dialectal Set (Table 6)</td></tr><tr><td>SAU</td><td>58.72</td><td>43.41</td><td>27.33</td><td>29.41</td><td>30.56</td><td>29.94</td></tr><tr><td>SYR</td><td>18.37</td><td>16.20</td><td>17.42</td><td>19.20</td><td>22.41</td><td>20.30</td></tr><tr><td>EGY</td><td>47.17</td><td>38.78</td><td>36.43</td><td>45.17</td><td>61.06</td><td>46.79</td></tr><tr><td>JOR</td><td>34.97</td><td>25.42</td><td>21.08</td><td>19.63</td><td>21.49</td><td>20.11</td></tr><tr><td>LEB</td><td>42.66</td><td>40.51</td><td>28.05</td><td>28.22</td><td>29.43</td><td>26.89</td></tr><tr><td>IRA</td><td>48.03</td><td>40.27</td><td>36.10</td><td>29.33</td><td>31.75</td><td>30.83</td></tr><tr><td>TUN</td><td>46.67</td><td>45.93</td><td>26.67</td><td>37.23</td><td>28.47</td><td>27.74</td></tr><tr><td>MOR</td><td>66.87</td><td>55.42</td><td>56.63</td><td>57.49</td><td>53.89</td><td>49.10</td></tr><tr><td>PAL</td><td>73.53</td><td>45.59</td><td>53.53</td><td>46.22</td><td>43.90</td><td>44.48</td></tr><tr><td>KUW</td><td>52.02</td><td>45.09</td><td>46.24</td><td>35.43</td><td>39.43</td><td>37.71</td></tr><tr><td>MSA</td><td>21.09</td><td>16.78</td><td>15.34</td><td>11.59</td><td>12.66</td><td>12.09</td></tr><tr><td>Macro Average</td><td>46.37</td><td>37.58</td><td>33.17</td><td>32.63</td><td>34.09</td><td>31.45</td></tr></table>
+
+Table 7:WER  $(\%)$  of various models compared with joint dialectal fine-tuning with different dialect ID strategies.
+
+# 5.1 Dialect ID
+
+We trained another model with the aforementioned joint dataset, but with the inclusion of explicit dialect identifiers. We augmented the dictionary with special tokens for dialect IDs, and used them to preprocess the decoding string:
+
+$$
+<   S > \text {D I A E C T} T _ {1} T _ {2} \dots T _ {n} <   / S >
+$$
+
+For inference, we experimented with two strategies: (1) Transcribing with dialect forcing, where we manually add the dialect ID to condition the decoder output; the decoder is forced to start predictions with the tokens  $\langle S \rangle$  DIALECT. (2) Transcribing with dialect inference, where we let the model predict the dialect token. We use this approach for zero-shot ASR on unseen dialects (Table 9).
+
+The results of the models trained with joint dialects compared to models trained on MGB2 and QASR are shown in Table 7. Note that both MGB2 and QASR contain mostly MSA, but also a small amounts of various dialects, but their exact distribution is unknown. We also reproduce the fine-tuning results from Table 5 for easy comparison. We see that joint modeling results in improvement for low-resource dialects, including: JOR, TUN, and KUW, but degrades performance of the high-resource SYR and EGY dialects. Interestingly, dialect forcing was worse on average than joint modeling with no dialect ID, while dialect inference resulted in the best performance overall. We surmise that the model learns dialectal patterns that do not perfectly align with the dialect ID as indicated in the training data. Since the dialect IDs are coarse country-level approximations, letting the model infer the dialect based on the speech is the best approach for most cases. Many dialectal sets, such as SYR and SAU, contain a lot of MSA utterances that are incorrectly identified as dialectal.
+
+Figure 3 illustrates dialect inference errors. Note that the number of errors is proportional to the test data size. The overall dialect identification performance is around  $90\%$ . Some low-resource dialects, such as KUW, are predicted as their closest high-resource variant, such as SAU, resulting in worse performance compared to joint models without a dialect ID, but on average, dialect forcing leads to the lowest WER.
+
+![](images/8a4b5061fa974f7103a3ec81105bd92e8781ebe01d5bb26c0dc7dce15b39e2d6.jpg)  
+Figure 3: Dialect identification performance: true (left), predicted (right). All lines are proportional to their ratio over the total errors except for SAU  $\rightarrow$  MSA, which is reduced 5 times for clarity.
+
+# 6 Effect of Data Adaptation
+
+In the above experiments, we followed the strategy of initializing the models by first fine-tuning on MSA data. In most cases, we used MGB2 as the base model, following previously established results on Egyptian ASR (Ali et al., 2017). This adaptation approach is meant to enhance the performance on low-resource dialects, facilitating faster
+
+<table><tr><td>Adaptation
+Fine-Tuning</td><td>—</td><td>MGB2</td><td>QASR</td></tr><tr><td>MGB3</td><td>136.52</td><td>25.28</td><td>19.53</td></tr><tr><td>MGB5</td><td>94.84</td><td>28.02</td><td>27.58</td></tr><tr><td>TARIC-SLU</td><td>30.46</td><td>14.80</td><td>14.48</td></tr><tr><td>ParallelCorp</td><td>28.97</td><td>9.31</td><td>9.08</td></tr><tr><td>SADA</td><td>29.77</td><td>29.91</td><td>29.55</td></tr><tr><td>Mixat</td><td>100.0</td><td>33.40</td><td>35.21</td></tr></table>
+
+convergence with limited training samples. However, as pre-training on more diverse sets proved to be effective, adaptation on more diverse data is also likely to be fruitful. As observed in Table 7, models trained on QASR resulted in far better zero-shot performance, approaching the performance of joint-dialects models. This is attributed to the fact that QASR is both larger in size and known to have more dialectal data compared to MGB2 (but both have no documented statistics of dialectal coverage). To validate this observation, we experimented with dialectal fine-tuning adapted from two variants: one based on MGB2 and one based on QASR (Mubarak et al., 2021), followed by dialect-specific fine-tuning. Table 4 shows fine-tuning results with no adaptation (directly fine-tuning on the target dialect), compared with starting from MGB2 or QASR. First, our results corroborate the previous findings that adapting models from MSA results in large reduction in error rates. In all except the Mixat dataset, starting from QASR results in better performance compared to MGB2. However, the difference is negligible except on MGB3 Egyptian set (around  $6\%$  absolute WER reduction). There are two factors that we speculate underline this result: The small size of the MGB3 set, and the existence of Egyptian dialect in the QASR corpus more substantially than the other dialects. Overall, using the QASR dataset as a basis for adapting dialectal models is recommended as it improves or maintains performance.
+
+# 7 Zero-Shot Performance
+
+We show the zero-shot performance on the three held-out sets: ALG, SUD, and YEM. We compare the baseline, v1, with multi-dialectal pretraining (v2). We also compare models fine-tuned on MGB2, QASR, or our joint dialectal set. The results are shown in Table 9. Our model achieves slightly lower error rates compared to v1, even
+
+Table 8:WER  $(\%)$  of fine-tuned models on various datasets with different adaptation methods: —: no adaptation, MGB2, or QASR.  
+
+<table><tr><td>Dialect System</td><td>ALG</td><td>SUD</td><td>YEM</td></tr><tr><td>ArTST v1→MGB2</td><td>73.18</td><td>69.20</td><td>41.64</td></tr><tr><td>v2→MGB2</td><td>70.82</td><td>69.31</td><td>39.45</td></tr><tr><td>v2→QASR</td><td>51.72</td><td>46.64</td><td>34.78</td></tr><tr><td>v2→Joint</td><td>45.20</td><td>40.69</td><td>33.08</td></tr><tr><td>v2→Joint (w. dialect inference)</td><td>47.12</td><td>40.15</td><td>31.84</td></tr></table>
+
+Table 9: WER% of various models on held-out dialects.
+
+when fine-tuned on the same MGB2 set. Better performance is achieved with QASR, which includes some dialectal data. The joint dialectal fine-tuning achieves the best performance on the held-out dialects. In general, performance in held-out sets is on a par with low-resource dialects, with WER above  $30\%$ . Table 13 in the Appendix shows the zero-shot performance after fine-tuning on a single target dialect.
+
+# 8 Code-Switching Performance
+
+The models analyzed so far were trained exclusively on Arabic data. While small amounts of code-switching (CS) exist in these sources, they are insufficient to learn the characteristics of the embedded languages. Large multi-lingual models are generally more effective on CS data (Kadaoui et al., 2024), even if they are less competent on monolingual Arabic. To make our models more inclusive, improving performance in the presence of code-switching is necessary. To that effect, we train a multilingual version of the model (we will refer to this as v3). The pre-training data for this version are listed in Table 12 in the Appendix. We test v3 against v1 and v2 on available CS data for Arabic: ArZN (Al-Sabbagh, 2024) for Egyptian-English speech, Mixat (Al Ali and Aldarmaki, 2024) for Emirati-English speech, and TunSwich (Abdallah et al., 2024) for Tunisian-French speech. We also train a joint multi-lingual model (without dialect or language ID). In addition to the datasets described in Table 6, we add the multi-lingual and code-switching data shown in Table 10.
+
+In Table 11, we show the performance of models finetuned directly from the pretrained checkpoints, or finetuned from existing ASR checkpoints (MGB2 checkpoint for v1, joint multi-dialectal checkpoint for v2, and joint multi-lingual checkpoint for v3). First, for models fine-tuned directly on the target set, we observe that multilingual pretraining significantly improves performance across all CS test sets, resulting in more than  $10\%$  ab
+
+<table><tr><td>Languages</td><td>Hours</td><td>Words</td><td>Source</td></tr><tr><td>EN</td><td>1601.92 hrs</td><td>10.35 M</td><td>CommonVoice</td></tr><tr><td>FR</td><td>732.02 hrs</td><td>5.03 M</td><td>CommonVoice</td></tr><tr><td>SP</td><td>408.34 hrs</td><td>2.79 M</td><td>CommonVoice</td></tr><tr><td>TUN-FR</td><td>10.89 hrs</td><td>70.86 K</td><td>TunSwitch</td></tr><tr><td>UAE-EN</td><td>8.97 hrs</td><td>57.82 K</td><td>Mixat</td></tr><tr><td>EGY-EN</td><td>5.61 hrs</td><td>52.00 K</td><td>ArzEn</td></tr></table>
+
+Table 10: Additional datasets used to train the joint multilingual model.  
+
+<table><tr><td>Adaptation data</td><td>-</td><td>-</td><td>-</td><td>MSA</td><td>Dialectal</td><td>Multilingual</td></tr><tr><td>Test Set</td><td>v1</td><td>v2</td><td>v3</td><td>v1</td><td>v2</td><td>v3</td></tr><tr><td>MGB2</td><td>13.42</td><td>12.5</td><td>13.0</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ArzEn</td><td>43.21</td><td>77.59</td><td>35.26</td><td>34.85</td><td>33.71</td><td>27.43</td></tr><tr><td>TunSwitch</td><td>53.85</td><td>101.94</td><td>40.68</td><td>43.87</td><td>43.59</td><td>36.66</td></tr><tr><td>Mixat</td><td>42.50</td><td>92.41</td><td>34.27</td><td>27.07</td><td>25.73</td><td>21.66</td></tr></table>
+
+Table 11: ASR Results using the various checkpoints: v1, v2 and v3. We compare models trained directly from the pretrained checkpoint vs. starting with an ASR checkpoint trained with the specified adaptation data: MSA adaptation using the MGB2 dataset; Dialectal adaptation using data listed in Table 6; Multilingual adaptation using data from Table 6 and Table 10.
+
+solute reductions in WER for all test sets. This clearly illustrates the advantage of multi-lingual pretraining in code-switching scenarios. We also evaluated models initialized from the joint models followed by target fine-tuning on the CS train sets, and this reduced error rates further. The best performing model is the joint multilingual v3 mode, with 4 to  $7\%$  absolute reduction in WER compared to the second best model. We show examples of ASR outputs from the three CS datasets using the various models in Figure 6 in the Appendix.
+
+Language Interference: We test the effect of multi-lingual pre-training on MSA performance. Language interference is known to negatively affect monolingual performance (Toyin et al., 2023), so we test our multilingual model on the MGB2 benchmark to quantify this effect (see Table 11). The model achieves  $13.0\%$  WER, which is indeed worse than the SOTA result we achieve with the Arabic-only model (see Table 2), but the difference at  $0.5\%$  absolute WER is rather small. When it comes to dialects, however, we find that language interference has a significant negative effect, resulting in  $4\%$  to  $16\%$  absolute increase in error rates, as shown in Figure 4.
+
+# 9 Conclusions
+
+We presented the largest study on dialectal Arabic ASR to empirically demonstrate the effect of
+
+![](images/3d975f379412c42aabd71d096d890019bd94db6d906653aa9d32cc325cf2bb8b.jpg)  
+Figure 4: WER (\%) and absolute difference on a subset of dialects, comparing our joint dialectal fine-tuning vs. joint multi-lingual fine-tuning on Arabic dialects.
+
+various training paradigms on ASR performance. We compared models pre-trained with and without dialects, in high, low, and medium-resource settings, in addition to zero-shot. We find that overall, dialectal pre-training improves performance in zero-shot and low-resource cases, and mostly maintains performance on MSA and high-resource dialects. We also find that all dialects benefit from adaptation of models pre-fine-tuned on MSA, and this effect is most noticeable for low and medium-resource dialects. We experimented with multi-dialectal fine-tuning, where we joined the train sets of 12 dialects. We observe performance improvements on average, and at least the same performance as the target-dialect fine-tuning setting, and the best performance on held-out dialects. Interestingly, while using dialect ID in decoding is effective, forcing the dialect ID results in worse performance compared to dialect inference. While joint training results in improved performance for the medium and low-resource dialects, target-dialect fine-tuning is more effective for high-resource dialects. Finally, we experimented with multi-lingual pre-training and fine-tuning for improving performance on code-switched utterances, and achieved significant reductions in error rates on all available test sets. However, reductions in monolingual performance were also observed due to language interference, particularly for dialects, were WER increased by  $4\%$  to  $16\%$  for some dialects. To enable easier adoption and further experiments, we released the pretrained dialectal and multilingual checkpoints, the fine-tuned MGB2 models, and the joint dialectal and multilingual ASR models.
+
+# Limitations
+
+One of the limitations in dialect-related work is the coarse classification of dialect IDs; dialects in our datasets are classified by regions or countries, whereas actual dialectal variations are far more fine-grained. For example, the Saudi dataset, SADA, covers a large geographical area and many dialects, but it is considered as one dialect based on our classification. Moreover, the way the datasets are collected do not guarantee that the data are indeed dialectal. For instance, with manual inspection of the Syrian test and dev sets from MASC, we observed that all instances are in MSA rather than Syrian dialects. In addition, Arabic dialects are spoken varieties that do not have a standard spelling system. This results in large variations in transcriptions, but standard WER does not account for these variations, resulting in more pessimistic results. With the exception of the MGB3 and MGB5 benchmarks where we report average and multi-reference WER across 4 references, all datasets have only a single reference.
+
+# References
+
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+
+# A Pre-Training Dataset Statistics
+
+Table 12 shows the complete list of datasets used for pre-training v1, v2, and v3.
+
+<table><tr><td>Dataset</td><td>Dialect</td><td>Hours</td><td>Words</td><td>v1</td><td>v2</td><td>v3</td></tr><tr><td>ASC</td><td>MSA</td><td>3.7 hrs</td><td>20.58 K</td><td></td><td>✓</td><td>✓</td></tr><tr><td>ArzEn[cs]</td><td>EGY</td><td>5.61 hrs</td><td>52.00 K</td><td></td><td></td><td>✓</td></tr><tr><td>CommonVoice</td><td>Dialect Mix</td><td>133.24 hrs</td><td>494.83 K</td><td></td><td>✓</td><td>✓</td></tr><tr><td rowspan="3">CommonVoice</td><td>ENG</td><td>1601.92</td><td>10.35 M</td><td></td><td></td><td>✓</td></tr><tr><td>FR</td><td>732.02</td><td>5.03 M</td><td></td><td></td><td>✓</td></tr><tr><td>ES</td><td>408.34</td><td>2.79 M</td><td></td><td></td><td>✓</td></tr><tr><td>ClArTTS</td><td>CA</td><td>12 hrs</td><td>76.31 K</td><td></td><td>✓</td><td>✓</td></tr><tr><td rowspan="12">MASC</td><td>EGY</td><td>175.36 hrs</td><td>1.03 M</td><td></td><td></td><td>✓</td></tr><tr><td>IRA</td><td>17.37 hrs</td><td>121.12 K</td><td></td><td></td><td>✓</td></tr><tr><td>JOR</td><td>42.21 hrs</td><td>330.83 K</td><td></td><td></td><td>✓</td></tr><tr><td>KUW</td><td>4.04 hrs</td><td>32.37 K</td><td></td><td></td><td>✓</td></tr><tr><td>LEB</td><td>25.20 hrs</td><td>155.76 K</td><td></td><td></td><td>✓</td></tr><tr><td>MOR</td><td>8.60 hrs</td><td>58.38 K</td><td></td><td></td><td>✓</td></tr><tr><td>MSA</td><td>612.28 hrs</td><td>3.80 M</td><td></td><td></td><td>✓</td></tr><tr><td>PAL</td><td>6.17 hrs</td><td>45.35 K</td><td></td><td></td><td>✓</td></tr><tr><td>SAU</td><td>452.24 hrs</td><td>301.92 K</td><td></td><td></td><td>✓</td></tr><tr><td>SYR</td><td>211.33 hrs</td><td>1.06 M</td><td></td><td></td><td>✓</td></tr><tr><td>TUN</td><td>12.17 hrs</td><td>34.34 K</td><td></td><td></td><td>✓</td></tr><tr><td>UAE</td><td>9.87 hrs</td><td>6.42 K</td><td></td><td></td><td>✓</td></tr><tr><td>MGB2</td><td>Mostly MSA</td><td>1000 hrs</td><td>7.31 M</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>QASR</td><td>Mostly MSA</td><td>2000 hrs</td><td>13.33 M</td><td></td><td>✓</td><td></td></tr><tr><td>MGB3[ASR]</td><td>EGY</td><td>2.83 hrs</td><td>18.93 K</td><td></td><td>✓</td><td></td></tr><tr><td rowspan="5">MGB3[ADI]</td><td>EGY</td><td>11.15 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>GLF</td><td>8.92 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>LAV</td><td>9.27 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>MSA</td><td>9.39 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>NOR</td><td>9.49 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>MGB5[ASR]</td><td>MOR</td><td>115.7hrs</td><td>—</td><td></td><td>✓</td><td></td></tr><tr><td rowspan="17">MGB5[ADI]</td><td>ALG</td><td>115.7hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>EGY</td><td>451.1 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>IRA</td><td>815.8 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>JOR</td><td>25.9 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>KSA</td><td>186.1 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>KUW</td><td>108.2 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>LEB</td><td>116.8 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>LIB</td><td>127.4 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>MAU</td><td>456.4 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>MOR</td><td>57.8 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>OMA</td><td>58.5 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>PAL</td><td>121.4 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>QAT</td><td>62.3 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>SUD</td><td>47.7 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>SYR</td><td>119.5 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>UAE</td><td>108.4 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>YEM</td><td>53.4 hrs</td><td>—</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Mixat[cs]</td><td>UAE</td><td>9.97 hrs</td><td>57.82 K</td><td></td><td></td><td>✓</td></tr><tr><td rowspan="4">ParallelCorp</td><td>EGY</td><td>32 hrs</td><td>48.56 K</td><td></td><td></td><td>✓</td></tr><tr><td>GLF</td><td>32 hrs</td><td>27.26 K</td><td></td><td></td><td>✓</td></tr><tr><td>LEV</td><td>32 hrs</td><td>18.43 K</td><td></td><td></td><td>✓</td></tr><tr><td>MSA</td><td>32 hrs</td><td>30.66 K</td><td></td><td></td><td>✓</td></tr><tr><td rowspan="3">MADAR</td><td>MOR ALG TUN</td><td rowspan="3">—</td><td rowspan="3">532.37K</td><td></td><td rowspan="3">✓</td><td rowspan="3">✓</td></tr><tr><td>LIB EGY LEV</td><td></td></tr><tr><td>IRA GLF YEM</td><td></td></tr><tr><td rowspan="6">NADI</td><td>ALG BAH EGY</td><td rowspan="6">—</td><td rowspan="6">702.67K</td><td></td><td rowspan="6">✓</td><td rowspan="6">✓</td></tr><tr><td>IRA JOR KUW</td><td></td></tr><tr><td>LEB LIB MOR</td><td></td></tr><tr><td>OMN PAL QAT</td><td></td></tr><tr><td>SAU SUD SYR</td><td></td></tr><tr><td>TUN UAE YEM</td><td></td></tr><tr><td>SADA</td><td>SAU</td><td>417.63 hrs</td><td>3.26M</td><td></td><td></td><td>✓</td></tr><tr><td>TARIC-SLU</td><td>TUN</td><td>6.81 hrs</td><td>53.48K</td><td></td><td></td><td>✓</td></tr><tr><td>TunSwitch[cs]</td><td>TUN</td><td>10.89 hrs</td><td>70.86K</td><td></td><td></td><td>✓</td></tr><tr><td>VoxBlink</td><td>Dialect Mix</td><td>19.92 hrs</td><td>—</td><td></td><td></td><td>✓</td></tr></table>
+
+# B Inference examples
+
+Figure 5 lists examples of ASR outputs using the dialect-specific fine-tuned models. Note that the 'errors' in SAU, EGY, and JOR examples are in fact alternative spellings.
+
+Table 12: Summary of Dataset Statistics for Pre-Training: Hours of Audio, Word Counts, and Associated Dialects.  $[cs]$  refers to Code Switching datasets.  $[txt]$  refers to textual datasets.  
+
+<table><tr><td>الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستعمال
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+الإستUTION
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en
+ال,en</td></tr><tr><td>(SAU)</td></tr><tr><td>(SYR)</td></tr><tr><td>(EGY)</td></tr><tr><td>(JOR)</td></tr><tr><td>(LEB)</td></tr><tr><td>(IRA)</td></tr><tr><td>(TUN)</td></tr><tr><td>(MOR)</td></tr><tr><td>(PAL)</td></tr><tr><td>(KUW)</td></tr></table>
+
+Figure 5: Examples of dialectal recognition after targeted fine-tuning, following MGB2 adaptation.
+
+Figure 6 shows example outputs from each model on the code-switching datasets: ArzEn (Egyptian-English), TunSwitch (Tunisian-French), and Mixat (Emirati-English).
+
+# C Cross-Dialectal Performance
+
+Table 13 shows the cross-dialectal performance, where models trained on a single target dialect are tested on other dialects, including the three held-out sets: ALG, SUD, and YEM. In most cases, the best performance is achieved by the model trained on the same target dialect (the diagonal in Table 13). However, for low-resource dialects, like KUW and PAL, the model trained on SAU achieved the lowest WER. This is likely a result of the large size of the SAU train set and the wide geographical area and dialectal variants it covers. Curiously, all models perform well on the SYR test set; upon close inspection, we found that the set consists mostly of MSA utterances, which explains the result since all models are adapted from MSA.
+
+# ArzEn
+
+pre-training
+
+ARTSTV1: 'IglaLo IgIgauan intervies a a a Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn Jn J
+
+ArTSTV2:1gjyull g0uulljzuluea uulnssuul yolouo' 5j aalee buiu oaaee
+
+ARTSTV3: 'IgLaag yIgIglaa interviews aI gIgLaIgLaIgLaIgLaIgLaIg' personality JI galalgKgagalalagJll Iglalg' 1st alol cagccl
+
+adaptation
+
+ARTSTV1: Iglas y Iglas interveic<unk> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 'personality jll gals lgalsgalsa galsa aull wll gaj   
+'jla alale aag pecilc
+
+ARSTv2: 'Igaa aIgaii interviews aJbJbJcIaIgaa IaIa JbJbJcI'   
+'Agcty presonality,JI aIgaiIgaiIgaiIgaiIgaiIgaiIgai
+
+ARTSTv3: 'Iglaas a IgSiaa interviews a 12 jnuuue uauuua'   
+'personality.jpalao Igalac g agalabu uU uU Ugay   
+'slal aloc uauuus
+
+Reference: 'Igaaas IgSaai interviews aajn lca Igaa laa'   
+personality jpalagai gaaalaylll wll lgaye'
+
+# TunSwitch
+
+pre-training
+
+ArTSTv1:  $\left\{ \begin{array}{l} \text{a} \leqslant \text{c} \\ \text{g} \leqslant \text{g} \end{array} \right.$  <unk>forme g a <unk>aire
+
+ArTSTv2:  $\mathcal{A}$  000000000000000000000000000000000000000
+
+ArTStv3:  $\omega_{\mathrm{d}^{\prime}\mathrm{d}^{\prime}}$  des formes g a daire  $\omega_{\mathrm{g}}$  Lg
+
+adaptation
+
+ArTSTv1: \(\{a\omega ,c\omega ,d\omega ,g\omega ,h\omega ,l\omega ,e\omega ,f\omega ,g\omega ,h\omega ,l\omega ,e\omega ,f\omega ,g\omega ,h\omega ,l\omega ,e\omega ,f\omega ,g\omega ,h\omega ,l\omega ,e\omega ,f\omega ,g\omega ,h\omega ,l\omega ,e\omega ,f\omega ,g\omega ,h\omega ,l\omega ,e\omega ,
+
+ArTSTv2:  $\omega_{\mathrm{g}}$  des formes g adaire  $\omega_{\mathrm{g}}$  Log
+
+ArTSTv3:"gds gds des formes g dailleur Log
+
+Reference: 15.2.3.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.
+
+# Mixat
+
+pre-training
+
+ArTSTV1: 'J Jnnn nn nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
+
+ArTSTv2: 100000000000000000000000000000000000000000000000000000000000000
+
+ArTSTv3:  $\mathbb{J}$  JI JSSS SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
+
+adaptation
+
+ArTSTv1:2gJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJgJg
+
+ArTSTv2: 's w q aq s q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qu 1
+
+ArTStv3: 'jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai jai
+
+Reference: 'level two level three level four' 'level three'
+
+Figure 6: Examples of code-switching samples from each of the code-switching dataset using (a) pre-training checkpoints fine-tuned on CS data directly, and (2) adapted models after fine-tuning on general data: v1 (MGB2), v2 (Dialectal data from Table 6), and v3 (dialectal data from Table 6 + Multilingual and CS data from Table 10).
+
+<table><tr><td rowspan="2"></td><td rowspan="2">Dialect</td><td colspan="11">Fine-Tuning Set</td></tr><tr><td>SAU(%)</td><td>SYR(%)</td><td>EGY(%)</td><td>JOR(%)</td><td>LEB(%)</td><td>IRA(%)</td><td>TUN(%)</td><td>MOR(%)</td><td>PAL(%)</td><td>KUW(%)</td><td>UAE(%)</td></tr><tr><td rowspan="10">test set</td><td>SAU</td><td>27.33</td><td>53.88</td><td>63.24</td><td>43.95</td><td>56.64</td><td>46.10</td><td>49.36</td><td>50.02</td><td>45.48</td><td>46.85</td><td>46.14</td></tr><tr><td>SYR</td><td>19.54</td><td>17.42</td><td>23.91</td><td>16.88</td><td>25.71</td><td>16.85</td><td>24.36</td><td>17.58</td><td>16.86</td><td>17.38</td><td>17.53</td></tr><tr><td>EGY</td><td>38.07</td><td>53.43</td><td>36.43</td><td>40.08</td><td>51.61</td><td>41.37</td><td>45.58</td><td>43.88</td><td>40.41</td><td>41.88</td><td>40.38</td></tr><tr><td>JOR</td><td>22.88</td><td>30.37</td><td>34.14</td><td>21.08</td><td>28.11</td><td>28.68</td><td>32.96</td><td>30.50</td><td>25.25</td><td>27.88</td><td>28.05</td></tr><tr><td>LEB</td><td>41.03</td><td>42.53</td><td>53.23</td><td>39.07</td><td>28.05</td><td>41.23</td><td>48.53</td><td>42.34</td><td>41.55</td><td>42.14</td><td>40.97</td></tr><tr><td>IRA</td><td>35.18</td><td>49.10</td><td>56.91</td><td>40.47</td><td>46.72</td><td>36.10</td><td>47.84</td><td>42.55</td><td>40.76</td><td>41.00</td><td>42.16</td></tr><tr><td>TUN</td><td>44.44</td><td>57.78</td><td>51.85</td><td>46.67</td><td>45.19</td><td>45.19</td><td>26.67</td><td>44.44</td><td>47.41</td><td>46.67</td><td>47.41</td></tr><tr><td>MOR</td><td>59.04</td><td>71.69</td><td>74.70</td><td>54.82</td><td>69.88</td><td>57.23</td><td>74.70</td><td>56.63</td><td>56.02</td><td>59.64</td><td>57.83</td></tr><tr><td>PAL</td><td>48.53</td><td>66.18</td><td>62.65</td><td>52.35</td><td>60.59</td><td>58.53</td><td>60.00</td><td>63.24</td><td>53.53</td><td>58.53</td><td>60.59</td></tr><tr><td>KUW</td><td>26.59</td><td>59.54</td><td>78.03</td><td>43.93</td><td>67.63</td><td>46.82</td><td>53.18</td><td>51.45</td><td>44.51</td><td>46.24</td><td>50.87</td></tr><tr><td rowspan="3">held-out</td><td>ALG</td><td>50.21</td><td>60.09</td><td>73.61</td><td>52.58</td><td>62.02</td><td>57.51</td><td>57.51</td><td>59.01</td><td>52.79</td><td>58.37</td><td>57.30</td></tr><tr><td>SUD</td><td>40.89</td><td>64.97</td><td>64.32</td><td>53.15</td><td>64.86</td><td>54.12</td><td>56.51</td><td>58.79</td><td>52.39</td><td>56.18</td><td>55.53</td></tr><tr><td>YEM</td><td>38.40</td><td>42.16</td><td>38.49</td><td>34.68</td><td>39.69</td><td>34.92</td><td>30.68</td><td>35.73</td><td>33.06</td><td>34.68</td><td>34.11</td></tr></table>
+
+Table 13:  $\mathrm{{WER}}\left( \% \right)$  for various models on unseen dialects. All models are adapted from v2  $\rightarrow$  MGB2.

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2025/Dialogue-RAG_ Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting/full.md

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+# Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting
+
+Qiwei  $\mathsf{Li}^{1\dagger}$ , Teng Xiao $^{1\dagger}$ , Zuchao Li $^{1*}$ , Ping Wang $^{2}$ , Mengjia Shen $^{3}$ , Hai Zhao $^{4}$
+
+$^{1}$ School of Computer Science, Wuhan University, Wuhan, China,
+
+$^{2}$ School of Information Management, Wuhan University, Wuhan, China,
+
+<sup>3</sup>Wuhan Second Ship Design and Research Institute, Wuhan, China,
+
+$^{4}$ School of Computer Science, Shanghai Jiao Tong University, Shanghai, China {qw-line, xiaoxiao, zcli-charlie, wangping}@whu.edu.cn, shenmj13@163.com, zhaohai@cs.sjtu.edu.cn
+
+# Abstract
+
+Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. However, ellipses and coreferences, as common phenomena in dialogue scenes, pose challenges to LLMs' understanding and RAG's retrieval accuracy. The previous works ignore the negative impact of this fuzzy data on RAG system. We explore the capabilities of LLMs and RAG systems in dialogue scenarios and use Incomplete Utterance Rewriting (IUR) to complete the key information in dialogue to enhance retrieval. Besides, we propose a lightweight IUR model for query rewriting. It is an end-to-end framework for node linking and iterative inference, incorporating two newly proposed probing semantic features derived from generative pre-training. This framework treats IUR as a series of link decisions on the input sequence and the incrementally constructed rewriting outputs. To test the performance of RAG system in the model multi-round dialogue scenario, we construct an RAG dialogue dataset on English and Chinese, Dialogue-RAG-MULTI-v1.0. Experiment results show that utterance rewriting can effectively improve the retrieval and generation ability of RAG system in dialogue scenes. Experiments on IUR tasks demonstrate the excellent performance of our lightweight IUR method.
+
+# 1 Introduction
+
+Human-machine interactions are ubiquitous in today's international web age. With continuous technological advancements, especially in deep learning, machines have gained the ability to understand human speech and language (Dongbo et al., 2023).
+
+![](images/40a311cc8ad29db0d6d113a703bc38e10364b9f692391658a1d6e71db37a6e16.jpg)  
+Figure 1: Two examples in human-machine dialogue. The question "How about Portuguese?" in Example (a) omits the noun "food". The pronoun "he" in the second question of Example (b) is a reference to "Jon Hippe".
+
+Recently, the emergence of Large Language Models (LLMs) (DeepSeek-AI, 2024; Yang et al., 2024; GLM et al., 2024; Li et al., 2023c; Yao et al., 2024) and Retrieval Augmented Generation (RAG) (Ram et al., 2023; Asai et al., 2023a; Shi et al., 2023; Gao et al., 2023a; Asai et al., 2023b; Cuconasu et al., 2024) technologies has brought new opportunities for the development of human-computer dialogue.
+
+Retrieval Augmented Generation is a technique that enhances text generation by utilizing information from additional data sources. In the context of LLMs, RAG technology has demonstrated outstanding performance in improving generation tasks. On one hand, models can use retrieved information to assist in generation, enhancing the quality and relevance of the generated text. On the other hand, RAG helps models acquire external knowledge and information, significantly expanding application scenarios of large language models. (Gao et al., 2023b; Gupta et al., 2024)
+
+However, in real-world human-machine interaction scenarios, ellipses and coreferences occur quite frequently. Figure 1 illustrates an example of this situation. In dialogue tasks, the ambiguous information can hinder the ability of LLMs to accurately understand the semantics of user queries. In a RAG system, this ambiguity also impacts the retrieval of key information. The inaccurate retrieval results will lead to a decline in the generation per
+
+formance of LLMs.
+
+To address this limitation, we propose a retrieval enhancement method for RAG, Dialogue-RAG, based on Incomplete Utterance Rewriting (IUR). It completes utterance using IUR, and realizes accurate RAG retrieval and model generation by more complete query. Besides, a novel lightweight IUR model is designed to complete the sentence information to improve the quality of the search results, and then improve the question-answering ability of the generated model. To evaluate the ability of RAG in the dialogue scenario, we propose an RAG dialogue dataset on English and Chinese, which constructs a question answering pair in the scenario of knowledge dialogue and a knowledge base for retrieving related information. This can effectively test the knowledge retrieval and question answering ability of RAG method in dialogue scenario.
+
+Our main contributions are as follows:
+
+- We explore the dialogue task scenario and design a new retrieval enhancement method for RAG based on IUR. We build an enhanced RAG system based on this approach (Dialogue-RAG) and it can achieve accurate retrieval of knowledge base by rewriting and completing incomplete data. Furthermore, it effectively improve the model's ability to answer fuzzy questions in real scenarios.  
+- We design a lightweight IUR model that transforms the IUR task into a digraph parsing task and uses a node linking parser and generative pre-trained language model to realize fast digraph parsing decision-making in the IUR tasks.  
+- We manually construct a retrieval-augmented generation dialogue dataset on English and Chinese, which contains multiple rounds of question answering pairs and a knowledge base for retrieval. It can not only test the dialogue ability of the model, but also evaluate the effect of RAG method on the enhancement of dialogue ability.
+
+# 2 Related Work
+
+# 2.1 Retrieval Augmented Generation
+
+Retrieval-Augmented Generation enhances language models by integrating retrieved text passages, significantly improving performance on knowledge-intensive tasks. Initial work by (Guu
+
+et al., 2020) and (Lewis et al., 2020) combined dense passage retrieval with sequence-to-sequence models, showing notable performance gains (Ram et al., 2023). Recent advancements include (Luo et al., 2023), who instruction-tuned models with fixed retrieved passages and pre-trained retrievers and models together. (Jiang et al., 2023) proposed adaptive retrieval during generation, and Toolformer (Schick et al., 2024) trained models to generate API calls. However, these approaches face challenges in efficiency, handling irrelevant context, and attribution accuracy (Mallen et al., 2023; Shi et al., 2023; Liu et al., 2023a). Self-RAG (Asai et al., 2023b) addresses these issues by allowing models to use retrieval on demand for diverse queries to some extent. (Cuonasu et al., 2024) studied the impact of several key factors, like the type, number, and position of documents that should augment the prompt to the LLM. However, the existing benchmarks ignore the negative effects of ellipsis and coreference on knowledge retrieval.
+
+# 2.2 Incomplete Utterance Rewriting
+
+Early works on Incomplete Utterance Rewriting primarily employ sequence-to-sequence models combined with a copy mechanism to extract relevant information from the context (Su et al., 2019; Elgohary et al., 2019; Kumar and Joshi, 2017). These methods use pointer networks or sequence generation models to extract information and address basic rewriting issues. With the introduction of pre-trained models, Incomplete Utterance Rewriting (IUR) research has significantly advanced. For example, RUN (Liu et al., 2020a) proposes a rewriting matrix based on context and utterance embeddings, which determines whether insertion or replacement operations should be carried out by annotating each token. Additionally, SRL (Xu et al., 2020) pre-identifies the subject, predicate, and object within the context and incorporates these as extra features into the encoder, improving performance. RaST (Hao et al., 2021) models the IUR problem as a sequence labeling task, implementing rewriting by predicting actions (insertion, deletion, and None) and the spans of the context separately. HCT (Jin et al., 2022) further enhances the action predictor from previous work to a rule predictor, optimizing the generation of modified words not explicitly present in the context. Recently, (Li et al., 2023b) improve IUR performance by continued pre-training of the T5 (Raffel et al., 2020) model. Meanwhile, (Du et al., 2023) and (Li et al., 2023a)
+
+further develop fine-grained subtasks and refined model architectures based on the edit matrix. Considering that the context of the dialogue contains similar content that tends to cause confusion and the contents of the different themes are numerous and complex, (Guo et al., 2024) design the Dynamic Context Introduction mechanism to filter out irrelevant contexts to handle the extended multi-turn dialogue. XSS (Peng et al., 2024) designs the Cross Scorer Sharing mechanism to support efficient pair locating for IUR, enabling faster and more accurate processing of extended dialogue contexts.
+
+# 3 Methods
+
+# 3.1 Dialogue Retrieval Augmented Generation
+
+To effectively deal with the degradation of retrieval quality caused by incomplete utterance, we design a Dialogue Retrieval Augmented Generation (Dialogue-RAG) method. The overall architecture of the method is shown in Figure 2. We rewrite the input question according to the history context and complete the missing parts. In RAG processing, the completed information is used for corresponding information retrieval and question answering. In this way, more accurate information retrieval and high-quality question answers are achieved.
+
+Specifically, suppose the multi-turn dialogue utterances are  $U = \{u^{1}, u^{2}, \dots, u^{n}\}$ . We use our new lightweight IUR model rewrite utterance  $u^{i}$  to a self-contained version  $Ru^{i}$  with context  $U_{c} = \{u^{1}, u^{2}, \dots, u^{i-1}\}$ , where  $u^{i} = \{w_{1}^{i}, w_{2}^{i}, \dots, w_{m}^{i}\}$  is the  $i$ -th utterance with  $m$  words in dialogue. We define the procedure as follows:
+
+$$
+R u ^ {i} = I U R (\{u ^ {1}, u ^ {2}, \dots , u ^ {i - 1} \}, u ^ {i}), \quad (1)
+$$
+
+where  $IUR(\cdot)$  is the rewriting process. Details are provided in Subsection 3.2 of this chapter. Based on  $Ru^i$ , we match key information through similarity. For knowledge base information  $V = \{v_1, v_2, \dots, v_l\}$ ,  $v_i$  represents the chunk vector in the knowledge base. We use cosine similarity to calculate the similarity  $s_{i,j}$  between  $Ru^i$  and  $v_j$ :
+
+$$
+s _ {i, j} = \frac {\mathbf {R} \mathbf {u} ^ {i} \cdot \mathbf {v} _ {j}}{\| \mathbf {R} \mathbf {u} ^ {i} \| \| \mathbf {v} _ {j} \|.} \tag {2}
+$$
+
+We reorder chunks according to the score  $s_{i,j}$  and select the  $Top - k$  chunks as the matching information  $T_{m} = \{t_{1}, t_{2}, \dots, t_{k}\}$  for  $Ru^{i}$ . The  $t_{j} \in T_{m}$
+
+represents the chunk text corresponding to the chunk vector  $v_{j}$ . As shown in Figure 2, matched chunk information  $T_{m}$  and rewritten utterance  $Ru^{i}$  will be fused into the prompt template as a context prompt  $P(T_{m},Ru^{i})$  and it will be used as input to the large language model for the dialogue answer  $a_{j}$ :
+
+$$
+a _ {i} = \operatorname {L L M} \left(P \left(T _ {m}, R u ^ {i}\right)\right), \tag {3}
+$$
+
+where  $\mathrm{LLM}(\cdot)$  is the large language model.
+
+# 3.2 Node Linking Iterative Inference
+
+To meet the retrieval requirements of RAG, we design a lightweight IUR model. Following the practice of previous text editing works (Liu et al., 2020a; Zhang et al., 2022), context utterances  $U_{c}$  are concatenated into a single word sequence  $S_{c}$  with a special token [SEP] as the utterance separator. In this part, there are three components to our model: a node linking parser, a contextual similarity feature (CSF) extractor, and a rewrite consistency feature (RCF) extractor. The CSF and RCF feature extractors are GPT model-based probes to enhance the interactive inference in IUR.
+
+Digraph Modeling. For IUR, it mainly uses the determined phrase from the context and inserts it into a certain position of the incomplete utterance. We transform it into a digraph modeling task and regard the beginning and ending words of the phrase span in the context and the words on the left of the insertion position in the incomplete utterance as nodes. The edge between two nodes acts as a link. We define two types of links: span link and insertion link. The first is used to determine the span of the phrase to be inserted in the context, while the second is used to determine the insertion position in the incomplete utterance.
+
+It is worth noting that although insertion can solve the vast majority of rewriting problems, there are still some words in the rewritten sentence that don't come from the context. Since these words do not depend on context, an additional model such as grammatical error correction can be used for post-processing. However, this phenomenon is not the focus of this paper and is therefore not considered in our digraph modeling transformation.
+
+Digraph modeling can effectively simplify the IUR problem. Since for a context of length  $m$ , an incomplete utterance of  $n$ , the complexity of the insertion operation is  $O(m \times n)$  and the complexity for determining the insertion span is  $O(m \times m)$ , so the overall complexity is  $O(m \times m \times n)$ . Digraph
+
+![](images/c0fc12fac5c95192e131497b4b3740b1f5727f42a41cb7f951ef09e764cc7bf5.jpg)  
+Figure 2: Overall architecture.
+
+modeling decomposes it into two steps of digraph parsing, determining the insertion point has complexity  $O(m \times n)$ , and determining the span has complexity  $O(m \times m)$ , so the overall implementation complexity is  $O(m \times n) + O(m \times m)$ , which greatly reduces inference difficulty. In the actual implementation, the two-step link inference is finished at one time, so the final complexity is reduced to  $O\big((m + n) \times (m + n)\big)$ .
+
+Node Linking Parser. Based on digraph modeling, we propose to use a node linking parser to learn the digraphs. The overall structure of the parser is shown on the left side of Figure 2. Formally, for an input sequence  $X = \{w_{1}, w_{2}, \dots, w_{n}\}$  with length  $n$  (which is a concatenation of context and incomplete utterances), we insert a special token BOS at the beginning of the sequence. We employ a sequence encoder to convert the input sentence into contextualized vector representations  $H$ .
+
+In order to distinguish the tokens in the sequence as the head node and the tail node of the link, we introduce two independent feed-forward neural (FFN) layers to project the representations from the sequence encoder to different spaces respectively:
+
+$$
+H ^ {\left(\text {h e a d}\right)} = \operatorname {F F N} _ {\text {h e a d}} (H), \tag {4}
+$$
+
+$$
+H ^ {(t a i l)} = \mathrm {F F N} _ {\text {t a i l}} (H).
+$$
+
+To capture the link relationship between the head node and the tail node, we introduce biaffine attenuate
+
+tion scorer (Dozat, 2016) to score the relationship between the head-tail node pair.
+
+$$
+s (i \xrightarrow {l} j) = H _ {i} ^ {(h e a d) \top} W H _ {j} ^ {(t a i l)} + b, \tag {5}
+$$
+
+where  $i$  and  $j$  indicate the index of the head and tail node in the sequence respectively,  $W$  and  $b$  are learnable parameters, and  $l$  is the relation label of link  $i \rightarrow j$ . A multi-class cross-entropy loss is applied to the biaffine score  $s(i \xrightarrow{l} j)$  to guide the model to learn node linking parsing.
+
+$$
+\mathcal {L} = \sum_ {i, j} X E n t (s (i \xrightarrow {l} j), r (i \rightarrow j)), \tag {6}
+$$
+
+where  $r(i\to j)$  indicates the gold relation label between node  $i$  and  $j$ , and NONE is used to indicate that there is no relationship between nodes.
+
+Iterative Inference. The foundation of our proposed two probing features is generative language modeling. Its modeling objective for the input language sequence  $X = \{x_{1}, x_{2}, \dots, x_{L}\}$  is to predict each token  $x_{i}$  using the unidirectional visible token sequence  $X_{<i}$  and maximize the following maximum likelihood so that the model can learn the language generation knowledge in an unsupervised way. GPT (Liu et al., 2023b), which is widely used in natural language generation and other fields, is one of many models that have been pre-trained with generative language modeling, thus becoming the
+
+basis for our probing feature. Formally, the probability of distribution for the next token prediction in GPT predicting is given by:
+
+$$
+P _ {\mathcal {G}} (y) = \mathbf {G P T} (x _ {0}, \dots , x _ {i - 1}). \tag {7}
+$$
+
+where  $y$  represents the next predicted token.
+
+# a. Contextual Similarity Feature.
+
+In the IUR inference process, assuming incomplete utterance rewriting depends on the phrase  $u_{k:k+lambda} = \{w_k^u, w_{k+1}^u, \ldots, w_{k+\lambda}^u\}$  in the context utterance  $u$ .  $u_{k:k+lambda}$  is a complement to incomplete utterance, and the complement insertion position is  $p$ . From the perspective of generative language modeling, the generation context of phrase  $u_{k:k+lambda}$  in original utterance is  $u_{<k} = \{w_1^u, w_2^u, \ldots, w_{k-1}^u\}$  while the generation context at the insertion position  $p$  incomplete utterance is  $iu_{<p}$ . Since two generation context aims at generating the same phrase  $u_{k:k+lambda}$ , we refer to this case as contextual similarity assumption. That is, by the context  $u_{<k}$  and  $iu_{<p}$  has similarity in the distribution of the next token prediction.
+
+Formally, for the generation context  $u_{<k}^i$  and  $i u_{<p}$ , GPT model output the next token distribution respectively as:
+
+$$
+P _ {\mathcal {G}} (y) ^ {u} = \mathbf {G P T} (w _ {1} ^ {u}, w _ {2} ^ {u},..., w _ {k - 1} ^ {u}),
+$$
+
+$$
+P _ {\mathcal {G}} (y) ^ {i u} = \mathbf {G P T} \left(w _ {1} ^ {i u}, w _ {2} ^ {i u}, \dots , w _ {p - 1} ^ {i u}\right). \tag {8}
+$$
+
+According to the contextual similarity assumption, the divergence between the two distributions should be smaller if the token from the  $k$ -th position of the utterance  $u$  can be inserted into the position  $p$  of the incomplete utterance  $iu$ . In other words, the distribution divergence measures the extent to which a token at a specific position in the utterance can be inserted into the incomplete utterance in IUR. Thus, the contextual similarity feature (CSF) can be written as:
+
+$$
+\begin{array}{l} C S F = D _ {K L} \left(P _ {\mathcal {G}} (y) ^ {i u} | | P _ {\mathcal {G}} (y) ^ {u}\right) \\ = \sum P _ {\mathcal {G}} (y) ^ {i u} \log \frac {P _ {\mathcal {G}} (y) ^ {i u}}{P _ {\mathcal {G}} (y) ^ {u}}. \tag {9} \\ \end{array}
+$$
+
+# b. Rewrite Consistency Feature.
+
+Apart from context similarity feature, for the typical scenario of incomplete utterance rewriting, inserting the phrase  $u_{k:k + \lambda} = \{w_k^u,w_{k + 1}^u,\dots,w_{k + \lambda}^u\}$  started with position  $k$  in the context into the position  $p$  of the incomplete utterance should make the new utterance a reasonable sentence. In other words, the partial sequence of the incomplete utterance  $i u_{\leq p}$  and the inserted word  $u_{k}$  will form
+
+a new valid sentence fragment. We refer to such phenomenon as rewriting consistency.
+
+Based on this phenomenon, we deduce a rewrite consistency feature (RCF) to enhance the determination of completion words and insertion positions in IUR. Since GPT's pre-training goal is to predict the next token, we can evaluate the probability of this prediction as the likelihood that the token and its context form a valid segment. In IUR, we adopt the probability value of a given token from GPT prediction distribution as the rewrite consistency score, which reflects the probability that the partial sequence  $i u_{\leq p}$  and  $u_{k}$  to form a valid fragment. For all words in the context, a vector composed of scores is calculated accordingly to obtain the rewrite consistency feature for insertion position  $p$ . Formally, for position  $p$  in the incomplete utterance and utterance context  $u = \{w_1^u, w_2^u, \dots, w_m^u\}$ , the rewrite consistency feature is written as:
+
+$$
+R C F = \left[ \mathbf {G P T} \left(w _ {k} ^ {u} \mid w _ {1} ^ {i u}, w _ {2} ^ {i u}, \dots , w _ {p} ^ {i u}\right) \right] _ {k = 1} ^ {m}, \tag {10}
+$$
+
+where  $\left[\cdot\right]_{k=1}^{m}$  represents a list operation from 1 to  $m$  elements.
+
+# c. Feature Integration.
+
+Denote that context length is  $m$  and incomplete utterance length is  $n$ . In the CSF calculation, any two token positions in the incomplete utterance and context are probed to obtain a divergence score, then the final CSF feature size is  $m \times n$ . Similarly, the RCF feature size is also  $m \times n$ . Since in node linking parsing, we concatenate context and incomplete utterance into one sequence. Therefore, we extend the feature to  $(m + n) \times (m + n)$  by zero padding to facilitate the use of the feature. The feature integration is achieved by adding the padded features to the biaffine score, i.e.,
+
+$$
+\begin{array}{l} s (i \xrightarrow {l} j) = H _ {i} ^ {(h e a d) \top} W H _ {j} ^ {(t a i l)} \\ + C S F _ {i, j} + R C F _ {i, j} + b, \\ \end{array}
+$$
+
+where  $\mathbf{b}$  is the bias.
+
+# 3.3 Dialogue RAG Dataset
+
+To test the performance of LLM and RAG methods in real dialogue scenarios, we manually construct an dialogue dataset on English and Chinese, Dialogue-RAG-MULTI-v1.0. The dataset provides multiple rounds of conversations as well as related document. We crawl web pages to get the documents and converted them into serialized text for retrieval knowledge bases. For the test dataset, we
+
+<table><tr><td>Spilt</td><td>Dialogue Rounds</td><td>Q&amp;A Number</td><td>Document Number</td></tr><tr><td>Train</td><td>Total: 23,996 English: 12,005 Chinese: 11,991</td><td>Total: 226,173 English: 124,852 Chinese: 101,321</td><td rowspan="3">Total: 9,321 English: 4,889 Chinese: 4,432</td></tr><tr><td>Dev</td><td>Total: 762 English: 391 Chinese: 371</td><td>Total: 6,310 English: 3,180 Chinese: 3,130</td></tr><tr><td>Test</td><td>Total: 1,036 English: 542 Chinese: 494</td><td>Total: 8,216 English: 4,321 Chinese: 3,895</td></tr></table>
+
+Table 1: Dataset setting.
+
+use the document to annotate the conversation data in the form of manual annotations. For the training and development datasets, annotations were generated using DeepSeek-V3's API, followed by manual review to remove low-quality data. The conversations are constructed based on the web pages. Dialogue-RAG-MULTI-v1.0 provides subjects with a real complex dialogue scene, which contains special concepts, contextual reference, incomplete utterance and other issues that often occur in daily dialogue process.
+
+In practical application scenarios, we can not only directly test the multi-round dialogue effect of the model, but also evaluate the dialogue ability of the model enhanced by the RAG method. The distribution of the dataset is shown in Table 1. According to the statistics of test dataset, the proportion of ellipses and coreferences in multi-round conversations exceeds  $60\%$ , which can effectively test RAG system in dialogue scenarios.
+
+# 4 Experiments
+
+# 4.1 Setup
+
+Dataset. We conduct knowledge dialogue task on our proposed Dialogue-RAG-MULTI-v1.0 dataset to test the effect of rewriting in RAG augmented human-machine dialogue tasks. To further explore the impact of utterance rewriting on retrieval, we conduct retrieval comparison on the CoQA (Reddy et al., 2019), TOPIOCQA (Adlakha et al., 2022) and OR-QuAC (Qu et al., 2020) dataset. To evaluate the validity of our IUR component, we conduct experiments on English Task-Oriented Dialogue dataset CANARD (Liu et al., 2020b) and Chinese Open Domain Dialogue dataset REWRITE (Su et al., 2019). Same dataset split as in the original paper has been adopted to ensure reproducibility.
+
+Baselines for RAG. In order to verify the effect of our method, we test the knowledge dialogue task
+
+in the condition of the generation without IUR and RAG (w/o IUR), the generation without RAG (w/IUR), the generation without IUR (RAG w/o IUR), the generation of IUR with few shot based on GPT-4o-mini (RAG w/GPT-4o-mini few shot IUR), and the generation of IUR with our Node Linking Iterative (Dialogue-RAG). In addition, we also compare the performance with other RAG methods, including GLM-RAG (ZhipuAI, 2024), AAR (Yu et al., 2023), Adaptive-RAG (Jeong et al., 2024), IRCot (Trivedi et al., 2022) and DRAGON+ (Liu et al., 2024).
+
+Baselines for IUR For the IUR task, in the setting without BERT pre-trained language model enhancement, the LSTM encoder is used in the baselines Syntactic, L-Gen, L-Ptr, and L-Ptr-Gen, which are consistent with our work. And vanilla attention-based generator (Bahdanau et al., 2015), pointer network generator (Vinyals et al., 2015) and hybrid pointer generator (See et al., 2017) is employed in L-Gen, L-Ptr, and L-Ptr-Gen, respectively. For DuS (Kumar and Joshi, 2016), a semantic sequence and a syntactic sequence model are combined to generate accurate rewrites. And RUN (Liu et al., 2020b) shaped the problem as the prediction of a word-level edit matrix and proposed a semantic segmentation model (UNet) to predict the edit operations. For the comparison with LLMs, we choose the open source models Qwen2-7B-Instruct (Yang et al., 2024) and ChatGLM3-6B (GLM et al., 2024). In addition, we tested DeepSeek-V3 (DeepSeek-AI, 2024) and GPT-4o-mini (OpenAI, 2024). For the REWRITE dataset, our baseline Transformer-as-encoder versions (TGen, T-Ptr and T-Ptr-Gen) are used to analyze the performance difference brought by the encoder architecture. L-Ptr- $\lambda$  and T-Ptr- $\lambda$  are variants of a pointer network that use  $\lambda$  to control whether to copy tokens from context or from utterance, instead of deciding to copy or generate as in the traditional pointer network.
+
+Evaluation Metrics. We use BLEU, ROUGE, and BERTScore automatic metrics for RAG enhanced dialogue scene. In the retrieval experiment, we use accuracy, recall and MRR@5 to evaluate the retrieval effect. Besides, we use BLEU, ROUGE, and EM automatic metrics for IUR performance evaluation as done in (Pan et al., 2019)
+
+Model Settings. For the knowledge dialogue task, ChatGLM3-6B (GLM et al., 2024)
+
+<table><tr><td>Method</td><td>B1</td><td>B2</td><td>B3</td><td>B4</td><td>R1</td><td>R2</td><td>RL</td><td>Sp</td><td>Sr</td><td>Sf</td></tr><tr><td>GLM-RAG</td><td>8.00</td><td>5.33</td><td>4.00</td><td>3.06</td><td>16.83</td><td>8.05</td><td>15.73</td><td>55.74</td><td>66.47</td><td>60.16</td></tr><tr><td>AAR</td><td>8.78</td><td>5.88</td><td>4.43</td><td>3.33</td><td>14.35</td><td>7.29</td><td>13.28</td><td>52.07</td><td>60.78</td><td>55.80</td></tr><tr><td>Adaptive-RAG</td><td>7.33</td><td>4.90</td><td>3.67</td><td>2.76</td><td>15.56</td><td>7.05</td><td>14.62</td><td>56.61</td><td>63.01</td><td>58.51</td></tr><tr><td>IRCot</td><td>5.43</td><td>3.58</td><td>2.68</td><td>1.99</td><td>13.04</td><td>5.65</td><td>12.25</td><td>56.39</td><td>58.34</td><td>55.67</td></tr><tr><td>DRAGON+</td><td>11.91</td><td>8.73</td><td>6.88</td><td>5.41</td><td>21.53</td><td>12.38</td><td>20.07</td><td>56.37</td><td>68.4</td><td>61.46</td></tr><tr><td>w/o IUR</td><td>5.84</td><td>3.31</td><td>2.25</td><td>1.58</td><td>10.34</td><td>3.5</td><td>9.55</td><td>52.82</td><td>57.37</td><td>54.83</td></tr><tr><td>w/ IUR</td><td>6.35</td><td>3.53</td><td>2.36</td><td>1.61</td><td>11.13</td><td>3.64</td><td>10.2</td><td>52.82</td><td>59.75</td><td>55.9</td></tr><tr><td>RAG w/o IUR</td><td>10.29</td><td>7.18</td><td>5.46</td><td>4.14</td><td>19.04</td><td>9.82</td><td>17.91</td><td>57.48</td><td>67.26</td><td>61.62</td></tr><tr><td>RAG w/ GPT-4o-mini few shot IUR</td><td>13.03</td><td>9.59</td><td>7.49</td><td>5.79</td><td>25.86</td><td>14.57</td><td>24.41</td><td>59.82</td><td>73.91</td><td>65.81</td></tr><tr><td>Dialogue-RAG (ours)</td><td>13.11</td><td>9.69</td><td>7.63</td><td>5.95</td><td>26.09</td><td>14.72</td><td>24.6</td><td>60.33</td><td>74.09</td><td>66.21</td></tr></table>
+
+Table 2: Main results.  $B$ ,  $R$ , and  $S$  are BLEU, ROUGE, and BERT SCORE respectively. The cumulative n-gram BLEU score is denote as  $B_{n}$ . For ROUGE metric, it measures the n-gram overlapping (denoted as  $R_{n}$ ) and longest matching (denoted as  $R_{L}$ ) between the rewritten utterances and the golden ones.  $S_{p}$ ,  $S_{r}$ , and  $S_{f}$  represent the precision, recall, and F1 score of BERT SCORE respectively.
+
+![](images/59bf6871ab663207f2cc6041b46a7fd283edaa70bf823e87000c3cbc30d1dbe1.jpg)  
+Figure 3: Comparison of results with and without rewrite on test sets across three datasets
+
+![](images/6b7fa6ace3149a282d46a7fb695164819cb8256ff021b42d2f2e76625c22b291.jpg)
+
+![](images/8de79f440a66f651558e3793d299a6773026a3edf4c85f6087a519767481c3ed.jpg)
+
+is used as our inference model. We test Qwen2.5-7B-Instruct (Team, 2024) and Llama3-8B-Chinese-Chat (Wang et al., 2024) as the base model and show their results in Appendix A. We test BiLSTM and Transformer as the IUR module encoder. The encoder setting can be found in Appendix B. For CSF and RCF feature extraction, we use the official GPT2-base. For the IUR experiment of LLM, we set the number of the LLM few shot samples as 3. For the non-LLM model, we tested it on a GeForce RTX 3090 graphics card. For LLM, we deployed models on two GeForce RTX 3090 GPUs for testing.
+
+# 4.2 Main Results and Analysis
+
+We test the results of different RAG systems on the incomplete utterance dialogue dataset. As shown in Table 2, we can find that the results become better after IUR (w/ IUR) or RAG (RAG w/o IUR) augmentation, which verifies the positive effect of IUR and RAG method in incomplete utterance dialogues. Besides, We can find the method of using IUR to enhance RAG (Dialogue-RAG) achieves the best results in various indicators, which fully
+
+confirms the effect of our method.
+
+To demonstrate the retrieval improvement of IUR, we separately compare the accuracy of correctly finding the dialogue document between direct retrieval (w/o IUR) and retrieval (w/ IUR) after rewriting. The results (w/o IUR, 37.80 vs w/ IUR, 77.54) show a surprising improvement in retrieval accuracy after rewriting incomplete sentences. In addition, we score the responses of the RAG system with GPT-4, and the results (w/o IUR, 4.89 vs w/ IUR, 7.70) also showed the gains brought by the IUR method.
+
+In addition, we also test the impact of rewriting results generated using large language models on RAG. The results show that the results generated by our IUR model have better performance than the results generated by the large language model (RAG w/ GPT-4o-mini few shot IUR). This fully validates the excellent performance of our IUR method.
+
+# 4.3 Retrieval Comparison
+
+To further explore the retrieval effect of utterance rewriting in the dialogue field, we conduct retrieval
+
+comparison on CoQA, TOPIOCQA and OR-QuAC datasets. The results are shown in Figure 3. The results on three datasets show that retrieval after rewriting is significantly better than direct retrieval. This shows that the utterance rewriting of the ambiguity in the dialogue by the IUR method can significantly improve the retrieval effect, which fully verifies our point.
+
+# 4.4 Evaluation on IUR Task
+
+To evaluate our IUR approach, we conducted comparative experiments on the CANARD and REWRITE benchmarks. Experiment results are presented in Tables 3 and 4. To show the effect of rewriting, we show the case comparison in Appendix C. Besides, We also show the results on other datasets in Appendix D.
+
+For the CANARD dataset, we can find that our model obtains the best results on BLEU and ROUGE, which illustrates digraph modeling, CSF and RCF features are generally effective for IUR. Besides, we test some LLM baselines on the CANARD benchmark to compare our methods. From Table 3, we can see that LLMs with both zero shot and few shot perform worse than our method. It is important to note that our approach not only performs significantly better than large models. As a lightweight model, its computing speed is also much higher than LLM. This is very important for the retrieval efficiency of the RAG system.
+
+For the REWRITE dataset, comparing the results of LSTM-based with Transformer-based baselines, we find that Transformer has an advantage in most systems, but not in all cases (L-Gen vs T-Gen), which may be due to the absence of optimization for Transformer. It suggests that just changing LSTMs with smaller parameters and training-data to Transformers may not necessarily be the best option, we keep using BiLSTM as the encoder when without BERT. On the REWRITE dataset, our full model still exhibits performance advantages with or without BERT. And since the REWRITE dataset is smaller, to increase the stability of the results, we use stricter EM and  $\mathbf{B}_2$ ,  $\mathbf{B}_4$ ,  $\mathbf{R}_2$ ,  $\mathbf{R}_L$  metrics. The reported improvements on these stricter metrics demonstrate the generalization ability of our method.
+
+Additionally, we also test some LLM baselines on the CANARD benchmark to compare our methods. From Table 3, we can see that LLMs with both zero shot and few shot perform worse than our method. Besides, as a lightweight model, our IUR
+
+<table><tr><td>Model</td><td>B1</td><td>B2</td><td>B4</td><td>R1</td><td>R2</td><td>RL</td></tr><tr><td colspan="7">Traditional Model</td></tr><tr><td>L-Ptr</td><td>52.4</td><td>46.7</td><td>37.8</td><td>72.7</td><td>54.9</td><td>68.5</td></tr><tr><td>Pronoun Sub†</td><td>60.4</td><td>55.3</td><td>47.4</td><td>73.1</td><td>63.7</td><td>73.9</td></tr><tr><td>L-Ptr-Gen</td><td>67.2</td><td>60.3</td><td>50.2</td><td>78.9</td><td>62.9</td><td>74.9</td></tr><tr><td>RUN</td><td>70.5</td><td>61.2</td><td>49.1</td><td>79.1</td><td>61.2</td><td>74.7</td></tr><tr><td colspan="7">Large Language Model w/ Zero shot</td></tr><tr><td>Qwen2</td><td>26.5</td><td>17.0</td><td>5.5</td><td>50.3</td><td>29.1</td><td>49.2</td></tr><tr><td>ChatGLM</td><td>33.6</td><td>24.8</td><td>12.2</td><td>58.9</td><td>40.8</td><td>54.8</td></tr><tr><td>DeepsEEK</td><td>27.1</td><td>18.6</td><td>7.5</td><td>50.4</td><td>31.5</td><td>46.8</td></tr><tr><td>GPT-4o-mini</td><td>34.6</td><td>25.9</td><td>18.6</td><td>60.2</td><td>42.5</td><td>56.6</td></tr><tr><td colspan="7">Large Language Model w/ Few shot</td></tr><tr><td>Qwen2</td><td>34.6</td><td>25.9</td><td>12.6</td><td>60.2</td><td>42.5</td><td>56.6</td></tr><tr><td>ChatGLM</td><td>36.9</td><td>28.1</td><td>14.7</td><td>62.8</td><td>45.0</td><td>59.3</td></tr><tr><td>DeepsEEK</td><td>42.6</td><td>34.8</td><td>20.9</td><td>70.4</td><td>55.3</td><td>67.8</td></tr><tr><td>GPT-4o-mini</td><td>44.4</td><td>36.7</td><td>22.2</td><td>72.8</td><td>58.0</td><td>70.5</td></tr><tr><td>Ours</td><td>71.5</td><td>62.8</td><td>51.3</td><td>81.0</td><td>63.5</td><td>76.0</td></tr></table>
+
+Table 3: Experimental results on CANARD. †: Results from Liu et al. (2020b).  
+
+<table><tr><td>Model</td><td>EM</td><td>B2</td><td>B4</td><td>R2</td><td>RL</td></tr><tr><td>L-Gen</td><td>47.3</td><td>81.2</td><td>73.6</td><td>80.9</td><td>86.3</td></tr><tr><td>L-Ptr-Gen</td><td>50.5</td><td>82.9</td><td>75.4</td><td>83.8</td><td>87.8</td></tr><tr><td>L-Ptr</td><td>51.5</td><td>82.7</td><td>75.5</td><td>84.0</td><td>88.2</td></tr><tr><td>L-Ptr-λ†</td><td>42.3</td><td>82.9</td><td>73.8</td><td>81.1</td><td>84.1</td></tr><tr><td>T-Gen</td><td>35.4</td><td>72.7</td><td>62.5</td><td>74.5</td><td>82.9</td></tr><tr><td>T-Ptr-Gen</td><td>53.1</td><td>84.4</td><td>77.6</td><td>85.0</td><td>89.1</td></tr><tr><td>T-Ptr</td><td>53.0</td><td>83.9</td><td>77.1</td><td>85.1</td><td>88.7</td></tr><tr><td>T-Ptr-λ</td><td>52.6</td><td>85.6</td><td>78.1</td><td>85.0</td><td>89.0</td></tr><tr><td>RUN</td><td>53.8</td><td>86.1</td><td>79.4</td><td>85.1</td><td>89.5</td></tr><tr><td>Ours</td><td>57.6</td><td>86.7</td><td>79.8</td><td>85.3</td><td>90.8</td></tr><tr><td>T-Ptr-λ + BERT</td><td>57.5</td><td>86.5</td><td>79.9</td><td>86.9</td><td>90.5</td></tr><tr><td>RUN + BERT</td><td>66.4</td><td>91.4</td><td>86.2</td><td>90.4</td><td>93.5</td></tr><tr><td>Ours + BERT</td><td>69.4</td><td>91.5</td><td>86.9</td><td>91.7</td><td>94.7</td></tr></table>
+
+Table 4: Experimental results on REWRITE. †: Reproduced from the code released by Su et al. (2019).
+
+component achieves significantly higher computing speeds. This enhances the retrieval efficiency of RAG.
+
+# 4.5 Ablation Study
+
+To illustrate the enhancement of CSF and RCF features on digraph modeling, we conduct an ablation study on the REWRITE dataset, and the results are shown in Table 5 and 6. We not only report the performance of IUR on the REWRITE dataset, but we also report the unique metrics of digraph modeling, parsing precision (PP), parsing recall (PR), parsing f-score (PF), and link triplet precision (TP), triplet recall (TR) and triplet f-score (TF). First, when the CSF or RCF feature is removed, the IUR and parsing performance both drop, indicating that these features can effectively help digraph decision-making. Among them, the CSF and RCF
+
+<table><tr><td></td><td>EM</td><td>B2</td><td>B4</td><td>R2</td><td>RL</td></tr><tr><td>RUN + BERT</td><td>66.4</td><td>91.4</td><td>86.2</td><td>90.4</td><td>93.5</td></tr><tr><td>Full Model</td><td>69.4</td><td>91.5</td><td>86.9</td><td>91.7</td><td>94.7</td></tr><tr><td>w/o CSF</td><td>69.0</td><td>90.9</td><td>86.3</td><td>90.8</td><td>94.1</td></tr><tr><td>w/o RCF</td><td>69.2</td><td>90.8</td><td>86.4</td><td>90.9</td><td>94.3</td></tr><tr><td>w/o CSF, RCF</td><td>68.6</td><td>90.9</td><td>86.3</td><td>90.8</td><td>94.1</td></tr></table>
+
+Table 5: IUR performance on REWRITE test set.  
+
+<table><tr><td></td><td>PP</td><td>PR</td><td>PF</td><td>TP</td><td>TR</td><td>TF</td></tr><tr><td>Full Model</td><td>82.6</td><td>76.8</td><td>79.6</td><td>76.9</td><td>70.1</td><td>73.3</td></tr><tr><td>w/o CSF</td><td>82.0</td><td>76.8</td><td>79.3</td><td>76.2</td><td>69.3</td><td>72.6</td></tr><tr><td>w/o RCF</td><td>81.7</td><td>77.4</td><td>79.5</td><td>75.7</td><td>70.6</td><td>73.1</td></tr><tr><td>w/o CSF, RCF</td><td>81.0</td><td>77.3</td><td>79.1</td><td>74.8</td><td>70.3</td><td>72.5</td></tr></table>
+
+Table 6: Parsing performance on REWRITE test set.
+
+features have the greatest impact on the EM metric, while the BLEU and ROUGE metrics are influenced slightly. Second, when CSF and RCF are completely removed, the results of digraph modeling still have a performance advantage compared to RUN, which shows the advantage of digraph modeling that simplifies the complexity without complex model designs. To further verify the advantages of our method, we conduct a case analysis in Appendix E.
+
+# 4.6 Rewriting Speed
+
+To illustrate the advantages of digraph modeling in IUR, we compare the rewriting speed between the RUN model, our base model, and our full model on the REWRITE test dataset in Figure 4. In order to minimize the influence of irrelevant factors, we run the three systems on the same machine for 5 times and report the average speed. Comparing the speed of RUN with our base model, it shows that modeling the IUR as a digraph structure is very efficient. Further use of our proposed CSF and RCF features reduces this efficiency advantage, but still achieves more than 3 times speedup. In addition, the speed of model w/ BERT is reduced compared with that w/o BERT, indicating that BERT encoding also occupies an important time overhead. In addition, the speed of LLM generation is about 20-30 tokens/s. We test the sentence generation speed of Qwen2-7B and ChatGLM3-6B on the same dataset and the results are 2.72 and 2.73 sentence per second, respectively. LLMs are far slower than our proposed method and not suitable for rewriting in RAG.
+
+![](images/fc70f3e8ff8187905ef687a58681c257f73da9227bbc9d1e1857633254524ac7.jpg)  
+Figure 4: Comparison of rewriting speed (sentences per second) on REWRITE test dataset.
+
+# 5 Conclusion
+
+Ellipses and coreferences in dialogues limit the retrieval capabilities of RAG systems. We propose a RAG method based on IUR enhancement. This method uses the Node Linking IUR model to rewrite queries, enabling better retrieval. In addition, we design a knowledge conversation dataset to evaluate performance of RAG in real dialogue scenarios. The experimental results show that our approach achieves better results when dealing with incomplete discourse in dialogs. In addition, our experimental results on the IUR task show that our IUR model greatly outperforms baseline and related systems, including large language models.
+
+# Limitation
+
+In this study, we investigate the effects of utterance rewriting methods on the enhancement of retrieval generation. Our research is centered on enhancing the generation capabilities of LLMs within RAG systems by optimizing retrieval effects. While the incorporation of IUR significantly enhances the quality of both retrieval and generation compared to standard retrieval methods, our exploration of the impact of retrieval results on generation remains incomplete. During the experiment, we find that not all queries positively influence the generation process of large models. In fact, some incorrect queries will mislead the large model generation. Moving forward, we will diligently examine how retrieval results impact models within RAG systems. By considering the necessity of retrieval, the relevance of retrieval outcomes, and other factors, we will further delve into the influence and optimization strategies for retrieval for LLMs and RAG systems.
+
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+Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, and Dong Yu. 2020. Semantic role labeling guided multi-turn dialogue rewriter. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6632-6639.  
+An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671.  
+Yao Yao, Zuchao Li, and Hai Zhao. 2024. Sirllm: Streaming infinite retentive llm. In Proceedings of the 62nd Annual Meeting of the Association for
+
+Computational Linguistics (Volume 1: Long Papers), pages 2611-2624.  
+Zichun Yu, Chenyan Xiong, Shi Yu, and Zhiyuan Liu. 2023. Augmentation-adapted retriever improves generalization of language models as generic plug-in. In The 61st Annual Meeting Of The Association For Computational Linguistics.  
+Yong Zhang, Zhitao Li, Jianzong Wang, Ning Cheng, and Jing Xiao. 2022. Self-attention for incomplete utterance rewriting. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8047-8051. IEEE.  
+ZhipuAI. 2024. Glmrag. Accessed: 2024-9-10.
+
+# A RAG Experiments on Other Base Model
+
+The experiment results on Qwen2.5-7B-Instruct (Team, 2024) and Llama3-8B-ChineseChat (Wang et al., 2024) are shown in Table 7. We are surprised to find that the IUR results on Qwen and Llama models are not ideal compared with ChatGLM3-6B (GLM et al., 2024). We check and find that this is due to the problem of rewriting the previous reference. Although this helps the model understand the question better, it can also lead to inconsistent responses, especially when the question answering of our dataset relies more on external data. The rewritten question is also incorrectly answered due to the lack of external knowledge, and the rewriting breaks the original dialogue coherence, so it is not effective. However, it can be found that the RAG system with IUR still has better results, which benefits from the enhancement of retrieval in the RAG process by IUR.
+
+# B Encoder Setting
+
+In BiLSTM sequence encoder setting, we use randomly initialized word embedding with dimension 100, character embedding with dimension 50, and set the character convolutional hidden size to 100. The embedding dropout is set to 0.2. Three BiLSTM layers are stacked with hidden size 600 and dropout 0.33. The hidden size of FFN for head and tail node projection is set to 600. While for the Transformer encoder setting, we fix the parameters of the bottom Transformer layers and only finetune the top 4 layers. We use a mean pooling strategy to deal with the inconsistency between the word-level parsing and subword-level representations. The hidden size of FFN is kept the same as in the BiLSTM encoder setting.
+
+Our models are trained using Adam optimizer for up to 200 epochs, with early stopping based on development dataset performance. The learning rate is set to 1e-3 and 5e-5 for the BiLSTM encoder and Transformer encoder settings respectively. For English, we use the official BERT-large-uncased, and for Chinese, we use BERT-base-chinese.
+
+# C Case Study of Dialogue-RAG
+
+To further explore the enhancement effect of IUR on RAG, we conduct a case study. Figure 5 illustrates a dialogue sample in our Dialogue-RAG-MULTI-v1.0 dataset and Figure 6 shows its related
+
+![](images/1aa8854074a44393cede58ab6f41b5996f10a2ad8eece16c140cc88316e5eb9c.jpg)  
+Figure 5: Sample Dialogue.
+
+![](images/ff4324e88e20ffa7899290a2d3fd498a8a12475ca026271e45843195aebc2a3f.jpg)  
+Figure 6: Sample Document.
+
+document. Figure 7 and Figure 8, respectively, show the query results and responses enhanced without or with IUR. Through comparison, it can be found that the IUR enhanced question carry more critical information. This enhances the retrieval accuracy, which in turn enhances the RAG system's response accuracy.
+
+# D Supplementary Dataset Experiments for IUR Task
+
+We conducted additional comparative experiments on English Task-Oriented Dialogue dataset MULTI (Pan et al., 2019) and Chinese Open Domain Dialogue dataset TASK (Quan et al., 2019).
+
+<table><tr><td>Method</td><td></td><td>B1</td><td>B2</td><td>B3</td><td>B4</td><td>R1</td><td>R2</td><td>RL</td><td>Sp</td><td>Sr</td><td>Sf</td></tr><tr><td rowspan="4">Qwen2.5</td><td>w/o IUR</td><td>9.04</td><td>5.54</td><td>3.90</td><td>2.77</td><td>14.97</td><td>5.79</td><td>13.96</td><td>58.43</td><td>63.53</td><td>60.69</td></tr><tr><td>w/ IUR</td><td>9.32</td><td>5.49</td><td>3.72</td><td>2.51</td><td>15.43</td><td>5.69</td><td>14.27</td><td>57.83</td><td>65.86</td><td>61.44</td></tr><tr><td>RAG w/o IUR</td><td>10.61</td><td>7.45</td><td>5.73</td><td>4.39</td><td>20.75</td><td>10.96</td><td>19.54</td><td>59.22</td><td>70.02</td><td>63.96</td></tr><tr><td>Dialogue-RAG (ours)</td><td>14.20</td><td>10.81</td><td>8.62</td><td>6.76</td><td>29.27</td><td>17.07</td><td>27.8</td><td>62.39</td><td>77.39</td><td>68.9</td></tr><tr><td rowspan="4">LLaMA3</td><td>w/o IUR</td><td>7.79</td><td>4.71</td><td>3.29</td><td>2.32</td><td>13.11</td><td>5.05</td><td>12.11</td><td>54.7</td><td>61.75</td><td>57.82</td></tr><tr><td>w/ IUR</td><td>7.74</td><td>4.55</td><td>3.08</td><td>2.06</td><td>13.54</td><td>4.95</td><td>12.4</td><td>54.4</td><td>63.77</td><td>58.56</td></tr><tr><td>RAG w/o IUR</td><td>9.76</td><td>6.92</td><td>5.37</td><td>4.14</td><td>19.10</td><td>10.15</td><td>18.07</td><td>56.58</td><td>68.47</td><td>61.69</td></tr><tr><td>Dialogue-RAG (ours)</td><td>13.00</td><td>9.80</td><td>7.78</td><td>6.09</td><td>26.82</td><td>15.59</td><td>25.44</td><td>60.28</td><td>76.02</td><td>66.99</td></tr></table>
+
+Table 7: RAG experiments on Qwen2.5 and LLaMA3.  $B$ ,  $R$ , and  $S$  are BLEU, ROUGE, and BERT SCORE respectively. The cumulative n-gram BLEU score is denote as  $B_{n}$ . For ROUGE metric, it measures the n-gram overlapping (denoted as  $R_{n}$ ) and longest matching (denoted as  $R_{L}$ ) between the rewritten utterances and the golden ones.  $S_{p}$ ,  $S_{r}$ , and  $S_{f}$  represent the precision, recall, and F1 score of BERT SCORE respectively.  
+
+<table><tr><td>{</td></tr><tr><td>&quot;question&quot;: &quot;How long did the construction take?&quot;,</td></tr><tr><td>&quot;ground truth&quot;: &quot;It took 44 years to complete.&quot;,</td></tr><tr><td>&quot;retrieval results&quot;: [</td></tr><tr><td>{&#x27;chunk id&#x27;: 18941, &quot;doc id&quot;: 5462, &quot;content&quot;: &quot; The work was suspended several times due to violation of the contract clauses by the customer. As of 2024, the work is not completed.\\References.\\n\\n&quot;,</td></tr><tr><td>{&#x27;chunk id&#x27;: 23971, &quot;doc id&quot;: 6828, &quot;content&quot;: &quot;On March 6 &amp; 10, 2011, installation of the 653 ton ea. sector gates was completed.\\nThe Notice of Construction Complete was issued on April 8, 2014.\\nSources.\\n&quot;,</td></tr><tr><td>{&#x27;chunk id&#x27;: 17610, &quot;doc id&quot;: 5109, &quot;content&quot;: &quot;The first such conditions were created by the Novi Sad Urban Institute in 1970. The Novi Sad Municipal Assembly decides on the construction of the bridge in December 1973. The architects of the bridge were academics Nikola Hajdin, Gojko Nenadic and Predrag Želacic.\\nWork began on 22 September 1976, when the first pier on the left bank of the Danube was stabbed. During the construction, the attention of the experts was focused on three details: The construction of the bars for pillar 17, where there were traces of land slides; the assembly of the steel structure, in pieces, weighing 200 tonnes, without scaffolding, with the help of the Derrick cranes; and the assembly of the steel ropes, which was original.\\n&quot;,</td></tr><tr><td>&quot;model response&quot;: &quot;The construction of the bridge took approximately 48 months.&quot;</td></tr><tr><td>}</td></tr></table>
+
+Figure 7: The case of RAG result without IUR.
+
+<table><tr><td>{</td></tr><tr><td>&quot;question&quot;: &quot;How long did the construction take?&quot;,</td></tr><tr><td>&quot;ground truth&quot;: &quot;It took 44 years to complete.&quot;,</td></tr><tr><td>&quot;rewritten question&quot;: &quot;How long did the construction of the Athar Jamad Mosque take?&quot;,</td></tr><tr><td>&quot;retrieval results&quot;: [</td></tr><tr><td>{&quot;chunk_id&quot;: 24918, &quot;file_id&quot;: 7088, &quot;content&quot;: &quot;nAthar Jamad Mosque\nMosque in Tamil Nadu, India\nAthar Jamad Masjid ( ; ; also known as &quot;\The Big Mosque&quot;) is located at Oppanakara Street within the Coimbatore district of Tamil Nadu in India. It is one of the oldest and biggest masjid in Coimbatore\nHistory and construction\nA perfume merchant named Athar, who migrated from Tirunelveli, built the structure. It took 44 years to complete, with construction beginning in 1860 and finishing in 1904. \nThe structure is made up of limestone and mortar and polished with egg white.&quot;,</td></tr><tr><td>{&quot;chunk_id&quot;: 30564, &quot;file_id&quot;: 8609, &quot;content&quot;: &quot; The analysis of the brickwork during the restoration showed different construction phases, and revealed that the foundations and the surviving walls were made of brick and stone. Moreover, the surveys show that during the conversion into a mosque in 1506 the atrium and the wall of the Mihrab had to be rebuilt. At the same time, a massive minaret was erected on the northeast side of the building\nReferences\nIn&quot;,</td></tr><tr><td>{&quot;chunk_id&quot;: 16735, &quot;file_id&quot;: 4877, &quot;content&quot;: &quot;Construction of the mosque took four years. It is 10,000 square meters in area. It has two minarets, each 35 meters high, and a dome of aluminum composite. The calligraphy on the interior of the dome was written by Semih Irtes. The large windows on three sides of the prayer hall were designed by Orhan Kogan. The minbar is acrylic and was designed by Tayfun Erdogmus. Decorative motifs are derived from Seljuk art. The large, asymmetrical chandelier has waterfall-shaped glass globes made by Nahide Büyükaymak#, &quot;reflecting a prayer that Allah&#x27;s light should fall on worshipers like rain,&quot; and the women&#x27;s section is designed especially to allow a clear view of the chandelier&quot;.],</td></tr><tr><td>&quot;model response&quot;: &quot; The construction of the Athar Jamad Mosque in Coimbatore, Tamil Nadu, India took 44 years, from 1860 to 1904.&quot;</td></tr></table>
+
+Figure 8: The case of RAG result with IUR.
+
+# D.1 Results on Multi Dataset
+
+In Table 8, we report the IUR performance on the MULTI dataset and list five baselines for comparison: Syntactic, L-Gen, L-Ptr-Gen, PAC, and RUN.
+
+Our method formulates IUR as a digraph parsing problem and proposes CSF and RCF features to strengthen the parsing process, thus achieving better IUR performance than the previous best system, improving on  $\mathcal{F}_1$ ,  $\mathcal{F}_2$ , and  $\mathcal{F}_3$  metrics, 2.4, 1.9, 2.0, respectively. For the final generated rewrite utterance, we get an improvement in BLEU and ROUGE metrics, reaching  $+0.8\mathrm{B}_1$ ,  $+0.8\mathrm{B}_2$ ,  $+0.5\mathrm{R}_1$ , and  $+1.3\mathrm{R}_2$ . On one hand, these improvements are due to digraph modeling and node linking parser, which can seek the insertion span and the insertion position more easily than UNet, due to only single-step digraph derivation is required, rather than enumerating all the words in the span as in RUN. On the other hand, the features derived from our generative PrLM can effectively help remove unreasonable digraph links, reducing
+
+the search space for insertion decisions.
+
+In the case of using BERT PrLM, PAC (Pan et al., 2019) proposes a "pick-and-combine" model that demonstrates strong competitiveness. We also achieved good improvements within this setting, with  $\mathcal{F}_1 + 2.1$ ,  $\mathcal{F}_2 + 2.0$ ,  $\mathcal{F}_3 + 1.3$  scores respectively, and 0.8, 1.2, 0.6, 1.2 gains on  $\mathbf{B}_1$ ,  $\mathbf{B}_2$ ,  $\mathbf{R}_1$ ,  $\mathbf{R}_2$  compared to RUN. This further illustrates the advantages of our digraph modeling compared to formalization as semantic segmentation. In addition, the improvements on  $\mathbf{B}_1$ ,  $\mathbf{B}_2$ ,  $\mathbf{R}_1$ , and  $\mathbf{R}_2$  show that our method makes the determination of context span and insertion point more accurate.
+
+# D.2 Results on Task Dataset
+
+Experiment results on the TASK benchmarks are presented in Table 9. We have additionally introduced two systems for comparison, GECOR and Pronoun Sub, of which GECOR 1 adopts the copy mechanism, while GECOR 2 adopts the gated copy mechanism. For a fair comparison, experiments
+
+<table><tr><td>Model</td><td>P1</td><td>R1</td><td>F1</td><td>P2</td><td>R2</td><td>F2</td><td>P3</td><td>R3</td><td>F3</td><td>B1</td><td>B2</td><td>R1</td><td>R2</td></tr><tr><td>DuS†</td><td>67.4</td><td>37.2</td><td>47.9</td><td>53.9</td><td>30.3</td><td>38.8</td><td>45.3</td><td>25.3</td><td>32.5</td><td>84.1</td><td>81.2</td><td>89.3</td><td>80.6</td></tr><tr><td>L-Gen†</td><td>65.5</td><td>40.8</td><td>50.3</td><td>52.2</td><td>32.6</td><td>40.1</td><td>43.6</td><td>27.0</td><td>33.4</td><td>84.9</td><td>81.7</td><td>88.8</td><td>80.3</td></tr><tr><td>L-Ptr-Gen†</td><td>66.6</td><td>40.4</td><td>50.3</td><td>54.0</td><td>33.1</td><td>41.1</td><td>45.9</td><td>28.1</td><td>34.9</td><td>84.7</td><td>81.7</td><td>89.0</td><td>80.9</td></tr><tr><td>RUN (Liu et al., 2020b)</td><td>66.9</td><td>54.9</td><td>60.3</td><td>53.0</td><td>43.4</td><td>47.7</td><td>43.8</td><td>35.7</td><td>39.3</td><td>91.1</td><td>88.0</td><td>91.0</td><td>83.3</td></tr><tr><td>Ours</td><td>67.3</td><td>58.7</td><td>62.7</td><td>53.7</td><td>46.1</td><td>49.6</td><td>44.9</td><td>38.3</td><td>41.3</td><td>91.9</td><td>88.8</td><td>91.5</td><td>84.6</td></tr><tr><td>PAC†</td><td>70.5</td><td>58.1</td><td>63.7</td><td>55.4</td><td>45.1</td><td>49.7</td><td>45.2</td><td>36.6</td><td>40.4</td><td>89.9</td><td>86.3</td><td>91.6</td><td>82.8</td></tr><tr><td>RUN + BERT</td><td>73.2</td><td>64.6</td><td>68.6</td><td>59.5</td><td>53.0</td><td>56.0</td><td>50.7</td><td>45.1</td><td>47.7</td><td>92.3</td><td>89.6</td><td>92.4</td><td>85.1</td></tr><tr><td>Ours + BERT</td><td>74.5</td><td>67.4</td><td>70.7</td><td>60.8</td><td>55.4</td><td>58.0</td><td>51.2</td><td>46.4</td><td>48.7</td><td>93.1</td><td>90.8</td><td>93.0</td><td>86.3</td></tr></table>
+
+Table 8: Experimental results on the MULTI dataset. †: Results from Pan et al. (2019). The bolded results in a column indicate a statistically significant improvement against all the baselines ( $p < 0.05$ ).  
+
+<table><tr><td>Model</td><td>EM</td><td>B4</td><td>F1</td></tr><tr><td>L-Ptr-Gen†</td><td>50.4</td><td>74.1</td><td>44.1</td></tr><tr><td>GECOR 1†</td><td>68.5</td><td>83.9</td><td>66.1</td></tr><tr><td>GECOR 2†</td><td>66.2</td><td>83.0</td><td>66.2</td></tr><tr><td>RUN (Liu et al., 2020b)</td><td>69.2</td><td>85.6</td><td>70.6</td></tr><tr><td>Ours</td><td>71.2</td><td>86.7</td><td>72.8</td></tr></table>
+
+Table 9: Experimental results on TASK. †: Results from Quan et al. (2019).
+
+are not augmented with BERT. From the results, our model obtains the best results on EM, BLEU, and ROUGE. In conclusion, on four datasets with different languages and different domains, these improvements illustrate digraph modeling, CSF and RCF features are generally effective for IUR.
+
+# E Case Study of Rewrite
+
+Table 10 shows a specific case-based comparison of performance between our model and baseline RUN. To rewrite the final utterance Could you give me the phone number and postcode?, our model can locate the insertion relationship clearly, and select the appropriate spans one restaurant listing for North American food and the only restaurant serving north American food for action. In contrast, RUN resolves the reference to north American food wrongly from the previous context. Though north American food forms a proper supplementary for the sentence, it is meaningless due to the user asks for restaurant instead of north American food. The possible reason is that the semantic segmentation and region searching algorithms are misled by the inference error and decode a grammatically correct but inappropriate span for insertion. The error from CNNs prediction in RUN is passed to the decoding algorithm, resulting in such inconsistency with the fact, which makes the region wrongly attended and weakens the model performance. Thus,
+
+it demonstrates that our model shows better robustness and is free from disturbing factors compared with region-based RUN.
+
+In addition, comparing the model outputs with and without CSF and RCF features, although both our models found the correct reference, i.e. American food restaurant, the one without CSF and RCF features chooses Utterance 4, while w/ CSF,RCF finds Utterance 5. Since CSF and RCF bring constraints from contextual similarity and rewriting consistency, the only restaurant serving American food in Utterance 5 is more consistent with the requirement. This also illustrates the effectiveness of our proposed CSF and RCF for IUR. However, in the final generation from surface tokens, there is an additional only compared to a gold reference, which is a common problem in extractive IUR modeling, but it does not have much effect on the actual role of IUR.
+
+<table><tr><td>Utterance 1: I am looking for a Danish restaurant.</td></tr><tr><td>Utterance 2: There are no danish restaurants listed . may i direct you toward another restaurant ?</td></tr><tr><td>Utterance 3: How about American food ?</td></tr><tr><td>Utterance 4: There is one restaurant listing for north American food , gourmet burger kitchen in centre part of town .</td></tr><tr><td>Utterance 5: Is there any north American food in centre part of town else ?</td></tr><tr><td>Utterance 6: That is the only restaurant serving north American food .</td></tr><tr><td>Utterance 7: Could you give me the phone number and postcode ?</td></tr><tr><td>Gold: Could you give me the phone number and postcode of the restaurant serving north American food?</td></tr><tr><td>RUN: Could you give me the phone number and postcode of north American food ?</td></tr><tr><td>Ours: Could you give me the phone number and postcode of one restaurant listing for north American food?</td></tr><tr><td>Ours w/ CSF,RCF: Could you give me the phone number and postcode of the only restaurant serving north American food?</td></tr></table>
+
+Table 10: Case study for comparison between our model and baselines.

2025/Dialogue-RAG_ Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting/images.zip

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2025/Did Translation Models Get More Robust Without Anyone Even Noticing_/full.md

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+# Did Translation Models Get More Robust Without Anyone Even Noticing?
+
+Ben Peters* and André F. T. Martins*†‡
+
+* Instituto de Telecomunicações, Lisbon, Portugal
+
+†Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
+
+$^{\ddagger}$ ELLIS Unit Lisbon (LULIS), Lisbon, Portugal
+
+Unbabel, Lisbon, Portugal
+
+benzurdopeters@gmail.com, andre.t.martins@tecnico.ulisboa.pt
+
+# Abstract
+
+Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to "noisy" inputs, such as spelling errors, abbreviations, and other formatting issues. In this paper, we revisit this insight in light of recent multilingual MT models and large language models (LLMs) applied to machine translation. Somewhat surprisingly, we show through controlled experiments that these models are far more robust to many kinds of noise than previous models, even when they perform similarly on clean data. This is notable because, even though LLMs have more parameters and more complex training processes than past models, none of the open ones we consider use any techniques specifically designed to encourage robustness. Next, we show that similar trends hold for social media translation experiments - LLMs are more robust to social media text. We include an analysis of the circumstances in which source correction techniques can be used to mitigate the effects of noise. Altogether, we show that robustness to many types of noise has increased.
+
+# 1 Introduction
+
+For years, the conventional wisdom has been that neural machine translation (MT) models are highly sensitive to source-side artificial and natural noise at inference time (Belinkov and Bisk, 2018). This insight has motivated many works that seek to make MT models more robust to noise through either specialized training (Ebrahimi et al., 2018; Karpukhin et al., 2019; Park et al., 2020; Vaibhav et al., 2019) or bespoke architectures (Rust et al., 2022; Salesky et al., 2021). However, MT is increasingly being performed in a different paradigm than when these analyses and architectures were created. Previously, models were mostly trained from scratch on task-specific data. Nowadays, strong results often depend on instruction-tuned large language models
+
+![](images/c2867fda8c5f1111e53b6960a2cd53c863b9b2ba43507f07d1e37051add8e9dc.jpg)  
+Figure 1: COMET-22 on the FLORES English-French devtest set when some proportion of source tokens are noised by swapping an adjacent pair of characters.
+
+(LLMs) like TowerLLM (Alves et al., 2024) or opaque proprietary systems like ChatGPT. These huge models may make existing robustness techniques more expensive (due to higher parameter counts) or impossible (specialized architectures cannot be grafted onto an existing pretrained system). So the question is, are these robustness techniques still necessary in the era of LLMs, or have larger models and training sets made today's models sufficiently robust on their own?
+
+In this work, we investigate these questions through experiments on social media text and synthetically noised corpora. These experiments play complementary roles: social media text contains diverse noise phenomena, but their effect is hard to isolate because the errors are unlabeled. On the other hand, synthetic errors differ from real-world noise, but they are interpretable and controllable, offering a way to measure noise in vitro. By evaluating on a broad spectrum of error types, we can paint a more vivid picture of what kinds of noise, and at what quantities, cause problems for MT sys
+
+tems. We make the following contributions:2
+
+- We show (§3) that large pretrained models are much more robust to synthetic errors than conventional NMT models (see Figure 1), even when they perform similarly on clean data. This result holds across noise types and language pairs, even though the large models lack architectural features that specifically encourage robustness to character noise.  
+- We introduce (§3.1) a novel technique for measuring the robustness of MT models by learning a regression to predict the quality decline as a function of how noisy the source is.  
+- We show (§4.1) that models that are robust to synthetic errors perform better at translating social media text. We investigate the relationship between synthetic robustness and performance on "real-world" noise.  
+- We conduct (§4.2) reference-free MT experiments on MultiLexNorm (van der Goot et al., 2021), which has never before been used for MT. We show that LLMs are more robust than conventional models to this type of noise.  
+- We show (§5) that finetuning on noisy translation data and source correction pipelines are both effective approaches to mitigate synthetic noise without substantially worsening performance on clean data, allowing conventional NMT models to become more robust than GPT-3.5 to 3 out of 4 synthetic noise types. Combining correction with 7-13B parameter LLM-based translation models yields even higher robustness, allowing these pipelines to surpass GPT-3.5 on all of our synthetic benchmarks, often by a wide margin. Although correction is less effective for social media data on the whole, many individual examples benefit from it, suggesting that identifying these examples is a future direction.
+
+# 2 Background
+
+# 2.1 Architectures for MT
+
+The transformer. In recent years, mainstream MT techniques have been based on the transformer (Vaswani et al., 2017), which uses multi-headed self-attention to mix information across time steps.
+
+In the original work, transformers used an encoder-decoder paradigm similar to recurrent MT models (Bahdanau et al., 2014). These models pair an encoder over the source with a decoder, an autoregressive language model that predicts target tokens one at a time. These tokens usually come from a subword vocabulary (Kudo, 2018; Sennrich et al., 2016). Initially, transformer MT models were trained from scratch for a single language pair on parallel data from sources such as the OPUS parallel corpus collection (Tiedemann, 2012).
+
+Multilingual models. Although single language pair models often perform well, they struggle in the absence of large quantities of data, making it difficult to achieve good results in low resource language settings. This problem can be mitigated through multilingual training with systems like M2M-100 (Fan et al., 2021) and NLLB-200 (NLLB Team et al., 2022). Low resource language pairs often benefit from training data in other languages. One challenge is language imbalance – the subword vocabulary and training procedure need to be designed to allow strong performance across covered language pairs in spite of this imbalance.
+
+LLMs for MT. In parallel to these MT-centric developments, transformers have increasingly been used in a transfer learning set-up in which a model is pretrained on a generic objective for which massive data is available. The model can then be finetuned on one or more downstream tasks. When the pretraining objective is language modeling (Radford et al., 2018), it is straightforward to use the model for generation tasks, including MT (Hendy et al., 2023). Recently, the paradigm has shifted from traditional finetuning to instruction tuning (Sanh et al., 2022; Wei et al., 2022), in which the finetuning data is accompanied by an instructional prompt. This has been shown to give models the ability to generalize to related tasks and has proven effective for MT (Alves et al., 2023, 2024).
+
+# 2.2 Robustness to Character Noise
+
+Character perturbations can have a large negative impact on MT model performance (Belinkov and Bisk, 2018). Consequently, a number of techniques have been proposed to mitigate their impact.
+
+Robustness through training. A common technique to increase robustness is to train MT models on examples with added source errors. Given that high-quality corpora containing authentic er
+
+<table><tr><td colspan="6">xx→en</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>88.17</td><td>89.19</td><td>86.35</td><td>88.36</td><td>88.02</td></tr><tr><td>NLLB</td><td>89.28</td><td>89.29</td><td>87.69</td><td>89.72</td><td>89.00</td></tr><tr><td>TI</td><td>89.77</td><td>89.76</td><td>88.69</td><td>90.16</td><td>89.60</td></tr><tr><td>GPT-3.5</td><td>89.64</td><td>89.45</td><td>87.98</td><td>89.81</td><td>89.22</td></tr></table>
+
+<table><tr><td colspan="6">en→xx</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>84.02</td><td>87.63</td><td>86.58</td><td>88.94</td><td>86.79</td></tr><tr><td>NLLB</td><td>88.07</td><td>88.30</td><td>88.48</td><td>89.58</td><td>88.61</td></tr><tr><td>TI</td><td>88.57</td><td>89.16</td><td>90.12</td><td>90.02</td><td>89.47</td></tr><tr><td>GPT-3.5</td><td>88.52</td><td>88.83</td><td>89.04</td><td>89.83</td><td>89.05</td></tr></table>
+
+Table 1: COMET on FLORES without added noise.
+
+rors are rare, the added noise is generally synthetic (Karpukhin et al., 2019), although it can be tuned to resemble natural errors (Martucci et al., 2021; Vaibhav et al., 2019). Whether training on synthetic noise is actually helpful for becoming robust to natural errors is an open question, with various works coming to contradictory conclusions (Belinkov and Bisk, 2018; Vaibhav et al., 2019).
+
+Robustness through architecture. As an alternative to specialized training techniques, robustness can be achieved with architectures other than the ubiquitous subword-level transformer. Modeling at the character or byte level (Sutskever et al., 2011; Xue et al., 2022) means that perturbations make only small changes to the sequence of tokens that the model is exposed to, whereas these same perturbations can cause a subword-level model to be exposed to completely different subword types. This may make character- and byte-level models more robust, although the evidence is mixed (Mielke et al., 2021). These models are also much slower than subword-level models because of longer sequence lengths. As an alternative, MT models can be trained on representations that are invariant to character shuffles (Belinkov and Bisk, 2018) or on visual representations of text (Salesky et al., 2021).
+
+# 3 Robustness to Synthetic Noise
+
+In our first experiments, we evaluate how models perform in the presence of token-level synthetic errors. Although these errors differ from "naturally occurring" noise, they are adjustable and function as a stress test for MT systems.
+
+# 3.1 Experiments
+
+In all of our synthetic experiments, we adopt a simple set-up: for each translation corpus, we in-
+
+troduce a particular type of perturbation into some percentage of the source-side tokens. We then compare performance translating this perturbed corpus to the performance on clean data. A model's robustness can be characterized by the steepness of its decline as the noise level is increased: a flatter slope indicates that the model handles noise better.
+
+Data. We use four types of synthetic perturbations, each of which is a plausible error based on the mechanics of typing. For each noise type, we create ten noised versions of the FLORES-200 devtest set (NLLB Team et al., 2022) corresponding to noise levels  $p \in \{0.1, 0.2, \ldots, 1.0\}$ . Within a version of the corpus, each whitespace-delimited token is perturbed with probability  $p$  and otherwise not altered. Therefore each token can be perturbed at most once. We use the following noise types:
+
+- swap: flip two adjacent characters.  
+- dupe: duplicate a character.  
+- drop: delete a character.  
+- key: replace a character with an adjacent key. Further details are in Appendix A.
+
+Models. We use models that differ in their scope (bi- or multilingual), architecture (encoder-decoder or decoder-only), and size (74M-13B parameters).
+
+- OPUS: We use transformer encoder-decoder models trained from scratch on a single language pair and released as part of OPUS-MT (Tiedemann and Thottingal, 2020). Model and vocabulary sizes are listed in Appendix B.  
+- NLLB (NLLB Team et al., 2022), like OPUS, is an encoder-decoder transformer trained on parallel text. However, NLLB is a many-to-many system trained on data in 202 languages. We use the 3.3 billion parameter version.  
+- TI: We use the 13 billion parameter version of TowerInstruct-v0.1 (Alves et al., 2024), an instruction-tuned LLM that can translate between 10 languages.  
+- GPT-3.5:³ the architecture and training data of GPT-3.5 are unknown, but we include it because of its success at MT (Hendy et al., 2023); the related GPT-4 can also correct character perturbations (Cao et al., 2023).
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-65.05</td><td>-65.41</td><td>-36.18</td><td>-63.44</td><td>-57.52</td></tr><tr><td>NLLB</td><td>-18.14</td><td>-20.97</td><td>-23.79</td><td>-20.81</td><td>-20.93</td></tr><tr><td>TI</td><td>-27.61</td><td>-27.01</td><td>-23.45</td><td>-25.54</td><td>-25.90</td></tr><tr><td>GPT-3.5</td><td>-4.36</td><td>-5.85</td><td>-20.89</td><td>-6.78</td><td>-9.47</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-53.61</td><td>-49.92</td><td>-29.54</td><td>-52.48</td><td>-46.39</td></tr><tr><td>NLLB</td><td>-16.50</td><td>-17.27</td><td>-21.11</td><td>-19.04</td><td>-18.48</td></tr><tr><td>TI</td><td>-19.62</td><td>-18.01</td><td>-17.33</td><td>-17.71</td><td>-18.17</td></tr><tr><td>GPT-3.5</td><td>-6.55</td><td>-5.68</td><td>-17.81</td><td>-7.09</td><td>-9.28</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-34.31</td><td>-31.83</td><td>-6.92</td><td>-34.58</td><td>-26.91</td></tr><tr><td>NLLB</td><td>-4.07</td><td>-5.31</td><td>-4.36</td><td>-4.58</td><td>-4.58</td></tr><tr><td>TI</td><td>-3.37</td><td>-4.54</td><td>-1.82</td><td>-3.62</td><td>-3.38</td></tr><tr><td>GPT-3.5</td><td>-1.36</td><td>-1.42</td><td>-5.64</td><td>-1.44</td><td>-2.47</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-63.78</td><td>-65.40</td><td>-38.48</td><td>-65.50</td><td>-58.29</td></tr><tr><td>NLLB</td><td>-20.66</td><td>-21.86</td><td>-28.01</td><td>-23.60</td><td>-23.53</td></tr><tr><td>TI</td><td>-29.15</td><td>-32.18</td><td>-19.80</td><td>-34.15</td><td>-28.82</td></tr><tr><td>GPT-3.5</td><td>-9.17</td><td>-8.63</td><td>-16.31</td><td>-10.27</td><td>-11.09</td></tr></table>
+
+Table 2: COMET-slope on FLORES for xx→en.  
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-72.01</td><td>-69.59</td><td>-73.99</td><td>-72.97</td><td>-72.14</td></tr><tr><td>NLLB</td><td>-23.33</td><td>-22.75</td><td>-19.68</td><td>-22.41</td><td>-22.04</td></tr><tr><td>TI</td><td>-16.64</td><td>-13.71</td><td>-12.63</td><td>-13.44</td><td>-14.11</td></tr><tr><td>GPT-3.5</td><td>-3.89</td><td>-4.46</td><td>-4.79</td><td>-3.76</td><td>-4.23</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-67.77</td><td>-63.30</td><td>-71.31</td><td>-69.66</td><td>-68.01</td></tr><tr><td>NLLB</td><td>-22.65</td><td>-22.23</td><td>-18.45</td><td>-21.71</td><td>-21.26</td></tr><tr><td>TI</td><td>-17.22</td><td>-15.00</td><td>-9.08</td><td>-14.68</td><td>-14.00</td></tr><tr><td>GPT-3.5</td><td>-6.59</td><td>-7.32</td><td>-6.72</td><td>-6.63</td><td>-6.81</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-54.90</td><td>-46.25</td><td>-65.86</td><td>-57.94</td><td>-56.24</td></tr><tr><td>NLLB</td><td>-4.04</td><td>-3.81</td><td>-2.79</td><td>-4.13</td><td>-3.69</td></tr><tr><td>TI</td><td>-2.40</td><td>-1.79</td><td>-1.39</td><td>-1.89</td><td>-1.87</td></tr><tr><td>GPT-3.5</td><td>-1.14</td><td>-1.32</td><td>-1.42</td><td>-0.98</td><td>-1.21</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-72.46</td><td>-72.01</td><td>-76.64</td><td>-75.81</td><td>-74.23</td></tr><tr><td>NLLB</td><td>-27.32</td><td>-25.91</td><td>-23.90</td><td>-25.57</td><td>-25.67</td></tr><tr><td>TI</td><td>-24.51</td><td>-21.10</td><td>-15.95</td><td>-22.04</td><td>-20.90</td></tr><tr><td>GPT-3.5</td><td>-8.19</td><td>-8.17</td><td>-8.91</td><td>-7.78</td><td>-8.26</td></tr></table>
+
+As TI and GPT-3.5 were both trained on closed data, it is possible that they were trained on our test sets. We include them because of the lack of high-quality fully-open LLM-based translation systems.
+
+Inference. For GPT-3.5, we sample with temperature 0. For other models, we decode with a beam size of 5. Details are in Appendix C.
+
+Evaluation. Our base corpus-level translation metric is COMET (Rei et al., 2020). COMET computes a normalized score for a hypothesis  $y$ , conditioned on the source  $x$  and a reference  $r$ . When we compute scores for translations from noisy data, we provide the COMET model the clean source, not the noisy version that was actually used to generate hypotheses. We measure the trajectory of performance as the amount of noise is increased, as depicted in Figure 1. To represent this trajectory as a single number, we fit a linear regression to predict the COMET decline relative to the clean score as a function of the proportion of noised tokens. We report the learned slope, which we call COMET-slope. A higher (closer to zero) COMET-slope indicates a more robust model. This metric
+
+Table 3: COMET-slope on FLORES for en  $\rightarrow$  xx.  
+
+<table><tr><td>Model</td><td>clean</td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>OPUS</td><td>88.94</td><td>-72.97</td><td>-69.66</td><td>-57.94</td><td>-75.81</td></tr><tr><td>OPUSLLM</td><td>85.78</td><td>-73.05</td><td>-69.68</td><td>-55.63</td><td>-74.48</td></tr></table>
+
+Table 4: Robustness of OPUSLLM on en→pt.
+
+can be interpreted as the number of COMET points that would be lost if every token were corrupted.
+
+Results. Table 1 shows that on clean data, TI records the highest COMET for all eight language pairs. The gap between the strongest system and the much smaller OPUS models is less than 2.5 COMET for all pairs except en→de and en→ko. However, Tables 2 and 3 show that OPUS suffers more from perturbations than the other models do. On the other end of the spectrum, GPT-3.5 is almost always the most robust system. NLLB and TI are between these two extremes. For swap and drop noise, NLLB is more robust than TI when translating to English, while the reverse is true when translating from English. This trend is less consistent for dupe noise. For key noise, NLLB is more robust than TI for every pair except ko→en. BLEU and chrF results are in Appendix H.
+
+![](images/d32eb31980b1f3d976aa233a6869ee0329654874f9e3b3283927515b4325b8f2.jpg)  
+Figure 2: COMET on en→fr swaps.
+
+# 3.2 Analysis
+
+Size and multilinguality. From these experiments, one might conclude that robustness depends largely on model size (OPUS is 14 times smaller than any other system) or multilinguality (all except OPUS are multilingual). However, Figure 2 shows that these are not the only factors. We reran swap experiments with NLLB-600M, NLLB-1.3B, and M2M-1.2B (Fan et al., 2021); despite the similar sizes of NLLB-1.3B and M2M-1.2B, and the fact that they are both massively multilingual, they handle noise differently: NLLB-1.3B is similar to NLLB-3.3B, while M2M-1.2B suffers as much as OPUS.
+
+Impact of architecture. Having shown that model size does not have a strong effect on robustness (at least not for NLLB), we next investigate the impact of architecture on performance. Is the gap between OPUS and other models primarily due to differences in training data, or is there some aspect of the LLMs' decoder-only structure that encourages robustness? To investigate, we trained a 1.3B parameter $^{6}$  decoder-only model on the same Tatoeba Challenge data as was used by the en→pt OPUS model. Training details are given in Appendix D. The performance and robustness of this model, which we dub OPUSLLM, are shown in Table 4. Although its performance on clean data lags 3 COMET points behind OPUS, the COMET-slope is similar for all four noise types, suggesting that the robustness of recent models is due to their training data, not their size or architecture.
+
+Tokenizer robustness. Introducing perturbations affects not only translation quality but also run
+
+<table><tr><td></td><td colspan="2">English</td><td colspan="2">Portuguese</td><td colspan="2">Korean</td></tr><tr><td></td><td>clean</td><td>key</td><td>clean</td><td>key</td><td>clean</td><td>key</td></tr><tr><td>OPUS</td><td>1.25</td><td>2.71</td><td>1.29</td><td>2.62</td><td>1.75</td><td>2.93</td></tr><tr><td>NLLB</td><td>1.40</td><td>2.20</td><td>1.53</td><td>2.21</td><td>2.03</td><td>3.02</td></tr><tr><td>TI</td><td>1.42</td><td>2.47</td><td>1.88</td><td>2.55</td><td>6.37</td><td>7.44</td></tr><tr><td>GPT-3.5</td><td>1.24</td><td>2.23</td><td>1.71</td><td>2.34</td><td>4.17</td><td>5.12</td></tr></table>
+
+Table 5: Tokenizer fertility with clean and key-perturbed data. For English, we used the en→fr OPUS model.  
+
+<table><tr><td></td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>OPUS</td><td>21.6</td><td>27.3</td><td>36.9</td><td>33.8</td></tr><tr><td>NLLB</td><td>27.6</td><td>35.1</td><td>45.7</td><td>42.3</td></tr><tr><td>TI</td><td>50.0</td><td>62.4</td><td>74.8</td><td>71.9</td></tr><tr><td>GPT-3.5</td><td>39.5</td><td>52.3</td><td>65.5</td><td>62.8</td></tr></table>
+
+Table 6: F1 between clean Korean token sequences and their  $100\%$  noisy counterparts.
+
+time. Perturbations create character sequences that are less similar to the data that tokenizers are trained on, which leads to more pieces being used to encode the sentence. This is true even for drop noise, which increases the length of the tokenized sequence even as it shortens the detokenized sequence. In Table 5, we compare tokenizers by their fertility — the average number of subword pieces per whitespace word — on clean and key data. While OPUS tokenizers generally have very low fertility on clean data, it increases more than the other tokenizers, suggesting the tokenizer itself is less robust to character perturbations. It is also notable that TI and GPT-3.5 have high fertility even on clean Korean text. While this is a symptom of tokenizer unfairness in large models (Petrov et al., 2023), it can also be a sign of tokenizer robustness: the higher the fertility, the closer the model is to byte-level tokenization. This results in noisy token sequences that are much closer to the clean sequences for TI and GPT-3.5, as can be seen in terms of F1 in Table 6. The same trend does not hold for the other languages.
+
+# 4 Robustness to Social Media Text
+
+The previous experiments show that large translation models and LLMs are more robust to synthetic character perturbations than conventional MT models. But is this result applicable to "authentically noisy" domains such as social media text? The nature of "noise" here is different than in the synthetic task: social media text does not necessarily contain many errors (Rello and Baeza-Yates, 2012), but the domain is very different from FLORES. This
+
+<table><tr><td>Method</td><td>en→fr</td><td>fr→en</td></tr><tr><td>OPUS</td><td>77.21</td><td>79.64</td></tr><tr><td>r/OPUS</td><td>79.22</td><td>81.94</td></tr><tr><td>NLLB</td><td>79.33</td><td>80.59</td></tr><tr><td>TI</td><td>81.91</td><td>83.66</td></tr><tr><td>GPT-3.5</td><td>81.33</td><td>84.72</td></tr></table>
+
+Table 7: COMET on the MTNT test set.
+
+makes it difficult to isolate the effect of noise from the general domain adaptation problem. Ideally, we would have a translation corpus in which each example is a triple consisting of an original noisy source, a manually annotated cleaned source, and a gold standard translation. This would allow translations of clean and noisy versions of the same source to be compared on some reference-based metric, isolating the effect of the errors. As we are aware of only one such corpus (Bawden and Sagot, 2023), we instead perform two complementary investigations. First, we evaluate on MTNT (Michel and Neubig, 2018), a noisy social media MT corpus. Although this is a useful test of our models, the noise is not labeled and there is no clean version of the same data to compare to. This motivates our second experiment, in which we translate data from MultiLexNorm (van der Goot et al., 2021), a lexical normalization benchmark. Together, these experiments allow us to see both which models succeed and how badly they fail.
+
+# 4.1 MTNT Experiments
+
+MTNT pairs Reddit posts with high-quality professional translations. Although the references are somewhat clean, the sources are only lightly filtered, making them potentially noisy. $^{7}$  Unfortunately no cleaned sources exist, making the effect of noise difficult to isolate. Despite this difficulty, it is often used as a robustness benchmark (Karpukhin et al., 2019; Park et al., 2020; Salesky et al., 2021; Vaibhav et al., 2019, inter alia).
+
+Finetuning. We finetuned OPUS on MTNT en  $\leftrightarrow$  fr as described in Appendix E. We dub this model r/OPUS.
+
+Results. Results are shown in Table 7. Despite in-domain finetuning benefiting OPUS by more than 2 COMET points for both en→fr and fr→en, this does not close the gap to TI and GPT-3.5.
+
+This suggests that TI and GPT-3.5 benefit from their massive training corpora, which likely contain large quantities of social media text. In contrast, the only social media text the finetuned OPUS models have seen are MTNT's tiny training sets (36k parallel examples for en→fr, 19k for fr→en), plus whatever is in the Tatoeba Challenge corpora.
+
+# 4.2 MultiLexNorm Experiments
+
+While MTNT is an established benchmark and useful sanity check, it is not controllable like our synthetic experiments; we cannot isolate the effect of noise because there is no non-noisy version of the corpus. Therefore we pivot to evaluate models on translating MultiLexNorm (van der Goot et al., 2021), a lexical normalization dataset that pairs social media text primarily from Twitter with manually cleaned versions of the same. Switching from MTNT to MultiLexNorm comes with a trade-off: in order to gain clean sources, we lose references.
+
+Data. We use the English, German, and Spanish data from MultiLexNorm as our translation sources. In experiments with English sources, we translate to German and Spanish; otherwise, we translate to English. Statistics are presented in Appendix F.
+
+Evaluation. As MultiLexNorm lacks reference translations, we use three reference-free metrics. Faux-BLEU (Anastasopoulos, 2019) computes  $\mathrm{spBLEU}(y_n, y_c)$  (Papineni et al., 2002; NLLB Team et al., 2022), where  $y_n$  and  $y_c$  are the hypotheses computed from the noisy source and the clean source, respectively.  $y_c$  is treated as a pseudoreference. By analogy we also compute faux-COMET. These faux-metrics measure the similarity between  $y_c$  and  $y_n$ , with faux-BLEU capturing lexical features while faux-COMET is "deeper." In addition, we use a new metric that we dub  $\Delta \mathrm{QE}$ . Given  $y_n$ ,  $y_c$ , noisy and clean source sequences  $x_n$  and  $x_c$ , and a reference-free quality estimation metric QE,  $\Delta \mathrm{QE} = \mathrm{QE}(x_c, y_c) - \mathrm{QE}(x_c, y_n)$ . We compute QE with COMETKiwi<sup>8</sup> (Rei et al., 2022b). A  $\Delta \mathrm{QE}$  close to zero means that a model produces similar-quality outputs for both inputs, indicating robustness, whereas a large positive value indicates that translation quality suffers on noisy data.
+
+Results. Table 8 shows the performance of all models with both noisy and gold-standard cleaned versions of the corpora. In terms of  $\Delta \mathrm{QE}$ , GPT-3.5 performs best for all language pairs. It also
+
+<table><tr><td rowspan="2">Model</td><td colspan="3">en→de</td><td colspan="3">de→en</td><td colspan="3">en→es</td><td colspan="3">es→en</td></tr><tr><td>FB</td><td>FC</td><td>ΔQE</td><td>FB</td><td>FC</td><td>ΔQE</td><td>FB</td><td>FC</td><td>ΔQE</td><td>FB</td><td>FC</td><td>ΔQE</td></tr><tr><td>OPUS</td><td>81.9</td><td>81.50</td><td>6.21</td><td>74.8</td><td>88.36</td><td>3.95</td><td>80.3</td><td>82.54</td><td>4.11</td><td>86.2</td><td>88.52</td><td>2.54</td></tr><tr><td>NLLB</td><td>87.3</td><td>87.28</td><td>2.81</td><td>74.8</td><td>87.75</td><td>2.88</td><td>88.3</td><td>88.62</td><td>2.24</td><td>84.2</td><td>88.70</td><td>2.30</td></tr><tr><td>TI</td><td>88.8</td><td>89.07</td><td>1.86</td><td>75.2</td><td>90.57</td><td>2.41</td><td>89.0</td><td>90.26</td><td>1.19</td><td>85.3</td><td>89.32</td><td>2.63</td></tr><tr><td>GPT-3.5</td><td>87.1</td><td>88.45</td><td>1.15</td><td>83.1</td><td>91.86</td><td>1.32</td><td>89.1</td><td>90.14</td><td>0.72</td><td>87.9</td><td>91.23</td><td>0.91</td></tr></table>
+
+records the best faux-BLEU for all pairs except  $\mathrm{en} \rightarrow \mathrm{de}$ , on which TI and NLLB both outperform it. The faux-COMET results show a split, where GPT-3.5 has the highest scores for both to-English pairs but TI passes it for  $\mathrm{en} \rightarrow \mathrm{de}$  and  $\mathrm{en} \rightarrow \mathrm{es}$ .
+
+# 5 Strategies for Mitigating Noise
+
+So far we have shown that OPUS is less robust to synthetic noise than larger models and performs worse on social media text. Next we evaluate two techniques for mitigating noise: finetuning MT models on synthetically noised data and incorporating a source correction pipeline. These approaches have contrasting trade-offs. Finetuning the MT model allows the robust translation task to be learned end-to-end. However, this is expensive if a model is very large (TI, NLLB) and impossible if its weights are closed (GPT-3.5). On the other hand, pipelines are modular, allowing the same correction system to be reused with any model. The drawback of this modularity is that pipelines may introduce errors as well as fixing them.
+
+# 5.1 Synthetic Experiments
+
+As a comparison of finetuning and source correction, we focus on en→pt with the same synthetically noised corpora as in Section 3. We kept the training data as similar as possible between finetuning and correction experiments: in both cases, we subsampled 5 million examples from the Tatoeba Challenge en→pt training set. We noised  $15\%$  of source tokens with each of the four noise types used in Section 3, so a total of  $60\%$  of tokens were corrupted. For validation, we concatenated the original FLORES en→pt development set to a version of the same set in which  $20\%$  of tokens have been corrupted with each noise type. By including clean validation data, this favors models that do not forget how to translate clean data. For finetuning OPUS, these noised source examples are paired with clean Portuguese targets. For finetuning correction models, they are paired with the original clean English sources. We followed the training procedure in
+
+Table 8: MultiLexNorm results. FB is faux-BLEU and FC is faux-COMET.  
+
+<table><tr><td>Model</td><td>clean</td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>OPUS</td><td>88.94</td><td>-72.97</td><td>-69.66</td><td>-57.94</td><td>-75.81</td></tr><tr><td>+finetuning</td><td>88.52</td><td>-2.14</td><td>-7.59</td><td>-0.87</td><td>-5.01</td></tr><tr><td>+correction</td><td>88.36</td><td>-2.02</td><td>-11.81</td><td>-0.08</td><td>-7.20</td></tr><tr><td>NLLB</td><td>89.58</td><td>-22.41</td><td>-21.71</td><td>-4.13</td><td>-25.57</td></tr><tr><td>+correction</td><td>89.11</td><td>-1.57</td><td>-9.09</td><td>-0.07</td><td>-5.47</td></tr><tr><td>TI</td><td>90.02</td><td>-13.44</td><td>-14.68</td><td>-1.89</td><td>-22.04</td></tr><tr><td>+correction</td><td>89.57</td><td>-0.86</td><td>-5.59</td><td>-0.16</td><td>-2.99</td></tr><tr><td>ChatGPT</td><td>89.83</td><td>-3.76</td><td>-6.63</td><td>-0.98</td><td>-7.78</td></tr></table>
+
+Table 9: Clean COMET and COMET-slope for en→pt with finetuning and source correction.  
+MT Finetuning  
+
+<table><tr><td>Model</td><td>clean</td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>100k</td><td>88.28</td><td>-12.08</td><td>-18.96</td><td>-4.63</td><td>-18.65</td></tr><tr><td>1m</td><td>88.13</td><td>-5.21</td><td>-11.72</td><td>-2.04</td><td>-9.49</td></tr><tr><td>5m</td><td>88.52</td><td>-2.14</td><td>-7.59</td><td>-0.87</td><td>-5.01</td></tr></table>
+
+Source Correction  
+
+<table><tr><td>Model</td><td>clean</td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>100k</td><td>88.59</td><td>-4.12</td><td>-15.31</td><td>-0.11</td><td>-10.84</td></tr><tr><td>1m</td><td>88.30</td><td>-2.25</td><td>-12.83</td><td>-0.15</td><td>-7.83</td></tr><tr><td>5m</td><td>88.36</td><td>-2.02</td><td>-11.81</td><td>-0.08</td><td>-7.20</td></tr></table>
+
+Table 10: Performance of noise mitigation with OPUS en  $\rightarrow$  pt at varying quantities of noisy training data.
+
+Appendix E for both MT and correction models.
+
+Source correction (SC). As our base model for SC, we adopted ByT5-Small (Xue et al., 2022). At inference time we report the results of a pipeline that pairs the corrector with a translation model. In intrinsic terms, this corrector manages a chrF (Popović, 2015) of at least 89.6 at correcting each noise type, and over 97.5 for swaps and dupes. Additional results are shown in Appendix G.
+
+Results. Our main results are shown in Table 9. For OPUS, both MT finetuning and correction greatly improve robustness to synthetic errors, with both techniques reporting flatter COMET-slopes than GPT-3.5 for all noise types except drop. At a more granular level, in Figure 3 we show OPUS's COMET scores at all levels of swap noise for MT
+
+![](images/6b7e0a418b13721eb5b5d28d2207c85fb54b7207795b194e8484859f076c70e3.jpg)  
+Figure 3: OPUS en  $\rightarrow$  pt swaps with finetuning and SC.
+
+finetuning and SC. It is clear that MT finetuning outperforms SC across almost all noise levels. Despite NLLB and TI being more robust than OPUS in general, they too benefit from SC, suggesting that its effect is complementary with the models' inherent robustness. Both MT finetuning and SC slightly degrade model performance on clean translation. However, these degradations are small, and become smaller as the base model gets larger: corrected OPUS declines 0.58 COMET, versus only 0.45 for TI. These results also show the surprising difficulty of drop errors: although these are not more problematic for baseline models than other error types (see Tables 2 and 3), neither correction nor MT finetuning handles them as effectively as the other error types. This suggests that the missing information from a single deleted character often cannot be recovered from surrounding context.
+
+Effect of training size. Although we used 5 million training examples in our main experiments, Table 10 shows the effect that using fewer has on OPUS's robustness. The correction approach performs much better when data is restricted to  $100k$  examples, but this advantage shrinks as the training size is increased and eventually MT finetuning outperforms it. This supports the intuition that noisy translation is a harder task than correction, so the OPUS model requires more examples to learn it.
+
+How often does mitigation work? In the main results, we showed that MT finetuning and SC both greatly improve robustness. However, as the scores are presented at corpus level, they do not give insight into what percentage of examples are actually helped, versus how many are harmed. To fill in this missing piece, we compared sentence-level COMET between our baseline models and the two
+
+![](images/01162ff5857edce610fd286d9bdbde4f9968e7ac54bb3b69107842b30b849591.jpg)
+
+![](images/820a98da1d6c08c71f58263e21dd4a0db2e698ffb1db2b9d8b716a585ac1af76.jpg)
+
+![](images/7d1f9ee310cbc74732ff76bee2400fdd10752977a5a68a45f0b56bcbd813f59f.jpg)  
+Figure 4: Percentage of en→pt swap examples for which finetuning OPUS (top), correcting OPUS (middle), or correcting TI (bottom) outperforms the baseline.
+
+mitigation approaches. Results are shown in Figure 4. At high noise levels, both techniques nearly always help OPUS. However, they behave differently on clean sources. While SC almost never improves clean scores and harms less than  $10\%$  of examples, finetuning is more of a high-risk strategy, as it makes scores worse for about half of examples while also improving COMET for  $38\%$  of them. The trend is subtly different for SC with TI. While correction helps more examples than it hurts whenever the noise level is at least  $10\%$ , the percentage of examples that are harmed by correction actually increases up to  $30\%$  noise. This suggests that correction may introduce errors into some noisy sequences that TI could have handled itself.
+
+# 5.2 Mitigating Errors in MTNT
+
+Having shown that MT finetuning and SC are both effective techniques for improving robustness to synthetic errors, we return to MTNT. Our goal is to determine whether either MT finetuning or SC can provide some benefit to performance on en→fr.
+
+Finetuning vs. correction for OPUS. We evaluate MT finetuning and SC on MTNT. For MT finetuning, we compare two techniques: finetuning only on MTNT, as in Section 4.1; and finetuning on
+
+<table><tr><td>Finetuning</td><td>Uncorrected</td><td>Corrected</td></tr><tr><td>None</td><td>77.21</td><td>76.31</td></tr><tr><td>MTNT</td><td>79.22</td><td>77.97</td></tr><tr><td>Synthetic</td><td>75.95</td><td>75.31</td></tr></table>
+
+Table 11: OPUS performance on the MTNT en→fr test set with various finetuning techniques.  
+
+<table><tr><td>Method</td><td>r/OPUS</td><td>NLLB</td><td>TI</td></tr><tr><td>Base</td><td>79.22</td><td>79.33</td><td>81.91</td></tr><tr><td>Correction</td><td>77.97</td><td>78.32</td><td>80.94</td></tr><tr><td>Oracle</td><td>79.71</td><td>79.82</td><td>82.43</td></tr></table>
+
+Table 12: MTNT results with and without source correction. The oracle selects the translation with the higher COMET between the baseline and the pipeline.
+
+5 million synthetically noised Tatoeba Challenge en→fr examples, matching the procedure used for en→pt in Section 5.1. For SC, we use the same English model as in the synthetic experiments. Results are shown in Table 11. Finetuning on MTNT outperforms the baseline, but both SC and MT finetuning are harmful, confirming that MTNT is not very noisy in terms of spelling errors (Karpukhin et al., 2019; Michel and Neubig, 2018).
+
+Effect of source correction. In Table 12, we show results with and without SC for various models. Although correction does not improve results on the whole, the oracle consistently outperforms the baseline by about 0.5 COMET, illustrating that many sequences do benefit from correction. Indeed,  $21.8\%$  of MTNT examples improve with correction and another  $46.1\%$  are not harmed. Future work could investigate the use of a routing mechanism that decides for each example whether to apply SC.
+
+# 6 Conclusion
+
+We presented several experiments testing the robustness of MT systems to synthetic and natural noise. On synthetic noise, we showed that large multilingual MT models and LLMs are far more robust than older models. The experiments on social media translation showed that larger models also worked better on natural noise. We further supported this conclusion through reference-free translation experiments with a novel evaluation metric based on quality estimation. Finally, we showed that noisy finetuning and source correction allow smaller models to exceed GPT-3.5's robustness synthetic noise, while also being useful in many cases
+
+for handling natural errors.
+
+# Limitations
+
+We acknowledge the limitations of our work. All of the languages included in this study have large speaker populations and many resources available, and are the official languages of countries. Some conclusions may not generalize to low resource languages. Additionally, this paper studies only one source of natural noise, namely social media text. Other varieties of text perceived as noisy, such as transcribed speech or text written by nonfluent language users, may have different properties. Finally, the evaluation techniques used in this work are all automatic or neural, and may differ from gold-standard human evaluation.
+
+# Acknowledgments
+
+This work was supported by EU's Horizon Europe Research and Innovation Actions (UTTER, contract 101070631), by the project DECOLLAGE (ERC-2022-CoG 101088763), by the Portuguese Recovery and Resilience Plan through project C645008882-00000055 (Center for Responsible AI), and by FCT/MECI through national funds and when applicable co-funded EU funds under UID/50008: Instituto de Telecomunicações. We thank Sweta Agrawal and the anonymous reviewers for their helpful comments.
+
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+
+# A Key Noise
+
+As different languages customarily use different keyboard layouts, we made slight alterations to our key noising procedure for each source language. We use the QWERTZ layout for German, AZERTY for French, QWERTY for English and Portuguese, and South Korean Dubeolsik for Korean. For Korean, we used hangul-jamo<sup>9</sup> to decompose hangul characters into jamo, which represent individual keystrokes, before applying perturbations.
+
+# B OPUS Models
+
+The parameters and vocabulary sizes of the OPUS models are shown in Table 13. All checkpoints come from the Tatoeba Challenge (Tiedemann, 2020).<sup>10</sup>
+
+<table><tr><td>LP</td><td>Vocab</td><td>Params</td></tr><tr><td>en→de</td><td>58.1k</td><td>74.4M</td></tr><tr><td>de→en</td><td>58.1k</td><td>74.4M</td></tr><tr><td>en→es</td><td>55.0k</td><td>234.8M</td></tr><tr><td>es→en</td><td>65.0k</td><td>77.9M</td></tr><tr><td>en→fr</td><td>53.0k</td><td>232.7M</td></tr><tr><td>fr→en</td><td>53.0k</td><td>232.7M</td></tr><tr><td>en→ko</td><td>32.0k*</td><td>209.2M</td></tr><tr><td>ko→en</td><td>32.0k*</td><td>209.2M</td></tr><tr><td>en→pt</td><td>54.7k</td><td>234.5M</td></tr><tr><td>pt→en</td><td>60.0k</td><td>75.4M</td></tr></table>
+
+Table 13: OPUS models. Each path is preceded by Helsinki-NLP/. *en↔ko use separate 32k source and target vocabularies. All others use shared vocabularies.
+
+# C Inference
+
+Owing to the differing frameworks between OPUS, OPUSLLM, NLLB, TI, and ByT5 models, we use different beam search implementations depending on the model. For OPUS models, we decode with Marian (Junczys-Dowmunt et al., 2018), while NLLB and ByT5 use Hugging Face transformers.[11] TI models and OPUSLLM are decoded with v11m (Kwon et al., 2023).[12] Regardless of framework, we used a beam size of 5 across all experiments.
+
+# C.1 Prompt for TowerInstruct
+
+"Translate the following text from [source language] to [target language].\nSource: [source text]\n[target language]:"
+
+# C.2 Prompt for GPT-3.5
+
+"Translate this sentence from [source language] to [target language].\nSource: [source text]\nTarget:"
+
+# D OPUSLLM Training Details
+
+The OPUSLLM model was trained with used Megatron-DeepSpeed $^{13}$  for a maximum of 300k steps with an effective batch size of 65k tokens and a base learning rate of  $3 \times 10^{-4}$ , with a constant learning rate schedule and 5000 warmup steps.
+
+The model used the same 32k tokenizer as TI. To account for the model's decoder-only structure, examples were formatted with two special tokens to indicate the beginning of the source and target sequences. At inference time, we selected the checkpoint with the best COMET on the FLORES en→pt dev set.
+
+# E Training Hyperparameters
+
+In this work, we finetune two types of base models: OPUS models and ByT5-Small. Despite the various technical differences between these two models, we used almost identical training procedures for them, with differences noted below.
+
+Basic training procedure. We finetuned using early stopping with patience 3. We validated every 500 steps. We used a grid over the learning rates  $\{10^{-4}, 10^{-5}, 10^{-6}\}$  and selected the best checkpoint by validation loss.
+
+Training library. For OPUS models, we finetuned with the marian command line tool from Marian (Junczys-Dowmunt et al., 2018). For ByT5-Small, we used a script that leverages the Trainer class from Hugging Face transformers.
+
+# F MultiLexNorm Statistics
+
+<table><tr><td>Lang.</td><td>Sent.</td><td>%Noisy</td><td>Reference</td></tr><tr><td>English</td><td>1967</td><td>6.9</td><td>(Baldwin et al., 2015)</td></tr><tr><td>German</td><td>583</td><td>8.9</td><td>(Sidarenka et al., 2013)</td></tr><tr><td>Spanish</td><td>531</td><td>7.7</td><td>(Alegria et al., 2013)</td></tr></table>
+
+# G Intrinsic Correction Performance
+
+Table 14: Statistics of selected MultiLexNorm corpora.  
+
+<table><tr><td></td><td>swap</td><td>drop</td><td>dupe</td><td>key</td></tr><tr><td>None</td><td>40.0</td><td>47.0</td><td>68.2</td><td>45.7</td></tr><tr><td>JamSpell</td><td>75.5</td><td>63.5</td><td>91.7</td><td>78.0</td></tr><tr><td>ByT5-Small</td><td>97.5</td><td>89.6</td><td>99.6</td><td>94.4</td></tr></table>
+
+Intrinsic correction results in terms of chrF (Popovic, 2015) are shown in Table 15. In addition to our ByT5-Small corrector, we include the results of the pretrained English model from JamSpell[14] (Ozinov, 2019), a trigram-based spell-checker.
+
+Table 15: chrF (Popović, 2015) of source correctors at  $100\%$  noise in FLORES.  
+
+<table><tr><td colspan="6">xx→en</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>44.7</td><td>49.7</td><td>30.7</td><td>49.3</td><td>43.6</td></tr><tr><td>NLLB</td><td>49.3</td><td>50.3</td><td>34.8</td><td>55.1</td><td>47.4</td></tr><tr><td>TI</td><td>50.6</td><td>52.1</td><td>37.0</td><td>56.7</td><td>49.1</td></tr><tr><td>GPT-3.5</td><td>48.8</td><td>49.1</td><td>32.5</td><td>53.6</td><td>46.0</td></tr></table>
+
+<table><tr><td colspan="6">en→xx</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>42.0</td><td>55.6</td><td>23.8</td><td>54.6</td><td>44.0</td></tr><tr><td>NLLB</td><td>46.8</td><td>56.2</td><td>24.8</td><td>54.5</td><td>45.6</td></tr><tr><td>TI</td><td>47.0</td><td>57.1</td><td>29.5</td><td>52.8</td><td>46.6</td></tr><tr><td>GPT-3.5</td><td>47.8</td><td>56.6</td><td>25.6</td><td>56.6</td><td>46.7</td></tr></table>
+
+Table 16: BLEU on FLORES without added noise.  
+
+<table><tr><td colspan="6">xx→en</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>66.4</td><td>69.1</td><td>55.8</td><td>69.4</td><td>65.2</td></tr><tr><td>NLLB</td><td>69.2</td><td>69.9</td><td>58.6</td><td>72.9</td><td>67.7</td></tr><tr><td>TI</td><td>69.9</td><td>70.6</td><td>60.3</td><td>73.9</td><td>68.7</td></tr><tr><td>GPT-3.5</td><td>69.8</td><td>69.7</td><td>58.2</td><td>72.7</td><td>67.6</td></tr></table>
+
+<table><tr><td colspan="6">en→xx</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>62.9</td><td>71.7</td><td>36.2</td><td>71.3</td><td>60.5</td></tr><tr><td>NLLB</td><td>65.7</td><td>71.8</td><td>36.7</td><td>71.0</td><td>61.3</td></tr><tr><td>TI</td><td>66.2</td><td>72.3</td><td>38.7</td><td>70.3</td><td>61.9</td></tr><tr><td>GPT-3.5</td><td>67.1</td><td>72.6</td><td>35.1</td><td>72.8</td><td>61.9</td></tr></table>
+
+Table 17:chrF on FLORES without added noise.
+
+# H Synthetic Results with Other Metrics
+
+The models' synthetic performance on clean data is shown in terms of  $\mathrm{BLEU}^{15}$  in Table 16 and in  $\mathrm{chrF}^{16}$  in Table 17. Synthetic results showing BLEU-slope are shown in Tables 18 and 19. Synthetic results showing chrF-slope are shown in Tables 20 and 21. BLEU-slope and chrF-slope are computed analogously to COMET-slope.
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-51.4</td><td>-58.2</td><td>-28.2</td><td>-56.2</td><td>-48.5</td></tr><tr><td>NLLB</td><td>-33.4</td><td>-35.8</td><td>-30.6</td><td>-37.5</td><td>-34.3</td></tr><tr><td>TI</td><td>-37.9</td><td>-38.8</td><td>-30.4</td><td>-39.6</td><td>-36.7</td></tr><tr><td>GPT-3.5</td><td>-10.9</td><td>-14.4</td><td>-27.4</td><td>-17.2</td><td>-17.5</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-45.6</td><td>-49.6</td><td>-26.4</td><td>-49.1</td><td>-42.7</td></tr><tr><td>NLLB</td><td>-29.8</td><td>-29.9</td><td>-26.1</td><td>-32.7</td><td>-29.6</td></tr><tr><td>TI</td><td>-29.5</td><td>-29.7</td><td>-25.6</td><td>-30.0</td><td>-28.7</td></tr><tr><td>GPT-3.5</td><td>-14.4</td><td>-13.8</td><td>-24.9</td><td>-16.0</td><td>-17.3</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-34.3</td><td>-38.0</td><td>-8.8</td><td>-37.6</td><td>-29.7</td></tr><tr><td>NLLB</td><td>-13.9</td><td>-15.7</td><td>-11.5</td><td>-14.8</td><td>-14.0</td></tr><tr><td>TI</td><td>-9.9</td><td>-13.0</td><td>-5.5</td><td>-10.5</td><td>-9.7</td></tr><tr><td>GPT-3.5</td><td>-5.5</td><td>-5.3</td><td>-12.0</td><td>-5.8</td><td>-7.2</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-50.3</td><td>-57.3</td><td>-27.8</td><td>-57.2</td><td>-48.2</td></tr><tr><td>NLLB</td><td>-35.2</td><td>-36.4</td><td>-33.3</td><td>-40.3</td><td>-36.3</td></tr><tr><td>TI</td><td>-38.5</td><td>-42.7</td><td>-27.2</td><td>-46.3</td><td>-38.7</td></tr><tr><td>GPT-3.5</td><td>-18.3</td><td>-18.2</td><td>-22.5</td><td>-21.9</td><td>-20.2</td></tr></table>
+
+Table 18: BLEU-slope on FLORES for xx→en.  
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-45.2</td><td>-47.4</td><td>-32.9</td><td>-46.0</td><td>-42.9</td></tr><tr><td>NLLB</td><td>-25.3</td><td>-26.6</td><td>-27.7</td><td>-26.4</td><td>-26.5</td></tr><tr><td>TI</td><td>-28.4</td><td>-27.3</td><td>-29.0</td><td>-28.1</td><td>-28.2</td></tr><tr><td>GPT-3.5</td><td>-7.1</td><td>-9.6</td><td>-29.2</td><td>-10.9</td><td>-14.2</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-40.1</td><td>-39.7</td><td>-29.0</td><td>-38.7</td><td>-36.9</td></tr><tr><td>NLLB</td><td>-23.8</td><td>-23.6</td><td>-26.6</td><td>-24.1</td><td>-24.5</td></tr><tr><td>TI</td><td>-22.3</td><td>-20.9</td><td>-25.5</td><td>-21.1</td><td>-22.5</td></tr><tr><td>GPT-3.5</td><td>-10.0</td><td>-9.3</td><td>-26.0</td><td>-10.4</td><td>-13.9</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-22.9</td><td>-24.5</td><td>-6.8</td><td>-23.1</td><td>-19.3</td></tr><tr><td>NLLB</td><td>-7.6</td><td>-8.6</td><td>-6.1</td><td>-7.5</td><td>-7.4</td></tr><tr><td>TI</td><td>-5.1</td><td>-6.4</td><td>-3.9</td><td>-5.3</td><td>-5.2</td></tr><tr><td>GPT-3.5</td><td>-2.7</td><td>-2.7</td><td>-10.2</td><td>-2.8</td><td>-4.6</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>de→en</td><td>fr→en</td><td>ko→en</td><td>pt→en</td><td>avg.</td></tr><tr><td>OPUS</td><td>-43.6</td><td>-45.5</td><td>-34.5</td><td>-46.0</td><td>-42.4</td></tr><tr><td>NLLB</td><td>-26.6</td><td>-27.7</td><td>-31.0</td><td>-29.6</td><td>-28.7</td></tr><tr><td>TI</td><td>-28.6</td><td>-30.6</td><td>-26.3</td><td>-34.6</td><td>-30.0</td></tr><tr><td>GPT-3.5</td><td>-12.1</td><td>-11.9</td><td>-22.6</td><td>-14.0</td><td>-15.2</td></tr></table>
+
+Table 20:chrF-slope on FLORES for xx  $\rightarrow$  en.  
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-49.1</td><td>-62.6</td><td>-28.9</td><td>-64.5</td><td>-51.3</td></tr><tr><td>NLLB</td><td>-31.2</td><td>-33.8</td><td>-18.1</td><td>-37.1</td><td>-30.0</td></tr><tr><td>TI</td><td>-19.8</td><td>-20.7</td><td>-12.6</td><td>-20.0</td><td>-18.3</td></tr><tr><td>GPT-3.5</td><td>-7.8</td><td>-9.3</td><td>-7.1</td><td>-5.1</td><td>-7.3</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-47.8</td><td>-59.4</td><td>-27.9</td><td>-61.4</td><td>-49.1</td></tr><tr><td>NLLB</td><td>-30.7</td><td>-33.7</td><td>-16.6</td><td>-36.2</td><td>-29.3</td></tr><tr><td>TI</td><td>-21.9</td><td>-23.5</td><td>-13.7</td><td>-22.6</td><td>-20.4</td></tr><tr><td>GPT-3.5</td><td>-12.0</td><td>-13.4</td><td>-9.6</td><td>-12.8</td><td>-12.0</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-39.5</td><td>-45.9</td><td>-26.5</td><td>-54.5</td><td>-41.6</td></tr><tr><td>NLLB</td><td>-9.3</td><td>-8.9</td><td>-3.6</td><td>-14.7</td><td>-9.1</td></tr><tr><td>TI</td><td>-4.6</td><td>-5.0</td><td>-3.5</td><td>-5.2</td><td>-4.6</td></tr><tr><td>GPT-3.5</td><td>-3.7</td><td>-4.0</td><td>-3.2</td><td>-3.1</td><td>-3.5</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-48.8</td><td>-62.9</td><td>-29.0</td><td>-64.6</td><td>-51.3</td></tr><tr><td>NLLB</td><td>-34.1</td><td>-37.4</td><td>-20.1</td><td>-40.3</td><td>-33.0</td></tr><tr><td>TI</td><td>-25.0</td><td>-26.9</td><td>-15.3</td><td>-26.8</td><td>-23.5</td></tr><tr><td>GPT-3.5</td><td>-11.8</td><td>-13.1</td><td>-10.5</td><td>-12.5</td><td>-12.0</td></tr></table>
+
+Table 19: BLEU-slope on FLORES for en  $\rightarrow$  xx.  
+
+<table><tr><td colspan="6">swap</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-50.0</td><td>-56.8</td><td>-38.0</td><td>-55.7</td><td>-50.1</td></tr><tr><td>NLLB</td><td>-24.7</td><td>-26.4</td><td>-21.0</td><td>-27.4</td><td>-24.9</td></tr><tr><td>TI</td><td>-15.9</td><td>-15.0</td><td>-11.8</td><td>-14.8</td><td>-14.4</td></tr><tr><td>GPT-3.5</td><td>-5.1</td><td>-6.0</td><td>-6.4</td><td>-5.1</td><td>-5.7</td></tr><tr><td colspan="6">drop</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-48.3</td><td>-53.3</td><td>-36.6</td><td>-53.5</td><td>-47.9</td></tr><tr><td>NLLB</td><td>-24.7</td><td>-26.6</td><td>-20.3</td><td>-27.2</td><td>-24.7</td></tr><tr><td>TI</td><td>-17.4</td><td>-17.0</td><td>-12.7</td><td>-16.6</td><td>-15.9</td></tr><tr><td>GPT-3.5</td><td>-8.3</td><td>-9.0</td><td>-8.4</td><td>-8.2</td><td>-8.5</td></tr><tr><td colspan="6">dupe</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-34.7</td><td>-36.8</td><td>-33.6</td><td>-41.8</td><td>-36.7</td></tr><tr><td>NLLB</td><td>-5.7</td><td>-5.8</td><td>-5.7</td><td>-8.6</td><td>-6.4</td></tr><tr><td>TI</td><td>-2.8</td><td>-2.6</td><td>-2.8</td><td>-2.7</td><td>-2.7</td></tr><tr><td>GPT-3.5</td><td>-1.9</td><td>-2.1</td><td>-2.5</td><td>-1.3</td><td>-2.0</td></tr><tr><td colspan="6">key</td></tr><tr><td>Model</td><td>en→de</td><td>en→fr</td><td>en→ko</td><td>en→pt</td><td>avg.</td></tr><tr><td>OPUS</td><td>-50.0</td><td>-56.6</td><td>-37.9</td><td>-55.9</td><td>-50.1</td></tr><tr><td>NLLB</td><td>-27.9</td><td>-29.8</td><td>-22.7</td><td>-30.4</td><td>-27.7</td></tr><tr><td>TI</td><td>-21.0</td><td>-20.6</td><td>-15.3</td><td>-20.5</td><td>-19.4</td></tr><tr><td>GPT-3.5</td><td>-8.4</td><td>-9.0</td><td>-8.8</td><td>-8.1</td><td>-8.6</td></tr></table>
+
+Table 21:chrF-slope on FLORES for en  $\rightarrow$  xx.

2025/Did Translation Models Get More Robust Without Anyone Even Noticing_/images.zip

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2025/DiffPO_ Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models/46b5b625-0dc0-459e-9994-9544997fc1e4_origin.pdf

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2025/DiffPO_ Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models/full.md

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+# DIFFPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models
+
+Ruizhe Chen  $^{1,2,5}$   
+Joey Tianyi Zhou
+
+Wenhao Chai 3 Tony Quek 3,*
+
+Zhifei Yang Soujanya Poria 7,*
+
+Xiaotian Zhang<sup>1</sup>  
+Zuozhu Liu<sup>1,5,*†</sup>
+
+<sup>1</sup> Zhejiang University <sup>2</sup> SUTD <sup>3</sup> Princeton University <sup>4</sup> Peking University <sup>5</sup> Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence <sup>6</sup> A*STAR Centre for Frontier AI Research <sup>7</sup> Nanyang Technological University
+
+# Abstract
+
+Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion-styled Preference Optimization (DIFFPO), which provides an efficient and policy-agnostic solution for aligning LLMs with humans. By directly performing alignment at sentence level, DIFFPO avoids the time latency associated with token-level generation. Designed as a plug-and-play module, DIFFPO can be seamlessly integrated with various base models to enhance their alignment. Extensive experiments on AlpacaEval 2, MT-bench, and HH-RLHF demonstrate that DIFFPO achieves superior alignment performance across various settings, achieving a favorable trade-off between alignment quality and inference-time latency. Furthermore, DIFFPO demonstrates model-agnostic scalability, significantly improving the performance of large models such as Llama-3-70B. Our model and code are available here.
+
+# 1 Introduction
+
+The alignment of large language models (LLMs) with human preferences has recently emerged as a focal area of research (Wang et al., 2023; Shen et al., 2023). Prominent techniques such as Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022) and Direct Preference Optimization (DPO) (Rafailov et al., 2024) have demonstrated substantial efficacy. However, these methods require the optimization of individual policies, posing challenges such as high consumption of training resources. Inference-time alignment (Mudgal et al., 2023; Han et al., 2024)
+
+![](images/299cf910ef945817af000544999f785142a34dc023f0a9ff2dc5ea22bdb3b164.jpg)  
+Figure 1: Comparison with Inference-Time Methods. Points closer to the top-right indicate a superior trade-off between performance and inference time.
+
+provides an efficient alternative through direct adjustment of the model's output distribution, thus avoiding the need for resource-intensive retraining. Despite its advantages, this approach still requires policy-specific value functions, limiting its scalability across different models. Additionally, the inference-time latency remains high, presenting further challenges to its practical deployment.
+
+In this paper, we investigate an efficient and policy-agnostic preference optimization method. We begin by reconsidering the objective of aligning with humans (Yao et al., 2023; Shen et al., 2023). As illustrated in Fig. 2(a), the alignment process operates at the sentence level, focusing on adjusting key components of the generated content, such as style or format, to better reflect human intentions or values. Inspired by the global controllability of the diffusion process (Li et al., 2022; Lyu et al., 2023), we propose Diffusion-styled Preference Optimization (DIFFPO). DIFFPO draws an analogy from the diffusion-based denoising process to model the iterative adjustment required for aligning human preferences, as shown in Fig. 2(b). By employing parallel decoding (Santilli et al., 2023; Leviathan et al., 2023), DIFFPO directly predicts sentence
+
+![](images/9ee1ddfed1da980e0f3e80a1abd0c940b45b7f0acf97ebd30a772b0f92d84a50.jpg)  
+(a) Alignment Objectives
+
+![](images/707fae990c4eb3b300255efc57b79101300cbfe2ef99835631464def1f7dac80.jpg)  
+(b) DIFFPO Framework
+
+![](images/66f4f4f0e14b5324a278eb528c2a78b2e3834f94b5c159017d5d8a872a40c516.jpg)  
+(c) Inference  
+Figure 2: Illustration of the DIFFPO Framework. (a) The objective of LLM alignment is to adjust the output of LLMs to reflect human values and intentions. In this process, preferences are considered at the sentence level, focusing on aspects such as the style and format of the complete output. (b) We propose Diffusion-style Preference Optimization (DIFFPO), which reconceptualizes the alignment process as a sentence-level denoising process, where the goal is to transform an unaligned sentence  $\mathbf{y}^{(0)}$  into an aligned sentence  $\mathbf{y}^{(T)}$  step by step. (c) Designed as a plug-and-play module, DIFFPO can be directly integrated with the base model output and yield better alignment.
+
+level transitions, thus avoiding the time latency associated with token-level generation. During the training phase, we optimize the DIFFPO with an objective that maps generations with varying alignment levels to an aligned target, making it a policy-agnostic, plug-and-play module. The optimized DIFFPO can then be seamlessly integrated with the output of the base model, enhancing its alignment level, as demonstrated in Fig. 2(c).
+
+We evaluate the performance of DIFFPO on several benchmark datasets, including AlpacaEval 2 (Dubois et al., 2024), MT-bench (Zheng et al., 2023), and HH-RLHF (Bai et al., 2022). Empirical results demonstrate that DIFFPO achieves superior alignment performance across various base models and settings. Compared to inference-time alignment techniques, DIFFPO strikes an optimal trade-off between alignment performance and inference-time latency, as shown in Fig. 1. Additional experiments highlight the model-agnostic scalability of DIFFPO across different base models.
+
+Specifically, DIFFPO-9B significantly enhances the performance of models such as Llama-3-70B and GPT-4o, showcasing its capability to improve weak-to-strong supervision.
+
+The advantages of DIFFPO can be summarized as:
+
+- Model-agnostic. DIFFPO is optimized to learn sentence-level refinement, independent of the specific base LLMs. This allows it to be applied across a variety of base LLMs. Furthermore, DIFFPO does not require access to model parameters, which enhances its compatibility with API-based models and existing preference-aligned models.  
+- Training and Inference Efficiency. As a post-inference alignment strategy, DIFFPO adopts a one-for-all approach: it involves training one single DIFFPO and applying it for all base models, thus significantly reducing the resource intensiveness associated with policy optimization. Moreover, by framing alignment as sentence-level prediction,
+
+DIFFPO bypasses the time latency associated with token-level generation, thereby improving inference-time efficiency.
+
+# 2 Method
+
+# 2.1 Preliminaries: Large Language Models
+
+Next-Token Prediction. The text generation of autoregressive large language models (LLMs) with prompt  $\mathbf{x}$  and response  $\mathbf{y}$  can be modelled as a next-token prediction process. Given the input  $\mathbf{x}$ , The language model  $\pi (\cdot |\mathbf{x})$  autoregressively maps from current tokens  $(\mathbf{x},\mathbf{y}_{1:n - 1})$  to a distribution over the next token  $\mathbf{y}_n$ . The maximum token,  $N$ , sets the length limit for LLM outputs, which conclude with an end-of-sentence (EoS) token  $\mathbf{y}_N = \mathrm{EoS}$  that ends the generation. The generated output  $\mathbf{y}$  consists of predicted tokens  $(\mathbf{y}_1,\mathbf{y}_2,\dots ,\mathbf{y}_N)$ .
+
+Alignment of LLMs. During the alignment of LLMs, the objective is to optimize a language model  $\pi_{\theta}$  that maximizes the user's preference (Christiano et al., 2017; Ouyang et al., 2022; Rafailov et al., 2024):
+
+$$
+\begin{array}{l} \max  _ {\pi_ {\theta}} \mathbb {E} _ {x \sim D, \mathbf {y} \sim \pi_ {\theta} (\mathbf {y} | \mathbf {x})} [ p (\mathbf {y} \succ \mathbf {y} ^ {\prime} | \mathbf {x}) \\ - \beta D _ {K L} \left(\pi_ {\theta} \| \pi_ {\text {r e f}}\right) ], \tag {1} \\ \end{array}
+$$
+
+where  $p(\mathbf{y} \succ \mathbf{y}'|\mathbf{x})$  represents the preference, i.e., the probability that  $\mathbf{y}$  is preferred over  $\mathbf{y}'$  given the context  $\mathbf{x}$ , which can be generally represented by the reward function  $r$ . The parameter  $\beta$  controls the deviation from the reference policy  $\pi_{\mathrm{ref}}$ , which generally corresponds to the SFT model.
+
+Parallel Decoding of LLMs. In comparison to next-token prediction, where token-level generation is performed sequentially to obtain a sentence, parallel decoding has demonstrated the capacity by enabling sentence-level generation and improving content quality (Santilli et al., 2023; Leviathan et al., 2023). Concretely, supposing
+
+$$
+f (\mathbf {y} _ {n}, \mathbf {y} _ {<   n}, \mathbf {x}) := \mathbf {y} _ {n} - \underset {\mathbf {y}} {\arg \max} \pi (\mathbf {y} | \mathbf {y} _ {<   n}, \mathbf {x}),
+$$
+
+parallel decoding re-frames the LLM inference process as solving a system of nonlinear equations w.r.t. all tokens in a sentence  $\mathbf{y}_n$  for  $n = 1,\dots ,N$
+
+It can be solved in a parallel and iterative way:
+
+$$
+\left\{ \begin{array}{l} \mathbf {y} _ {1} ^ {(t + 1)} = \arg \max  _ {\mathbf {y}} \pi (\mathbf {y} \mid \mathbf {x}) \\ \mathbf {y} _ {2} ^ {(t + 1)} = \arg \max  _ {\mathbf {y}} \pi (\mathbf {y} \mid \mathbf {y} _ {1} ^ {(t)}, \mathbf {x}) \\ \vdots \\ \mathbf {y} _ {N} ^ {(t + 1)} = \arg \max  _ {\mathbf {y}} \pi (\mathbf {y} \mid \mathbf {y} _ {<   N} ^ {(t)}, \mathbf {x}) \end{array} \right. \tag {2}
+$$
+
+In this way, for one forward pass of the LLM at time  $t$ , we can obtain the next sentence  $\mathbf{y}^{(t + 1)}$  based on the previous one  $\mathbf{y}^{(t)}$ .
+
+# 2.2 Diffusion-styled Preference Optimization
+
+Motivation. The goal of LLM alignment is to align the outputs of LLMs with human values or intentions (Yao et al., 2023). In this process, preferences are defined at the sentence-level, focusing on the style or format of complete generated answers, as illustrated in Fig. 2(a). However, the generation of these responses occurs at the token level, following the next-token prediction pattern inherent in LLM modeling. This requires existing alignment techniques to optimize preferences (or rewards) at the token-level, which complicates the learning process (Andrychowicz et al., 2017; Zhong et al., 2024; Zeng et al., 2024). This inconsistency prompts us to reconsider the formulation of the alignment process.
+
+Reformulation. Inspired by the potential benefits of the diffusion process in controllable text generation (Gong et al., 2022; Han et al., 2022; Ye et al., 2024b), we draw an analogy between the aligning LLMs and the diffusion process. Specifically, we propose Diffusion-styled Preference Optimization (DIFFPO), which reconceptualizes alignment as a sentence-level denoising process. The denoising process  $\pi$  gradually refines the initial unaligned output  $\mathbf{y}^{(0)}$  by adjusting the format or style as a whole. This process ultimately produces the aligned output  $\mathbf{y}^{(T)}$ , as illustrated in Fig. 2(b). The sentence-level alignment process can be formulated as follows:
+
+$$
+\pi \left(\mathbf {y} ^ {(0: T)}\right) := p \left(\mathbf {y} ^ {(0)}\right) \prod_ {t = 1} ^ {T} \pi \left(\mathbf {y} ^ {(t)} \mid \mathbf {y} ^ {(t - 1)}, \mathbf {x}\right), \tag {3}
+$$
+
+where  $\mathbf{y}^{(0)}$  and  $\mathbf{y}^{(T)}$  represent the initial unaligned and final aligned generations, respectively. The intermediate sequence  $\mathbf{y}^{(1:T-1)}$  can be viewed as the unaligned generations progressively transitioning along the trajectory from  $\mathbf{y}^{(0)}$  to  $\mathbf{y}^{(T)}$ .
+
+Assuming the existence of a reward model  $r(\mathbf{x},\mathbf{y})$  , which captures how well the generated output  $\mathbf{y}$  aligns with human preferences given the input  $\mathbf{x}$  , the goal is to optimize a DIFFPO model  $\pi_{\theta}$  . This model learns to take a sentence as input and predict the next sentence with a higher reward, as illustrated in Fig. 2(c). The goal can be expressed as follows:
+
+$$
+\pi_ {\theta} (\mathbf {y} ^ {(t)} | \mathbf {y} ^ {(t - 1)}, \mathbf {x}) \propto p (\mathbf {y} ^ {(t - 1)}, \mathbf {x}) \mathrm {e x p} (r (\mathbf {x}, \mathbf {y} ^ {(t)})).
+$$
+
+By employing parallel decoding, the DIFFPO model directly performs sentence-level predictions.
+
+# 2.3 Consistency Optimization of DIFFPO
+
+Inspired by Consistency LLMs (Kou et al., 2024), we propose to consistently map any intermediate (unaligned) generation  $\mathbf{y}^{(t)}$  to the aligned generation  $\mathbf{y}^{(T)}$ . We jointly optimize the DIFFPO model  $\pi_{\theta}$  with two losses: one aligns the intermediate generation with the aligned generation, and the other prevents the corruption of the autoregressive (AR) modeling in the base model, thereby maintaining the generation quality.
+
+Consistency Loss. For a prompt  $\mathbf{x}$  with an unaligned generation  $\mathbf{y}^{(t)}$ , we directly guide the model to output  $\mathbf{y}^{(T)}$  with  $\mathbf{y}^{(t)}$  as the input by minimizing the following loss  $L_{\mathrm{Con}} =$
+
+$$
+\mathbb {E} _ {(\mathbf {x}, \mathbf {y} ^ {(t)}, \mathbf {y} ^ {(T)}) \sim \mathcal {D}} \left[ \sum_ {i = 1} ^ {N} \mathrm {K L} \left(\pi_ {\theta^ {-}} \left(\mathbf {y} _ {<   i} ^ {(T)}, \mathbf {x}\right) \| \pi_ {\theta} \left(\mathbf {y} _ {<   i} ^ {(t)}, \mathbf {x}\right)\right) \right. \tag {4}
+$$
+
+where  $\theta^{-} = \mathrm{stopgrad}(\theta)$  and  $N$  denotes the length of generation.  $\mathrm{KL}(\cdot |\cdot)$  denotes the forward KL distance between two distributions.
+
+AR Loss. To prevent the corruption of the autoregressive (AR) modeling in the base model and maintain the generation quality, we incorporate the AR loss based on the generated sequence  $\mathbf{y}^{(T)}$ :
+
+$$
+L _ {\mathrm {A R}} = \mathbb {E} _ {(\mathbf {x}, \mathbf {y} ^ {(T)}) \sim \mathcal {D}} \left[ - \sum_ {i = 1} ^ {N} \log \pi_ {\theta} \left(\mathbf {y} _ {i} ^ {(T)} \mid \mathbf {y} _ {<   i} ^ {(T)}, \mathbf {x}\right) \right]. \tag {5}
+$$
+
+The total loss with weight  $\omega$  is:
+
+$$
+L (\theta) = L _ {\mathrm {A R}} + \omega L _ {\mathrm {C o n}}. \tag {6}
+$$
+
+# 2.4 The Objective of DIFFPO within RLHF
+
+In this section, we analyze the role of DIFFPO in achieving the goal of RLHF. We start with the same RL objective as prior work, Eq. 1, under a general reward function  $r^*$ . Following prior work (Peng et al., 2019; Rafailov et al., 2024), the optimal solution to the KL-constrained reward maximization objective in Eq. 1 takes the form:  $r^*(\mathbf{x}, \mathbf{y}) = \beta \log \left( \frac{\pi^*(\mathbf{y}|\mathbf{x})}{\pi_{\mathrm{ref}}(\mathbf{y}|\mathbf{x})} \right) + \beta \log Z(\mathbf{x})$ , where  $Z(\mathbf{x}) = \sum_{\mathbf{y}} \pi_{\mathrm{ref}}(\mathbf{y}|\mathbf{x}) \exp \left( \frac{1}{\beta} r^*(\mathbf{x}, \mathbf{y}) \right)$  is the partition function. With Bradley-Terry model, we can represent the preference function as the difference of rewards for a preferred answer  $\mathbf{y}_w$  and a dispreferred answer  $\mathbf{y}_l$ :
+
+$$
+\begin{array}{l} p \left(\mathbf {y} _ {w} \succ \mathbf {y} _ {l} | \mathbf {x}\right) = \sigma \left(r ^ {*} (\mathbf {x}, \mathbf {y} _ {w}) - r ^ {*} (\mathbf {x}, \mathbf {y} _ {l})\right) \\ = \sigma \left(\beta \log \frac {\pi^ {*} (\mathbf {y} _ {w} \mid \mathbf {x})}{\pi_ {\mathrm {r e f}} (\mathbf {y} _ {w} \mid \mathbf {x})} - \beta \log \frac {\pi^ {*} (\mathbf {y} _ {l} \mid \mathbf {x})}{\pi_ {\mathrm {r e f}} (\mathbf {y} _ {l} \mid \mathbf {x})}\right). \\ \end{array}
+$$
+
+Substitute by  $\pi^{*}(\mathbf{y}\mid \mathbf{x}) = \pi_{\mathbf{DIFFPO}}(\mathbf{y}\mid \mathbf{y}^{\prime},x)$ $\pi_{\mathrm{ref}}(\mathbf{y}'\mid \mathbf{x})$  , we obtain  $p(\mathbf{y}_w\succ \mathbf{y}_l|\mathbf{x})$  equals to
+
+$$
+\sigma \left(\beta \log \frac {\pi_ {\mathbf {D I F F P O}} (\mathbf {y} _ {w} \mid \mathbf {y} _ {l} , \mathbf {x})}{\pi_ {\mathbf {D I F F P O}} (\mathbf {y} _ {l} \mid \mathbf {y} _ {l} , \mathbf {x})} - \beta \log \frac {\pi_ {\text {r e f}} (\mathbf {y} _ {w} \mid \mathbf {x})}{\pi_ {\text {r e f}} (\mathbf {y} _ {l} \mid \mathbf {x})}\right).
+$$
+
+Note that the first term in Eq. 7 is optimized through the consistency loss in Eq. 4 by maximizing the probability of predicting  $\mathbf{y}_w$ . The second term depends only on  $\mathbf{x}$ , with  $\pi_{\mathrm{ref}}$  remaining constant. Moreover, the deviation from the base policy can be easily controlled, since  $\mathbf{y}_w$  is derived from  $\mathbf{y}_l$ .
+
+In summary, the objective of DIFFPO as defined in Eq. 6 aligns with the RLHF objective in Eq. 1. Furthermore, since  $\pi_{\mathrm{DIFFPO}}$  is optimized independently from the base model  $\pi_{\mathrm{ref}}$ , it can be deployed in a model-agnostic manner.
+
+# 2.5 Practical Implementations
+
+Generate Alignment Trajectories. To implement DIFFPO, we collect the alignment trajectory for each prompt, thereby forming an original training set  $\mathcal{D}$ . Specifically, for each prompt  $\mathbf{x}$  from the UltraFeedback dataset (Cui et al., 2023), we generate  $T$  responses using different base models. We then employ ArmoRM (Wang et al., 2024) reward model to score these responses. The response with the highest score is selected as  $\mathbf{y}^{(T)}$ . The remaining five responses are ranked based on their scores to form  $\mathbf{y}^{(0:T-1)}$ .  $T$  is set to 6.
+
+Training and Inference. During the training phase, we initialize our aligning model  $\pi_{\theta}$  using three backbones of varying sizes: Gemma-2-it-2B/9B, and Llama-3-8B-Instruct. The DIFFPO model is optimized adhering to the optimization loss in Eq. 6 with parameters  $N = 256$  and  $w = 10^{3}$ . Given the variable lengths of generations in  $\mathcal{D}$ , we standardize their lengths through padding or truncation. In the inference phase, the optimized model  $\pi_{\theta}^{*}$  is employed to align responses from the vanilla generations produced by base models. Appendix B.1 shows more implementation details.
+
+# 3 Experiment
+
+# 3.1 Experiment Setup
+
+Evaluation Benchmarks and Metrics. We conduct our experiments using two widely recognized benchmarks for open-ended instruction-following: MT-Bench (Zheng et al., 2023) and AlpacaEval 2 (Dubois et al., 2024). These benchmarks are designed to evaluate the conversational abilities of models across a diverse set of queries. AlpacaEval 2 includes 805 questions drawn from five distinct datasets, while MT-Bench covers eight categories and comprises a total of 80 questions. Additionally, we employ the HH-RLHF (Bai et al., 2022) datasets to assess how well the models' generative capabilities align with human values, particularly emphasizing helpfulness and harmlessness. We adhere to each benchmark's specific evaluation protocol to report scores. In AlpacaEval 2, we report both the raw win rate (WR) and the length-controlled win rate (LC), comparing performance against the GPT-4 model. In contrast, we present the average score for MT-Bench, also utilizing GPT-4 as the judge model. For HH-RLHF, we report scores that reflect the models' helpfulness and harmlessness, as well as the overall score. These scores are measured using ArmoRM (Wang et al., 2024), a state-of-the-art reward model from RewardBench (Lambert et al., 2024), designed to align with human preferences.
+
+Baselines. We compare DIFFPO with two primary categories of offline preference optimization methods. In the category of training-based methods: Direct Preference Optimization (DPO) (Rafailov et al., 2024) reparameterizes reward functions to simplify and stabilize the preference learning process. SimPO (Meng et al., 2024) utilizes the average log probability of a sequence as an implicit reward, aligning more closely with model generation. For training-free methods:
+
+Black-Box Prompt Optimization (BPO) (Cheng et al., 2024) adapts user prompts to better align with LLMs' input comprehension, achieving user intents optimally without altering LLM parameters. ARGs (Khanov et al., 2024) integrates alignment into the decoding process through reward-guided search, eliminating the need for costly RL training. Best-of-N sampling (BoN) (Nakano et al., 2021) samples N times and selects the highest-scoring sample based on the reward model, with N set to 4 in our experiments using ArmoRM (Wang et al., 2024) as the reward model. Furthermore, Aligner (Ji et al., 2024) and MetaAligner (Yang et al., 2024a) employ an additional model to learn corrective residuals between preferred and dispreferred responses to refine model generation.
+
+Base Models and Inference Settings. We perform preference optimization primarily on two model families: Llama-3-8B (AI@Meta, 2024) and Mistral-7B (Jiang et al., 2023), under two configurations: SFT and Instruct. In the SFT configuration, we utilize open-source models from SimPO (Meng et al., 2024) that follow Zephyr (Tunstall et al., 2023) to train the base models (i.e., meta-llama/Meta-Llama-3-8B) on the UltraChat-200k (Ding et al., 2023) dataset to derive an SFT model. For the Instruct configuration, we employ an off-the-shelf instruction-tuned models (i.e., meta-llama/Meta-Llama-3-8B-Instruct). To further validate scalability, we conduct additional experiments using the Llama-3.2 series, Qwen-2.5 series (Team, 2024), and GPT4o (Achiam et al., 2023) as the base models.
+
+During the inference phase of DIFFPO, we initially generate responses using the base models. For each benchmark. In AlpacaEval 2 and HHRLHF, we employ a sampling decoding strategy with a temperature setting of 0.7. For MT-Bench, we adhere to the official decoding configuration, which specifies varying temperatures for different categories. In our primary experiments, we set the maximum token generation length to 256. Results for experiments conducted at various lengths are provided in Tab. 4. Subsequently, the responses generated by the base models are aligned using the trained DIFFPO. For the main results, parallel decoding is executed with a block size of 256.
+
+# 3.2 Experiment Results
+
+DIFFPO significantly outperforms existing preference optimization methods. As shown in Ta
+
+<table><tr><td rowspan="3">Method</td><td colspan="5">Llama-3-SFT (8B)</td><td colspan="5">Llama-3-Instruct (8B)</td></tr><tr><td>MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="2">HH-RLHF</td><td>MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="2">HH-RLHF</td></tr><tr><td>GPT-4</td><td>LC (%)</td><td>WR (%)</td><td>Helpful</td><td>Harmless</td><td>GPT-4</td><td>LC (%)</td><td>WR (%)</td><td>Helpful</td><td>Harmless</td></tr><tr><td>Base Model</td><td>6.21</td><td>22.09</td><td>20.81</td><td>0.59</td><td>0.91</td><td>6.78</td><td>36.83</td><td>42.12</td><td>0.67</td><td>0.93</td></tr><tr><td>w. DPO</td><td>6.59</td><td>29.84</td><td>36.77</td><td>0.68</td><td>0.89</td><td>6.90</td><td>47.20</td><td>53.56</td><td>0.74</td><td>0.92</td></tr><tr><td>w. SimPO</td><td>6.62</td><td>32.27</td><td>40.96</td><td>0.66</td><td>0.86</td><td>7.05</td><td>52.57</td><td>58.33</td><td>0.75</td><td>0.92</td></tr><tr><td>w. BPO</td><td>5.84</td><td>21.34</td><td>22.33</td><td>0.60</td><td>0.92</td><td>6.43</td><td>22.39</td><td>34.06</td><td>0.67</td><td>0.92</td></tr><tr><td>w. ARGs</td><td>6.14</td><td>9.06</td><td>13.97</td><td>0.49</td><td>0.86</td><td>6.84</td><td>31.83</td><td>34.74</td><td>0.64</td><td>0.89</td></tr><tr><td>w. BoN</td><td>6.79</td><td>35.14</td><td>32.26</td><td>0.62</td><td>0.92</td><td>6.89</td><td>45.10</td><td>49.94</td><td>0.67</td><td>0.92</td></tr><tr><td>w. Aligner</td><td>4.88</td><td>20.41</td><td>17.15</td><td>0.60</td><td>0.91</td><td>4.82</td><td>32.53</td><td>32.69</td><td>0.67</td><td>0.96</td></tr><tr><td>w. MetaAligner</td><td>4.46</td><td>19.81</td><td>18.23</td><td>0.52</td><td>0.89</td><td>4.50</td><td>20.75</td><td>19.08</td><td>0.52</td><td>0.91</td></tr><tr><td>w. DIFFPO-8B</td><td>6.96</td><td>36.24</td><td>40.96</td><td>0.62</td><td>0.93</td><td>7.02</td><td>36.44</td><td>41.01</td><td>0.68</td><td>0.93</td></tr><tr><td>w. DIFFPO-9B</td><td>7.45</td><td>49.72</td><td>54.23</td><td>0.71</td><td>0.98</td><td>7.40</td><td>55.84</td><td>61.88</td><td>0.72</td><td>0.98</td></tr><tr><td colspan="6">Mistral-SFT (7B)</td><td colspan="5">Mistral-Instruct (7B)</td></tr><tr><td rowspan="2">Method</td><td>MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="2">HH-RLHF</td><td>MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="2">HH-RLHF</td></tr><tr><td>GPT-4</td><td>LC (%)</td><td>WR (%)</td><td>Helpful</td><td>Harmless</td><td>GPT-4</td><td>LC (%)</td><td>WR (%)</td><td>Helpful</td><td>Harmless</td></tr><tr><td>Base Model</td><td>5.73</td><td>20.15</td><td>17.24</td><td>0.56</td><td>0.87</td><td>6.39</td><td>32.81</td><td>34.86</td><td>0.66</td><td>0.94</td></tr><tr><td>w. DPO</td><td>5.91</td><td>31.28</td><td>32.65</td><td>0.66</td><td>0.91</td><td>6.29</td><td>35.60</td><td>37.73</td><td>0.67</td><td>0.92</td></tr><tr><td>w. SimPO</td><td>6.17</td><td>31.16</td><td>33.72</td><td>0.63</td><td>0.86</td><td>6.36</td><td>35.78</td><td>40.21</td><td>0.67</td><td>0.93</td></tr><tr><td>w. BPO</td><td>5.55</td><td>18.23</td><td>17.23</td><td>0.64</td><td>0.92</td><td>5.99</td><td>19.61</td><td>27.49</td><td>0.66</td><td>0.93</td></tr><tr><td>w. ARGs</td><td>5.12</td><td>11.07</td><td>13.95</td><td>0.55</td><td>0.87</td><td>6.20</td><td>26.60</td><td>29.68</td><td>0.66</td><td>0.92</td></tr><tr><td>w. BoN</td><td>6.21</td><td>33.36</td><td>27.74</td><td>0.64</td><td>0.94</td><td>6.40</td><td>34.75</td><td>39.24</td><td>0.68</td><td>0.94</td></tr><tr><td>w. Aligner</td><td>4.27</td><td>18.27</td><td>15.53</td><td>0.60</td><td>0.95</td><td>4.42</td><td>28.88</td><td>30.30</td><td>0.66</td><td>0.93</td></tr><tr><td>w. MetaAligner</td><td>4.08</td><td>12.40</td><td>9.72</td><td>0.51</td><td>0.85</td><td>3.71</td><td>18.55</td><td>16.91</td><td>0.55</td><td>0.91</td></tr><tr><td>w. DIFFPO-8B</td><td>6.87</td><td>34.42</td><td>40.08</td><td>0.62</td><td>0.88</td><td>7.04</td><td>35.92</td><td>40.70</td><td>0.68</td><td>0.92</td></tr><tr><td>w. DIFFPO-9B</td><td>7.13</td><td>48.99</td><td>52.87</td><td>0.70</td><td>0.96</td><td>7.33</td><td>56.22</td><td>61.71</td><td>0.72</td><td>0.98</td></tr></table>
+
+Table 1: Comparison results with baseline methods. DIFFPO achieves the superior alignment performance across all benchmarks, outperforming the training-based baselines (i.e., SimPO and DPO) in various settings. Notably, DIFFPO requires only a single training session and is applicable to multiple base models. The best result is highlighted in bold, while the second-best result is highlighted with underline.
+
+ble 1, while all preference optimization algorithms improve performance over the base model, DIFFPO achieves the best overall performance across all benchmarks and settings. These consistent and significant improvements underscore the robustness and effectiveness of DIFFPO. Notably, DIFFPO outperforms the training-based baselines (i.e., SimPO and DPO) across various settings, despite requiring only a single training session of DIFFPO model and being capable of enhancing the performance of multiple base models.
+
+DIFFPO consistently improves the performance of base models of various sizes. We report the performance of DIFFPO-2B and DIFFPO-9B on base models of various sizes, with the results presented in Table 2. The results demonstrate that both DIFFPO-2B and DIFFPO-9B lead to performance improvements across different base models. However, the performance gain of DIFFPO-2B is limited, showing notable improvements primarily for smaller models. In contrast, DIFFPO-9B enhances the performance of larger models, such as
+
+Qwen2.5-14B and 32B, as well as black-box GPT-4, exhibiting a weak-to-strong improvement pattern. Furthermore, the results show that DIFFPO can be effectively integrated with existing preference optimization methods, such as DPO and SimPO, further enhancing alignment performance. These results underscore the scalability of DIFFPO.
+
+DIFFPO achieves a surpassing performance-efficiency trade-off. We compare DIFFPO with existing inference-time alignment techniques, evaluating both alignment performance and execution time. The results are illustrated in Fig. 3, with the execution time measured on a single NVIDIA A100 80GB GPU. Points located closer to the top-right corner indicate a more favorable Pareto frontier. BoN and MetaAligner achieves commendable alignment performance and inference time respectively. However, when considering both aspects, DIFFPO demonstrates a surpassing performance-efficiency trade-off on all three datasets. The experiments are conducted on Llama-3-SFT.
+
+<table><tr><td rowspan="2">Base Models</td><td colspan="3">MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="3">HH-RLHF</td></tr><tr><td>1-Turn</td><td>2-Turn</td><td>Avg.</td><td>LC (%)</td><td>WR (%)</td><td>Overall</td><td>Helpful</td><td>Harmless</td></tr><tr><td>Llama-3.2-1B-Instruct</td><td>5.32</td><td>5.13</td><td>5.25</td><td>15.57</td><td>19.09</td><td>0.0955</td><td>0.5978</td><td>0.9313</td></tr><tr><td>w. DIFFPO-2B</td><td>6.97</td><td>5.97</td><td>6.47</td><td>39.42</td><td>44.20</td><td>0.1077</td><td>0.6948</td><td>0.9728</td></tr><tr><td>w. DIFFPO-9B</td><td>7.56</td><td>6.95</td><td>7.30</td><td>50.70</td><td>56.08</td><td>0.1130</td><td>0.7059</td><td>0.9770</td></tr><tr><td>Llama-3.2-3B-Instruct</td><td>6.84</td><td>6.06</td><td>6.45</td><td>33.41</td><td>37.43</td><td>0.1037</td><td>0.6533</td><td>0.9183</td></tr><tr><td>w. DIFFPO-2B</td><td>7.13</td><td>6.46</td><td>6.79</td><td>39.94</td><td>45.32</td><td>0.1069</td><td>0.6956</td><td>0.9682</td></tr><tr><td>w. DIFFPO-9B</td><td>7.58</td><td>7.00</td><td>7.36</td><td>54.06</td><td>59.30</td><td>0.1132</td><td>0.7106</td><td>0.9875</td></tr><tr><td>Llama-3-8B-SFT+DPO</td><td>6.70</td><td>6.48</td><td>6.59</td><td>29.84</td><td>36.77</td><td>0.1044</td><td>0.6814</td><td>0.8900</td></tr><tr><td>w. DIFFPO-9B</td><td>7.42</td><td>7.03</td><td>7.22</td><td>54.29</td><td>59.51</td><td>0.1134</td><td>0.7178</td><td>0.9765</td></tr><tr><td>Llama-3-8B-SFT+SimPO</td><td>6.63</td><td>6.61</td><td>6.62</td><td>32.27</td><td>40.96</td><td>0.1022</td><td>0.6640</td><td>0.8589</td></tr><tr><td>w. DIFFPO-9B</td><td>7.59</td><td>7.08</td><td>7.42</td><td>55.66</td><td>60.67</td><td>0.1121</td><td>0.7156</td><td>0.9638</td></tr><tr><td>Llama-3-8B-it+DPO</td><td>6.75</td><td>7.05</td><td>6.90</td><td>47.20</td><td>53.56</td><td>0.1120</td><td>0.7387</td><td>0.9154</td></tr><tr><td>w. DIFFPO-9B</td><td>7.79</td><td>6.98</td><td>7.39</td><td>58.56</td><td>63.60</td><td>0.1140</td><td>0.7211</td><td>0.9831</td></tr><tr><td>Llama-3-8B-it+SimPO</td><td>7.09</td><td>7.00</td><td>7.05</td><td>52.57</td><td>58.33</td><td>0.1143</td><td>0.7483</td><td>0.9182</td></tr><tr><td>w. DIFFPO-9B</td><td>7.43</td><td>7.22</td><td>7.33</td><td>59.66</td><td>65.32</td><td>0.1142</td><td>0.7229</td><td>0.9756</td></tr><tr><td>Llama-3-70B-Instruct</td><td>7.41</td><td>7.59</td><td>7.5</td><td>46.14</td><td>51.12</td><td>0.1087</td><td>0.6928</td><td>0.9163</td></tr><tr><td>w. DIFFPO-9B</td><td>8.23</td><td>7.28</td><td>7.75</td><td>58.18</td><td>62.34</td><td>0.1137</td><td>0.719</td><td>0.9757</td></tr><tr><td>Qwen2.5-3B-Instruct</td><td>6.73</td><td>5.59</td><td>6.16</td><td>35.52</td><td>40.42</td><td>0.1050</td><td>0.6802</td><td>0.9587</td></tr><tr><td>w. DIFFPO-2B</td><td>7.06</td><td>6.26</td><td>6.66</td><td>42.63</td><td>47.83</td><td>0.1065</td><td>0.6973</td><td>0.9771</td></tr><tr><td>w. DIFFPO-9B</td><td>7.58</td><td>7.24</td><td>7.41</td><td>55.71</td><td>61.43</td><td>0.1132</td><td>0.7106</td><td>0.9875</td></tr><tr><td>Qwen2.5-7B-Instruct</td><td>7.11</td><td>6.96</td><td>7.03</td><td>45.03</td><td>49.95</td><td>0.1095</td><td>0.6995</td><td>0.9442</td></tr><tr><td>w. DIFFPO-2B</td><td>7.01</td><td>6.34</td><td>6.67</td><td>43.89</td><td>49.07</td><td>0.1074</td><td>0.7013</td><td>0.9659</td></tr><tr><td>w. DIFFPO-9B</td><td>7.62</td><td>7.10</td><td>7.35</td><td>57.89</td><td>63.01</td><td>0.1117</td><td>0.7100</td><td>0.9445</td></tr><tr><td>Qwen2.5-14B-Instruct</td><td>7.24</td><td>6.71</td><td>6.98</td><td>51.60</td><td>57.10</td><td>0.1117</td><td>0.7100</td><td>0.9445</td></tr><tr><td>w. DIFFPO-2B</td><td>7.08</td><td>6.33</td><td>6.71</td><td>43.70</td><td>48.76</td><td>0.1078</td><td>0.7017</td><td>0.9704</td></tr><tr><td>w. DIFFPO-9B</td><td>7.62</td><td>7.35</td><td>7.48</td><td>55.13</td><td>60.65</td><td>0.1136</td><td>0.7185</td><td>0.9759</td></tr><tr><td>Qwen2.5-32B-Instruct</td><td>7.35</td><td>6.95</td><td>7.15</td><td>54.93</td><td>60.95</td><td>0.1132</td><td>0.7189</td><td>0.9594</td></tr><tr><td>w. DIFFPO-9B</td><td>7.58</td><td>7.63</td><td>7.60</td><td>55.13</td><td>61.94</td><td>0.1143</td><td>0.7248</td><td>0.9797</td></tr><tr><td>GPT-4o (API)</td><td>7.40</td><td>7.47</td><td>7.43</td><td>53.64</td><td>62.01</td><td>0.1119</td><td>0.6974</td><td>0.9669</td></tr><tr><td>w. DIFFPO-9B</td><td>7.66</td><td>7.37</td><td>7.51</td><td>58.91</td><td>64.30</td><td>0.1129</td><td>0.7167</td><td>0.9893</td></tr></table>
+
+Table 2: Performance of DIFFPO models. The results demonstrate that both DIFFPO-2B and DIFFPO-9B lead to performance improvements across different base models. DIFFPO-9B enhances the performance of larger models, such as Qwen2.5-14B and 32B, as well as black-box GPT-4o, exhibiting a weak-to-strong improvement pattern. Furthermore, the results show that DIFFPO can be effectively integrated with existing preference optimization methods, such as DPO and SimPO, further enhancing alignment performance.
+
+# 3.3 Analysis
+
+Performance Under Hybrid Decoding. We investigate the hybrid decoding strategy of DiffPO, with results provided in Tab 3. We segment the vanilla generation, which has a maximum length of 256, into blocks of varying sizes and sequentially apply DiffPO-8B to each block. This approach allows DiffPO decoding to be parallel within blocks and auto-regressive between blocks. It can be observed that hybrid decoding significantly reduces the decoding time, with optimal efficiency achieved at a block size of 32. On the other hand, performance is enhanced when the block size is set to 256, which corresponds to purely paral
+
+Ie1 decoding, indicating a feasible trade-off. The experiments are conducted on Llama-3-SFT.
+
+Scaling towards Longer Generation Lengths. We validate the scalability of the DIFFPO model in response to increasing generation lengths, with results presented in Tab 4. Using base models, we generate outputs on MT-Bench under various maximum length settings and observe a positive correlation between increased text length and higher scores. Subsequently, the same optimized DIFFPO-8B and 9B is applied to these outputs using the hybrid decoding strategy described in the previous section. This approach consistently
+
+![](images/a68790876fe7df6d2af5a0d7cbcec9a942cd997a78175d2d39bd7643b3df92da.jpg)  
+Figure 3: Comparison of Inference-Time Efficiency. We compare DIFFPO with existing inference-time alignment techniques, evaluating both alignment performance and execution time. Points located closer to the top-right corner indicate a better trade-off. When considering both aspects, DIFFPO demonstrates a surpassing performance-efficiency trade-off on all three datasets.
+
+![](images/4bf5d75314229d7477fdff1469486dba70f5a6e6315de020a5138e893c39e8b5.jpg)
+
+![](images/cea768e6b6db2ea85e231e93418898291e7c58d7b655fee69a3de4358b8d7080.jpg)
+
+<table><tr><td>Block Size</td><td>16</td><td>32</td><td>64</td><td>128</td><td>256</td></tr><tr><td>MT (GPT-4)</td><td>6.86</td><td>6.96</td><td>6.84</td><td>6.81</td><td>6.77</td></tr><tr><td>Time (s)</td><td>1080</td><td>1012</td><td>1390</td><td>1520</td><td>1937</td></tr><tr><td>AE2 (LC)</td><td>33.52</td><td>33.54</td><td>33.46</td><td>32.98</td><td>36.24</td></tr><tr><td>Time (s)</td><td>3712</td><td>3471</td><td>4614</td><td>5510</td><td>7520</td></tr><tr><td>HH (Avg.)</td><td>0.7742</td><td>0.7749</td><td>0.7743</td><td>0.7741</td><td>0.7761</td></tr><tr><td>Time (s)</td><td>1564</td><td>1551</td><td>1620</td><td>1816</td><td>2684</td></tr></table>
+
+yields enhanced alignment performance, demonstrating DIFFPO's robust scaling capabilities towards longer generation lengths.
+
+Loss and Hyperparameter Ablation. We evaluate the effectiveness of the training loss of DIFFPO in Section 2.3 and the inference strategy in Section 2.5. The results are presented in Table 5. We report on two decoding strategies: vanilla decoding of a single model and DIFFPO decoding, which applies the optimized DIFFPO-9B on the output of the base model. The findings indicate that applying DIFFPO to the base model achieves performance superior to that of single models alone, thus demonstrating the effectiveness of the DIFFPO strategy. Furthermore, we report the results of an ablation study on the hyperparameter  $w$  in Eq. 6. When using DIFFPO decoding, employing  $L_{\mathrm{Con}}$  with larger values of  $w$  lead to a more pronounced improvement in performance.
+
+Table 3: Performance Under Hybrid Decoding. We segment the vanilla generation into blocks of varying sizes and sequentially apply DIFFPO-8B to each block. This approach allows DIFFPO decoding to be parallel within blocks and auto-regressive between blocks. Hybrid decoding significantly reduces the decoding time, indicating a feasible trade-off for performance.  
+
+<table><tr><td>Generation Length</td><td>256</td><td>512</td><td>1,024</td><td>2,048</td></tr><tr><td>Llama-3-SFT</td><td>6.21</td><td>6.61</td><td>6.76</td><td>6.71</td></tr><tr><td>w. DIFFPO-8B (Δ)</td><td>+0.75</td><td>+0.81</td><td>+0.93</td><td>+1.05</td></tr><tr><td>w. DIFFPO-9B (Δ)</td><td>+1.24</td><td>+1.64</td><td>+1.48</td><td>+0.50</td></tr><tr><td>Llama-3-Instruct</td><td>6.78</td><td>7.87</td><td>7.99</td><td>8.00</td></tr><tr><td>w. DIFFPO-8B (Δ)</td><td>+0.24</td><td>-0.12</td><td>+0.01</td><td>+0.02</td></tr><tr><td>w. DIFFPO-9B (Δ)</td><td>+0.62</td><td>+0.68</td><td>+0.34</td><td>+0.62</td></tr><tr><td>Mistral-SFT</td><td>5.73</td><td>6.42</td><td>6.50</td><td>6.36</td></tr><tr><td>w. DIFFPO-8B (Δ)</td><td>+1.14</td><td>+1.16</td><td>+1.25</td><td>+1.51</td></tr><tr><td>w. DIFFPO-9B (Δ)</td><td>+1.40</td><td>+1.47</td><td>+1.71</td><td>+1.86</td></tr><tr><td>Mistral-Instruct</td><td>6.39</td><td>7.47</td><td>7.68</td><td>7.64</td></tr><tr><td>w. DIFFPO-8B (Δ)</td><td>+0.65</td><td>+0.06</td><td>+0.13</td><td>+0.17</td></tr><tr><td>w. DIFFPO-9B (Δ)</td><td>+0.94</td><td>+0.59</td><td>+0.58</td><td>+0.78</td></tr></table>
+
+Table 4: Scaling towards Longer Generation Lengths. We evaluate the performance of DIFFPO under various maximum length settings. When the same optimized DIFFPO-8B and 9B is applied to these outputs, consistently enhanced performance demonstrates DIFFPO's robust scaling capabilities.
+
+# 4 Conclusion
+
+This paper introduces a novel inference-time alignment framework for large language models, DiffPO. DiffPO achieves alignment at the sentence level to better model human preferences, drawing inspiration from the denoising process. DiffPO outperforms both strong training-based and inference-time alignment techniques in terms of alignment performance and inference speed. Experiments scaling DiffPO from 2B to 9B parameters, expanding the base model from 1B to 70B, and increasing the context length from 256 to 2,048 demonstrate that DiffPO is a robust and scalable framework for LLM alignment.
+
+<table><tr><td rowspan="2">Loss</td><td>MT</td><td>AE2</td><td>HH</td></tr><tr><td>GPT-4</td><td>LC (%)</td><td>Avg.</td></tr><tr><td colspan="4">Vanilla Decoding</td></tr><tr><td>Llama-3-Instruct</td><td>6.78</td><td>36.83</td><td>0.7985</td></tr><tr><td>\( L_{\text{AR}} \)</td><td>6.90</td><td>36.35</td><td>0.7971</td></tr><tr><td colspan="4">Llama-3-it w. DIFFPO-8B</td></tr><tr><td>\( L_{\text{AR}} \) only</td><td>6.75</td><td>35.84</td><td>0.7968</td></tr><tr><td>\( 10 \times L_{\text{Con}} + L_{\text{AR}} \)</td><td>6.85</td><td>35.96</td><td>0.7997</td></tr><tr><td>\( 100 \times L_{\text{Con}} + L_{\text{AR}} \)</td><td>6.86</td><td>35.92</td><td>0.7998</td></tr><tr><td>\( 1,000 \times L_{\text{Con}} + L_{\text{AR}} \)</td><td>7.02</td><td>36.44</td><td>0.7998</td></tr></table>
+
+Table 5: Loss and Hyperparameter Ablation. We report the results of vanilla decoding from the base model and the optimized DIFFPO model. The results indicate that applying DIFFPO to the base model yields outperforming performance than single models, demonstrating the effectiveness of DIFFPO strategy.
+
+# Limitations
+
+We acknowledge the presence of certain limitations. While DiffPO has demonstrated a superior tradeoff between performance and inference-time cost, it still introduces additional inference latency due to the need for an extra model for alignment. Moreover, we observe that the performance of DiffPO scales with its size, which presents challenges for cost-effectiveness during deployment. Additionally, despite the empirical success and intuitive motivation behind DiffPO, a more rigorous theoretical analysis is required to fully understand its effectiveness. Future work could explore how to combine the diffusion process (i.e., the denoising process) with the alignment task more effectively. This paper draws insights from the analogy between the denoising process and alignment. We hope our findings will facilitate future exploration of existing successful techniques in the natural language processing domain.
+
+# Potential Risks
+
+As an inference-time alignment technique, DIFFPO aims to develop AI assistants that align with positive human intentions and social values. However, there is a potential risk that DIFFPO could be misused to align with harmful or negative values. We strongly oppose any such misuse, as it could hinder human progress, and advocate for the responsible and ethical use of DIFFPO.
+
+# Acknowledgements
+
+This work is supported by the National Natural Science Foundation of China (Grant No. 12326612, 62476241), the Natural Science Foundation of Zhejiang Province, China (Grant No. LZ23F020008), Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, and the Zhejiang University-Angelalign Inc. R&D Center for Intelligent Healthcare.
+
+This research/project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-GV2023-010).
+
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+
+# A Related Works
+
+# A.1 Align LLM with Human Preference.
+
+A prominent approach to learning from human preferences is RLHF (Ouyang et al., 2022; Stiennon et al., 2020; Christiano et al., 2017; Bai et al., 2022). In this framework, a reward model is first trained, followed by the training of a bandit policy using Proximal Policy Optimization (PPO) (Schulman et al., 2017). Recent advancements such as direct preference optimization (DPO) (Rafailov et al., 2024; Meng et al., 2024; Ethayarajh et al., 2024) optimize the bandit policy directly from human preferences, bypassing the need for a reward model. These approaches are simpler to implement and require fewer computational resources. Inference-time approaches, on the other hand, achieve alignment by customizing the output of large language models (LLMs) during the decoding phase, without the need for parameter optimization. This results in enhanced flexibility and efficiency (Khanov et al., 2024; Mudgal et al., 2023; Chen et al., 2024d). One representative method treats the text-generation process as a search problem, guided by external rewards (Huang et al., 2024; Han et al., 2024; Chakraborty et al., 2024). Another category of methods focuses on learning to refine the generated text (Li et al., 2023a; Ji et al., 2024; Yang et al., 2024a).
+
+Token and Sentence-level. Existing training-based or inference-time alignment approaches typically rely on token-level rewards, while human preferences are generally provided and defined at the sentence level (Li et al., 2023b; Ahmadian et al., 2024; Zeng et al., 2024). To address this discrepancy, some works (Lightman et al., 2023; Yang et al., 2024b; Zeng et al., 2024) leverage token-wise or step-wise information to improve alignment performance. In contrast, this paper proposes modeling alignment as a sentence-level denoising process. We introduce a model-agnostic, inference-time alignment method, and our empirical results demonstrate its superiority in both performance and efficiency.
+
+# A.2 Parallel Decoding and Diffusion Process.
+
+Parallel Decoding of LLMs Parallel decoding has been increasingly utilized and developed in recent research to accelerate the inference processes of large language models (LLMs). One line of research, including works by Leviathan et al. (2023);
+
+Chen et al. (2023), focuses on speculative decoding. These techniques enhance LLM decoding speed by employing a smaller draft model to predict the outputs, which are then verified in parallel by a larger target model. Another research trajectory explores parallel decoding strategies that do not rely on a draft model. Methods such as conditioning on "look-ahead" tokens or employing Jacobi iterations have been investigated by Santilli et al. (2023); Fu et al. (2024). These approaches allow the target model to produce several tokens simultaneously, aiming for rapid convergence to a fixed point on a Jacobi trajectory. CLLMs (Song et al., 2023) develop a novel approach, refining the target LLM to consistently predict the fixed point from any given state.
+
+Text Diffusion Models Diffusion models have demonstrated significant diversity and controllability in image generation (Ho et al., 2020; Song et al., 2020; Dhariwal and Nichol, 2021). Recently, these models have been extended to text generation, as evidenced by the works of (Li et al., 2022; Gong et al., 2022; Lovelace et al., 2024). In essence, diffusion models execute a multi-step denoising process that progressively transforms random noise into a coherent data sample. In the context of text, diffusion models can be considered an evolution of traditional iterative Non-Autoregressive models, as described by Gong et al. (2022). These models have demonstrated the ability to match or surpass Autoregressive (AR) models in terms of text perplexity (Han et al., 2022; Gulrajani and Hashimoto, 2024), diversity (Gong et al., 2023; Zhang et al., 2024), and various sequence-to-sequence tasks (Ye et al., 2024b,a).
+
+Connection with DIFFPO In this paper, we are motivated by the goal of aligning Large Language Models (LLMs) with human values or intentions, as outlined in (Yao et al., 2023). We define preferences at the sentence-level, focusing on the style or format of complete answers generated by the LLMs. If we consider each iteration of parallel decoding as a transition between states, this bears a formal resemblance to discrete diffusion models. In DIFFPO, we leverage parallel decoding to implement sentence-level denoising, thereby enhancing the modeling of the alignment process.
+
+The development of DIFFPO is also inspired by Consistency Models (Song et al., 2023) and CLLMs (Kou et al., 2024). Consistency models address the limitation of the slow iterative sampling
+
+Table 6: Comparison results of DIFFPO models. The experiments are conducted on base models of Qwen-2.5-7B and 14B. It shows that DIFFPO consistently achieves superior performance across various base models.  
+
+<table><tr><td rowspan="2">Base Models</td><td colspan="3">MT-bench</td><td colspan="2">AlpacaEval 2</td><td colspan="3">HH-RLHF</td></tr><tr><td>1-Turn</td><td>2-Turn</td><td>Avg.</td><td>LC (%)</td><td>WR (%)</td><td>Overall</td><td>Helpful</td><td>Harmless</td></tr><tr><td>Qwen2.5-7B-Instruct</td><td>7.11</td><td>6.96</td><td>7.03</td><td>45.03</td><td>49.95</td><td>0.1095</td><td>0.6995</td><td>0.9442</td></tr><tr><td>w. DPO</td><td>7.37</td><td>6.96</td><td>7.17</td><td>50.55</td><td>55.00</td><td>0.1109</td><td>0.7061</td><td>0.9460</td></tr><tr><td>w. SimPO</td><td>7.41</td><td>6.98</td><td>7.20</td><td>48.76</td><td>52.75</td><td>0.1100</td><td>0.7047</td><td>0.9387</td></tr><tr><td>w. BPO</td><td>6.90</td><td>6.16</td><td>6.53</td><td>29.44</td><td>39.85</td><td>0.1086</td><td>0.6765</td><td>0.9418</td></tr><tr><td>w. BoN</td><td>7.50</td><td>7.10</td><td>7.30</td><td>50.42</td><td>55.43</td><td>0.1159</td><td>0.7066</td><td>0.9440</td></tr><tr><td>w. Aligner</td><td>6.24</td><td>3.76</td><td>5.00</td><td>42.15</td><td>45.82</td><td>0.1088</td><td>0.6993</td><td>0.9438</td></tr><tr><td>w. MetaAligner</td><td>6.41</td><td>5.13</td><td>5.77</td><td>36.58</td><td>38.45</td><td>0.0995</td><td>0.6966</td><td>0.9422</td></tr><tr><td>w. DIFFPO-2B</td><td>7.01</td><td>6.34</td><td>6.67</td><td>43.89</td><td>49.07</td><td>0.1074</td><td>0.7013</td><td>0.9659</td></tr><tr><td>w. DIFFPO-9B</td><td>7.62</td><td>7.10</td><td>7.35</td><td>57.89</td><td>63.01</td><td>0.1117</td><td>0.7100</td><td>0.9445</td></tr><tr><td>Qwen2.5-14B-Instruct</td><td>7.24</td><td>6.71</td><td>6.98</td><td>51.60</td><td>57.10</td><td>0.1117</td><td>0.7100</td><td>0.9445</td></tr><tr><td>w. BPO</td><td>7.21</td><td>6.82</td><td>7.02</td><td>37.02</td><td>47.51</td><td>0.1005</td><td>0.6853</td><td>0.9323</td></tr><tr><td>w. BoN</td><td>7.49</td><td>6.98</td><td>7.24</td><td>54.92</td><td>59.02</td><td>0.1182</td><td>0.7163</td><td>0.9485</td></tr><tr><td>w. Aligner</td><td>6.14</td><td>4.11</td><td>5.13</td><td>45.14</td><td>47.24</td><td>0.1114</td><td>0.7099</td><td>0.9438</td></tr><tr><td>w. MetaAligner</td><td>6.24</td><td>5.73</td><td>5.99</td><td>41.25</td><td>43.23</td><td>0.1092</td><td>0.7074</td><td>0.9375</td></tr><tr><td>w. DIFFPO-2B</td><td>7.08</td><td>6.33</td><td>6.71</td><td>43.70</td><td>48.76</td><td>0.1078</td><td>0.7017</td><td>0.9704</td></tr><tr><td>w. DIFFPO-9B</td><td>7.62</td><td>7.35</td><td>7.48</td><td>55.13</td><td>60.65</td><td>0.1136</td><td>0.7185</td><td>0.9759</td></tr></table>
+
+process by mapping any point along the probability flow ODE of the diffusion process back to the original point in a single step. CLLMs propose accelerating LLM inference by mapping the intermediate process of LLM parallel decoding to the final process. Similar to these works, we optimize DIFFPO with consistency loss, thus enabling model-agnostic alignment.
+
+# B Experiment
+
+# B.1 Experimental Setups
+
+Training Details. As for the training set, we collect 6 generations from 6 base models (i.e., Llama-3-8B-Instruct, Llama-3-8B-SFT, Mistral-7B-SFT, Mistral-7B-Instruct, Gemma-2-2B-Instruct, Gemma-2-9B-Instruct). We then employ ArmoRM (Wang et al., 2024) to score these responses. The response with the highest score is selected as  $\mathbf{y}^{(T)}$ . The remaining five responses are ranked according to their scores to serve as  $\mathbf{y}^{(0:T - 1)}$ . In the training process, at each iteration, we randomly sample  $\mathbf{y}^t$  from  $\mathbf{y}^{(0:T - 1)}$  for optimization. We train DIFFPO models using the following hyperparameters: a learning rate of 1e-9, a batch size of 1 and gradient accumulation steps of 4, a max sequence length of 1024, and a cosine learning rate schedule with  $3\%$  warmup steps for 1 epoch. All the models are trained with an Adam optimizer. All the training experiments in this paper were con
+
+duced on  $8\times \mathrm{A}100$  GPUs.
+
+Evaluation Details. For the MT-bench, we use GPT-4 as the judge model, following the default settings. The scores are based on a single-answer rating scale from 1 to 10. For AlpacaEval, we use GPT-4 Turbo as the judge model, which performs pairwise comparison of responses generated by GPT-4, each with the same maximum length. For HH-RLHF, we use ArmoRM for single-answer rating and report the overall score, along with the "helpful" and "harmless" scores, which are provided in dimensions 9 and 10, respectively.
+
+Baseline Details. Implementation details for different baselines are as follows:
+
+MetaAligner: we use the opensourced MetaAligner-7B model https://huggingface.co/MetaAligner/  
+MetaAligner-HH-RLHF-7B on Huggingface and follow its official inference guideline on Huggingface.  
+DPO, SimPO: we directly use open-sourced models https://huggingface.co/princeton-nlp on Huggingface, which are fine-tuned according to the recipes in SimPO (Meng et al., 2024).  
+-Args: We reproduceArgs according to https://github.com/deeplearning-wisc/args/
+
+tree/main by replacing the reward model with ArmoRM (Wang et al., 2024).
+
+- Aligner: we use the open-sourced Aligner-7B https://huggingface.co/aligner/ aligner-7b-v1.0 on Huggingface and follow its guideline on Huggingface.  
+- BPO: we use the open-sourced BPO model https://huggingface.co/THUDM/BPO on Huggingface and follow its official inference on Huggingface.
+
+# B.2 Experimental Results
+
+DIFFPO significantly outperforms existing preference optimization methods. We provided additional comparison with baselines, with results presented in Table 6. The experiments are conducted on base models of Qwen-2.5-7B and 14B. While all preference optimization algorithms improve performance over the base model, DIFFPO achieves the best overall performance across all benchmarks and settings. These consistent and significant improvements underscore the robustness and effectiveness of DIFFPO. Notably, DIFFPO outperforms the training-based baselines (i.e., SimPO and DPO) across various settings, despite requiring only a single training session of DIFFPO model and being capable of enhancing the performance of multiple base models.
+
+# C Analysis
+
+# C.1 Illustration of the Speed-up of DiffPO
+
+As shown in Figure 4, AR decoding (e.g., Aligner (Ji et al., 2024)) typically generates only one aligned token per iteration. In contrast, DIFFPO enables the skipping of satisfied tokens, thereby avoiding the time latency associated with token-level generation. As a result, DIFFPO can predict the modified subsequence in 3 iterations, achieving the same result as 11 iterations of AR decoding.
+
+# D More Related Works
+
+LLM Pluralism and Fairness LLM alignment ensures AI systems follow human intentions and values (Stiennon et al., 2020; Bai et al., 2022; Ouyang et al., 2022; Achiam et al., 2023). However, within a single task, users' goals and values often differ. As AI systems are increasingly used by diverse groups, they must address a broader
+
+range of needs. In short, we need AI systems that are pluralistic and fair, being capable of reflecting diverse human values (Chen et al., 2024c,b; Fan et al., 2024b; Luo et al., 2024; Fan et al., 2024a; Chen et al., 2024a).
+
+t=0
+
+I hope this email finds you well. I am writing to seek your feedback on the 'Quarterly Financial Report'
+
+t=1
+
+1 ...
+
+t=2
+
+I hope ...
+
+t=3
+
+I hope this ...
+
+···
+
+t=12
+
+I hope this email finds you well. I would kindly seek
+
+# (b) DIFFPO
+
+![](images/e705da21af151e929c36cc6ce8a84710f417e596bd45de4a77a3cf9724d13a37.jpg)  
+(a) AR decoding  
+Figure 4: Illustration of the Speedup of DIFFPO.
+
+t=0
+
+I hope this email finds you well. I am writing to seek your feedback on the 'Quarterly Financial Report'
+
+t=1
+
+I hope this email finds you well. I would writing to seek your feedback on the 'Quarterly Financial Report'
+
+t=2
+
+I hope this email finds you well. I would kindly to seek your feedback on the 'Quarterly Financial Report'
+
+t=3
+
+I hope this email finds you well. I would kindly seek seek your feedback on the 'Quarterly Financial Report'

2025/DiffPO_ Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models/images.zip

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2025/DiffuseDef_ Improved Robustness to Adversarial Attacks via Iterative Denoising/dc48f8da-29af-45f8-a455-c2476233d7d9_origin.pdf

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2025/DiffuseDef_ Improved Robustness to Adversarial Attacks via Iterative Denoising/full.md

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+# DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising
+
+Zhenhao Li, Huichi Zhou, Marek Rei, Lucia Specia
+
+Language and Multimodal AI (LAMA) Lab, Imperial College London {zhenhao.li18, h.zhou24, marek.rei, l.specia}@imperial.ac.uk
+
+# Abstract
+
+Pretrained language models have significantly advanced performance across various natural language processing tasks. However, adversarial attacks continue to pose a critical challenge to systems built using these models, as they can be exploited with carefully crafted adversarial texts. Inspired by the ability of diffusion models to predict and reduce noise in computer vision, we propose a novel and flexible adversarial defense method for language classification tasks, DiffuseDef<sup>1</sup>, which incorporates a diffusion layer as a denoiser between the encoder and the classifier. The diffusion layer is trained on top of the existing classifier, ensuring seamless integration with any model in a plug-and-play manner. During inference, the adversarial hidden state is first combined with sampled noise, then denoised iteratively and finally ensembled to produce a robust text representation. By integrating adversarial training, denoising, and ensembling techniques, we show that DiffuseDef improves over existing adversarial defense methods and achieves state-of-the-art performance against common black-box and white-box adversarial attacks.
+
+# 1 Introduction
+
+Pretrained language models (PLM) have significantly advanced the performance of various natural language processing (NLP) tasks. Despite such improvements, current NLP systems remain susceptible to adversarial attacks where carefully crafted text perturbations can lead to incorrect model outputs (Alzantot et al., 2018; Jin et al., 2020; Li et al., 2020). In order to improve robustness to adversarial attacks, various defense methods have been proposed, such as adversarial training (Zhu et al., 2020; Si et al., 2021; Zhou et al., 2021; Xi et al., 2022), text denoising (Nguyen Minh and Luu, 2022; Wang et al., 2023), ensembling (Zhou et al., 2021; Zeng
+
+et al., 2023; Li et al., 2023), etc. However, existing defense methods either assume the test-time perturbation/attack set is similar to that used in training (Li et al., 2021), or are limited to specific architectures (Xi et al., 2022), or at inference time require large computational costs, thereby limiting their practical applicability.
+
+Diffusion models are commonly used in computer vision (CV) to generate high-quality images by predicting and removing noise from a sampled noisy image. Therefore, they can be adopted to remove noise from adversarial images and thus improve robustness to attacks (Im et al., 2016; Nie et al., 2022; Gulsen et al., 2024). However, in NLP very limited research has investigated adversarial defense with diffusion models due to the discrete and contextual nature of text data. Li et al. (2023) adopt the idea of iterative denoising and reconstruct adversarial texts from masked texts, while Yuan et al. (2024) use a diffusion model as a classifier and perform reverse diffusion steps on the label vector, conditioning on the input text. Inspired by the general noise prediction and reduction capability of diffusion models, we propose DiffuseDef, a novel adversarial defense method which employs diffusion training to denoise hidden representations of adversarial texts. Unlike Li et al. (2023) and Yuan et al. (2024), which apply diffusion on texts or labels, DiffuseDef directly removes noise from the hidden states, providing a more effective and robust text representation to defend against adversarial texts. Different from diffusion-based defenses in CV (Nie et al., 2022; Gulsen et al., 2024) that apply purification to input images, DiffuseDef only performs denoising at the final hidden layer, resulting in an improvement in efficiency. This improvement is further enhanced when combined with ensembling, as it eliminates the need for a forward pass through all classifier parameters for each ensemble.
+
+DiffuseDef combines adversarial training with diffusion training, where the diffusion layer is
+
+trained to predict randomly sampled noise at a given timestep. During inference, the diffusion layer serves as a denoiser, iteratively removing noise from adversarial hidden states to yield a robust hidden representation. Moreover, we adopt the ensembling strategy by first adding random noise to the text hidden states to create multiple variants, then denoising them via the diffusion layer. The model output is made by averaging all denoised hidden states. Since ensembling happens solely at the diffusion layer, DiffuseDef is more efficient than traditional ensembling-based methods (Ye et al., 2020; Zeng et al., 2023), which require a full forward pass through all model parameters.
+
+Through systematic experimentation, we demonstrate that DiffuseDef outperforms strong defense methods and is able to defend against multiple types of black-box and white-box adversarial attacks, while preserving performance on clean texts. Our analysis also reveals that the ensembling diffused representation provides a stronger defense against finding vulnerable words to attack and can reduce the distance in latent space between adversarial texts and their clean text counterparts.
+
+Our contributions can be summarized as follows:
+
+- We propose DiffuseDef, a novel and flexible adversarial defense method that can be added on top of any existing adversarial defense methods to further improve robustness to adversarial attacks.  
+- DiffuseDef outperforms existing adversarial methods and achieves state-of-the-art performance against prevalent black-box and white-box adversarial attacks.  
+- Through extensive analysis, we demonstrate the effectiveness of the ensembling diffused representation and the efficiency of DiffuseDef compared to existing ensembling-based methods.
+
+# 2 Related Work
+
+# 2.1 Textual Adversarial Attacks
+
+Textual adversarial attacks focus on constructing adversarial examples from an original text that maximize the likelihood of incorrect predictions by a neural network. These attacks require adversarial examples to be perceptually similar to the original text, which is typically achieved by introducing subtle perturbations to the original text, such as character swapping (Gao et al., 2018; Ebrahimi et al.,
+
+2018), synonym-substitutions (Ren et al., 2019; Yoo and Qi, 2021; Liu et al., 2023), and paraphrasing (Gan and Ng, 2019; Huang and Chang, 2021). Taking the text classification task as an example, given a classifier  $\mathcal{C}(\mathbf{x})$  that maps an input sequence of words  $\mathbf{x} = [w_{1}, w_{2}, \dots, w_{L}]$  to its designated label  $y$ , the goal of the attack model is to construct an adversarial example  $\mathbf{x}' = \mathbf{x} + \delta$  to fool the classifier, where  $\delta$  is a subtle adversarial perturbation constrained by  $||\delta|| < \omega$ . The adversarial example  $\mathbf{x}'$  is considered a successful attack if it leads to an incorrect prediction  $\mathcal{C}(\mathbf{x}') \neq y$ . The attacker can iteratively generate multiple adversarial examples and query the classifier to obtain a successful attack, whereas the classifier must consistently return the correct prediction within a specified number of query attempts to be considered robust.
+
+Common textual adversarial attack methods adopt a two-stage process to construct effective adversarial examples: word importance ranking and word substitution. In the first stage, words or subwords are ranked based on their influence on the model's prediction. This is measured by leveraging either gradient information (Liu et al., 2022) or changes in prediction probabilities when words are removed (Jin et al., 2020) or masked (Ren et al., 2019; Li et al., 2020). In the second stage, candidate words are substituted with synonyms (Zang et al., 2020), perturbed variants (Gao et al., 2018), or outputs from masked language models (Garg and Ramakrishnan, 2020; Li et al., 2020). The substitution process is guided by various constraints to ensure the adversarial example remains natural and semantically equivalent to the original text. Common constraints include thresholding the similarity between the replacement word embedding and the substituted word embedding, or ensuring the semantic similarity (Zhou et al., 2024) between sentence vectors modeled from Universal Sentence Encoder (Cer et al., 2018). Despite these constraints, current textual adversarial attacks still pose significant challenges to NLP models (Liu et al., 2022; Xu et al., 2021; Yuan et al., 2023), highlighting the necessity for defense methods for better adversarial robustness.
+
+# 2.2 Adversarial Defense Methods
+
+To mitigate the performance degradation caused by adversarial attacks, various adversarial defense methods have been developed. They can be grouped into three categories: training-based, ensembling-based, and denoising-based methods.
+
+Adversarial training improves the robustness of the model to adversarial examples through strategies like data augmentation (Si et al., 2021; Bao et al., 2021) and adversarial regularization (Madry et al., 2018; Zhu et al., 2020; Wang et al., 2021; Xi et al., 2022; Gao et al., 2023; Formento et al., 2024). However, adversarial training methods are limited as they assume similar train-test adversarial examples, and thus tend to overfit to specific types of adversarial attacks. Ensembling-based methods generate multiple variants of the input text at inference time and ensemble model predictions over all the variants (Ye et al., 2020; Zhou et al., 2021; Zeng et al., 2023; Li et al., 2023), but they can be inefficient given that model predictions are needed on every ensemble, increasing the inference time with the number of ensembles. More recently, denoising-based methods have been proposed to improve adversarial robustness by mapping the vector representation of the adversarial text to another point in the latent space that is close to the clean text (Nguyen Minh and Luu, 2022; Wang et al., 2023; Moon et al., 2023; Yuan et al., 2024; Ji et al., 2024). The denoised representation makes it more difficult to find vulnerable words to attack, thus improving adversarial robustness (Wang et al., 2023). Nevertheless, denoising might lead to very different representations of clean text and adversarial text, therefore changing the semantic meanings.
+
+The proposed DiffuseDef builds on these three approaches and can use any adversarially trained classifier as the base, applying denoising via a diffusion layer, and ensembling the diffused representations with a small number of ensembles. Using a diffusion layer as a denoiser addresses the overfitting problem from adversarial training and mitigates the efficiency problem by performing ensembling only at the diffusion layer. By averaging denoised hidden states across all ensembles, DiffuseDef also addresses the issue stemming from denoising, maintaining good performance on clean texts.
+
+# 3 DiffuseDef
+
+# 3.1 Training
+
+The proposed diffusion defense model consists of a pretrained encoder for feature extraction, a transformer-based diffusion layer for noise prediction and reduction, and a classifier layer for output generation. The training process is split into two stages: adversarial training and diffusion training
+
+(Figure 1). The adversarial training stage employs a neural network-based adversarial training method like FreeLB++ (Li et al., 2021) or RSMI (Moon et al., 2023), which optimizes the encoder and classifier for robustness by perturbing the latent representation of the text input.
+
+In the diffusion training stage, only the diffusion layer is trained to predict random noise added to the clean text hidden state at different timesteps, enabling it to denoise the adversarial hidden state at inference time. The pretrained encoder, however, is frozen during this stage. Since the pretrained encoder is only used for feature extraction, the diffusion training method is compatible with any neural network-based adversarial training method.
+
+Given an input sequence of tokens  $\mathbf{x} \in \mathbb{R}^L$ , the pretrained encoder extracts the hidden state  $h \in \mathbb{R}^{L \times D}$ . A random Gaussian noise  $\epsilon$  is sampled to perturb the hidden state  $h$ . Sohl-Dickstein et al. (2015) define the forward diffusion process as a Markov Chain where at each timestep a Gaussian noise is sampled and added to the previous latent feature:  $h_t = \sqrt{1 - \beta_t} h_{t-1} + \sqrt{\beta} \epsilon$ , where  $\epsilon \in \mathcal{N}(0, \mathcal{I})$ ,  $h_t$  is the noisy hidden state at step  $t$  and  $\beta$  is a pre-calculated variance schedule changing with  $t$ . As shown by Ho et al. (2020), this equation can be reformulated to calculate  $h_t$  directly from  $h$  by defining  $\alpha_t = 1 - \beta_t$  and  $\bar{\alpha} = \prod_{i=1}^t \alpha_i$ , thus
+
+$$
+h _ {t} = \sqrt {\bar {\alpha} _ {t}} h + \sqrt {1 - \bar {\alpha} _ {t}} \epsilon \tag {1}
+$$
+
+At each training step, a random forward diffusion timestep  $t$  is sampled from a uniform distribution. Therefore, the noisy hidden state  $h_t$  is created from  $h$ ,  $t$ , and  $\epsilon$ . The diffusion layer  $\theta$  consists of a time embedding and a transformer layer. The time embedding receives the diffusion timestep  $t$  as input and produces an embedding  $e_t$ , which is added to  $h_t$  as input for the transformer layer. Finally, the transformer layer outputs the predicted noise  $\epsilon_{\theta}(h_t, t)$ , and mean square error (MSE) is used to compute the loss between the predicted noise  $\epsilon_{\theta}(h_t, t)$  and the actual sampled noise  $\epsilon$ .
+
+$$
+L = \mathbb {E} _ {t, h, \epsilon} \left[ \left\| \epsilon - \epsilon_ {\theta} \left(\sqrt {\bar {\alpha} _ {t}} h + \sqrt {1 - \bar {\alpha} _ {t}} \epsilon\right) \right\| ^ {2} \right] \tag {2}
+$$
+
+# 3.2 Inference
+
+Leveraging the diffusion layer's ability to predict noise at a given timestep  $t$ , we utilize it as a denoiser during inference by iteratively performing the reverse diffusion steps, which sample from  $p_{\theta}(h_{t - 1}|h_t) = \mathcal{N}(h_{t - 1};\mu_{\theta}(h_t,t),\Sigma_{\theta}(h_t,t))$  to
+
+![](images/bc6be205caa756f03028832e72521ba0e717c6d33374f50ac45011f17e778504.jpg)  
+Figure 1: Training and inference of DiffuseDef model. The adversarial training stage trains the pretrained encoder and classifier with perturbed input for adversarial robustness. The diffusion training trains the diffusion layer to predict injected noise at a given timestep  $t$ . At inference time, the text hidden state is first noised by 1 step and then denoised by  $t'$  steps to create the denoised hidden states, which are ensembled to make the final prediction.
+
+produce the denoised hidden state
+
+$$
+\mu_ {\theta} \left(h _ {t}, t\right) = \frac {1}{\sqrt {\alpha_ {t}}} \left(h _ {t} - \frac {1 - \alpha_ {t}}{\sqrt {1 - \bar {\alpha} _ {t}}} \epsilon_ {t}\right) \tag {3}
+$$
+
+$$
+\Sigma_ {\theta} \left(h _ {t}, t\right) = \sigma_ {t} ^ {2} \mathcal {I} \tag {4}
+$$
+
+where  $\epsilon_{t}$  is the predicted noise from diffusion layer and  $\sigma_t^2 = \beta_t$ . The denoised hidden state can thus be computed with
+
+$$
+h _ {t - 1} = \frac {1}{\sqrt {\alpha_ {t}}} \left(h _ {t} - \frac {1 - \alpha_ {t}}{\sqrt {1 - \bar {\alpha} _ {t}}} \epsilon_ {t}\right) + \sigma_ {t} z \tag {5}
+$$
+
+where  $z\in \mathcal{N}(0,\mathcal{I})$
+
+Inference in DiffuseDef combines a one-step noising, a multi-step denoising, and an ensembling step. After the pretrained encoder extracts its hidden state  $h$ , a set of  $k$  Gaussian noise vectors  $E = [\epsilon^0, \epsilon^1, \dots, \epsilon^k]$  are sampled to perform a single forward diffusion step. These noise vectors  $E$  are then added to the hidden state  $h$  following equation 1, resulting in a set of noisy hidden states  $H_{t'} = [h_{t'}, h_{t'}, \dots, h_{t'}^k]$ , where  $t'$  denotes the number of denoising steps. The noisy hidden states  $H_{t'}$  are subsequently denoised through  $t'$  reverse diffusion steps, where noise is predicted by the diffusion layer and subtracted from the previous noisy hidden states. Unlike Ho et al. (2020), where the reverse diffusion step starts with pure noise sampled from a
+
+standard normal distribution, we assume the noisy hidden state  $H_{t'}$  is already an intermediate state in the reverse diffusion steps. This allows us to use a smaller number of  $t'$  than the training timestep  $t$  to prevent the denoised hidden states from diverging substantially from the initial hidden state  $h$ . This sequence of denoising steps creates the final denoised hidden states  $H_0 = [h_0^0, h_0^1, \dots, h_0^k]$ , which are averaged and used by the classifier to output the final predicted label. This process is summarized in Algorithm 1.
+
+# Algorithm 1: Inference of DiffuseDef
+
+# Data: Input text x
+
+Result: Predicted label  $y'$
+
+1  $h\gets Enc(\mathbf{x})$  
+2 Sample  $E = [\epsilon^0, \epsilon^1, \dots, \epsilon^k], \epsilon \sim \mathcal{N}(0, \mathcal{I})$ ;  
+3  $H_{t^{\prime}}\gets \sqrt{\bar{\alpha}_{1}} h + \sqrt{1 - \bar{\alpha}_{1}} E;$  
+4 for  $i\gets 0$  to  $t^{\prime} - 1$  do  
+5  $E_{t^{\prime} - i}\gets \epsilon_{\theta}(H_{t^{\prime} - i},t^{\prime} - i);$  
+6  $H_{t^{\prime} - i - 1}\gets$
+
+$$
+\begin{array}{l} \frac {1}{\sqrt {\alpha_ {t ^ {\prime} - i}}} \left(H _ {t ^ {\prime} - i} - \frac {1 - \alpha_ {t ^ {\prime} - i}}{\sqrt {1 - \bar {\alpha} _ {t ^ {\prime} - i}}} E _ {t ^ {\prime} - i}\right) + \\ \sigma_ {t ^ {\prime} - i} z; \end{array}
+$$
+
+7 end  
+$y^\prime \gets CLS(\text{avg}(H_0));$
+
+# 4 Experiments
+
+Datasets. We focus on two common NLP tasks in our experiments: topic classification and natural language inference (NLI). We conduct experiments on three common datasets: AG News (Zhang et al., 2015a), IMDB (Maas et al., 2011a), and QNLI datasets (Wang et al., 2018). We randomly split AGNews, IMDB, and QNLI datasets into train, validation, and test splits. Appendix A gives details on data preparation.
+
+Evaluation. Following previous work on adversarial defense, we use three benchmarking black-box attack methods to evaluate the robustness of DiffuseDef: TextFooler (TF) (Jin et al., 2020), TextBugger (TB) (Li et al., 2019), and Bert-Attack (BA) (Li et al., 2020). We also evaluated the robustness against two white-box attacks: T-PGD (Yuan et al., 2023) and SemAttack (Wang et al., 2022). Regarding evaluation metrics, we measure the clean accuracy ( $\text{Clean}\%$ ) on the test set, the accuracy under attack ( $\text{AUA}\%$ ), and the number of adversarial queries ( $\# \text{Query}$ ) needed for a successful attack. Higher scores on the three metrics denote a better robustness performance of a defense method. The accuracy on clean data is measured across the entire test set. The accuracy under attack and number of queries, due to the lengthy attacking process, is measured on a randomly sampled subset of 1000 examples from the test set. We use the TextAttack library as the adversarial evaluation framework. To ensure a fair comparison and high-quality adversarial examples, we follow the same evaluation constraints as in Li et al. (2021). The evaluation metrics are averaged based on experiments run with 5 random seeds.
+
+# 4.1 Comparison to SOTA
+
+We compare our proposed method with state-of-the-art adversarial defense approaches, trained using both BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) as backbones: Fine-tune: Finetuning pretrained models on downstream tasks with no defense method applied<sup>2</sup>. InfoBERT (Wang et al., 2021): Applying mutual-information-based regularizer during fine-tuning of pretrained models to improve robustness. FreeLB++ (Li et al., 2021): An adversarial training method improving on FreeLB(Zhu et al., 2020), which adds adversarial perturbations to word embedding during fine
+
+tuning. EarlyRobust $^3$  (Xi et al., 2022): Extracting early-bird subnetworks and pruning pretrained models for efficient adversarial training. RMLM (Wang et al., 2023): A denoising-based model combined with adversarial text detection. ATINTER (Gupta et al., 2023): A fine-tuned T5 model that rewrites the input adversarial texts, and the classifier predicts labels from the rewritten texts. RSMI (Moon et al., 2023): A two-stage training method that combines randomized smoothing and masked inference to improve adversarial robustness.
+
+# 4.2 Implementation and Settings
+
+We train two DiffuseDef variants using FreeLB++ and RSMI models as base models considering their robust adversarial defense capabilities. In the diffusion layer, only one transformer encoder layer (Vaswani et al., 2017) is used. The maximum nois-ing timestep  $t$  during training is set to 30 for AG-News and QNLI datasets, and 10 for the IMDB dataset, while at inference time, we only apply 5 denoising steps for  $t'$ . We follow (Ho et al., 2020) to use a linear  $\beta_{t}$  schedule from  $\beta_{1} = 10^{-4}$  to  $\beta_{t} = 0.02$ . The base encoder and classifier are first fine-tuned for 10 epochs, and the diffusion layer is trained for 100 epochs, with the base encoder and classifier parameters frozen for efficiency. During the diffusion training stage, the same train-dev splits are used as in the adversarial training stage, thus ensuring no data leakage. At inference time, the number of ensembles is set to 10. Appendix C lists the training hyperparameters.
+
+# 5 Results and Analysis
+
+# 5.1 Adversarial Robustness
+
+In Table 1, we compare the adversarial robustness of DiffuseDef with baselines and SOTA methods on AGNews, IMDB, and QNLI datasets trained with BERT (results for RoBERTa in Appendix E). DiffuseDef consistently outperforms all other methods on all three datasets across the three attacks, exhibiting substantial improvements in accuracy under attack. After applying diffusion training, the AUA score for both FreeLB++ and RSMI models improves significantly, with an average increase of  $30\%$  AUA against the three attack methods. When comparing the clean accuracies to its base model (i.e., FreeLB++ and RSMI), DiffuseDef only
+
+<table><tr><td rowspan="2">Dataset</td><td rowspan="2">Method</td><td rowspan="2">Clean%</td><td colspan="3">AUA %</td><td colspan="3">#Query</td></tr><tr><td>TF</td><td>TB</td><td>BA</td><td>TF</td><td>TB</td><td>BA</td></tr><tr><td rowspan="9">AGNews</td><td>Fine-Tuned</td><td>94.4</td><td>10.2</td><td>25.4</td><td>27.1</td><td>348</td><td>372</td><td>379</td></tr><tr><td>InfoBERT (Wang et al., 2021)</td><td>95.0</td><td>35.5</td><td>39.1</td><td>42.6</td><td>377</td><td>397</td><td>397</td></tr><tr><td>FreeLB++ (Li et al., 2021)</td><td>95.0</td><td>54.7</td><td>56.5</td><td>44.6</td><td>426</td><td>430</td><td>390</td></tr><tr><td>EarlyRobust (Xi et al., 2022)</td><td>94.4</td><td>35.6</td><td>37.2</td><td>45.7</td><td>475</td><td>516</td><td>533</td></tr><tr><td>RMLM (Wang et al., 2023)</td><td>94.3</td><td>45.6</td><td>44.4</td><td>54.1</td><td>642</td><td>738</td><td>705</td></tr><tr><td>ATINTER (Gupta et al., 2023)</td><td>94.2</td><td>68.0</td><td>59.0</td><td>81.0</td><td>527</td><td>235</td><td>122</td></tr><tr><td>RSMI (Moon et al., 2023)</td><td>94.3</td><td>52.6</td><td>56.7</td><td>55.4</td><td>680</td><td>737</td><td>687</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>94.8</td><td>84.5</td><td>86.0</td><td>84.6</td><td>877</td><td>972</td><td>910</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>93.8</td><td>82.7</td><td>83.3</td><td>84.4</td><td>894</td><td>1029</td><td>930</td></tr><tr><td rowspan="9">IMDB</td><td>Fine-Tuned</td><td>93.3</td><td>7.7</td><td>8.3</td><td>10.5</td><td>540</td><td>534</td><td>378</td></tr><tr><td>InfoBERT (Wang et al., 2021)</td><td>93.9</td><td>29.2</td><td>25.4</td><td>30.7</td><td>642</td><td>644</td><td>390</td></tr><tr><td>FreeLB++ (Li et al., 2021)</td><td>94.3</td><td>44.2</td><td>39.6</td><td>40.6</td><td>784</td><td>829</td><td>426</td></tr><tr><td>EarlyRobust (Xi et al., 2022)</td><td>92.7</td><td>49.7</td><td>46.8</td><td>43.8</td><td>2267</td><td>2788</td><td>1841</td></tr><tr><td>RMLM (Wang et al., 2023)</td><td>93.6</td><td>48.8</td><td>47.9</td><td>47.1</td><td>2554</td><td>3258</td><td>2064</td></tr><tr><td>ATINTER (Gupta et al., 2023)</td><td>94.3</td><td>25.0</td><td>15.0</td><td>39.0</td><td>1173</td><td>645</td><td>637</td></tr><tr><td>RSMI (Moon et al., 2023)</td><td>90.9</td><td>60.0</td><td>54.4</td><td>51.1</td><td>2840</td><td>3455</td><td>2070</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>94.4</td><td>82.1</td><td>83.0</td><td>84.0</td><td>3174</td><td>4348</td><td>2842</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>90.2</td><td>80.9</td><td>79.8</td><td>79.8</td><td>3590</td><td>4748</td><td>2901</td></tr><tr><td rowspan="8">QNLI</td><td>Fine-Tuned</td><td>90.8</td><td>21.5</td><td>15.5</td><td>13.3</td><td>195</td><td>206</td><td>177</td></tr><tr><td>InfoBERT (Wang et al., 2021)</td><td>91.2</td><td>27.8</td><td>22.8</td><td>18.7</td><td>217</td><td>232</td><td>201</td></tr><tr><td>FreeLB++ (Li et al., 2021)</td><td>90.3</td><td>45.6</td><td>40.2</td><td>30.5</td><td>253</td><td>279</td><td>226</td></tr><tr><td>EarlyRobust (Xi et al., 2022)</td><td>89.2</td><td>24.3</td><td>21.2</td><td>19.1</td><td>265</td><td>292</td><td>255</td></tr><tr><td>ATINTER (Gupta et al., 2023)</td><td>90.4</td><td>43.0</td><td>24.8</td><td>34.0</td><td>185</td><td>115</td><td>89</td></tr><tr><td>RSMI (Moon et al., 2023)</td><td>87.4</td><td>35.2</td><td>30.9</td><td>28.2</td><td>314</td><td>353</td><td>314</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>90.3</td><td>66.7</td><td>65.3</td><td>64.4</td><td>485</td><td>587</td><td>543</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>86.4</td><td>55.5</td><td>54.8</td><td>57.2</td><td>459</td><td>569</td><td>528</td></tr></table>
+
+Table 1: Main adversarial robustness results on classification tasks with BERT. Clean: accuracy on clean test set. TF: TextFooler. TB: TextBugger. BA: BertAttack.
+
+shows a minor decline, between 0.2 and 0.7 accuracy score, which indicates that it can preserve the clean text performance while improving adversarial robustness. Moreover, models trained with DiffuseDef show a much smaller gap between clean accuracy and accuracy under attack, and such a difference can be reduced to less than  $10\%$  AUA.
+
+Another benefit of DiffuseDef is the increased number of adversarial queries needed to obtain a successful attack. Models applying DiffuseDef require over twice the number of queries on all three datasets compared to the other methods. This increase is even larger on the IMDB dataset due to the longer text length. For example, the DiffuseDef model requires on average over 3000 queries to achieve a successful attack, while FreeLB++ can only defend for 400 to 800 queries. The substantial increase suggests that even if the attackers manage to construct a successful adversarial attack, they need  $2\mathrm{x}$  to  $3\mathrm{x}$  more time to find the attack on DiffuseDef than other models, affirming the improved robustness from diffusion train
+
+ing. In addition, we observe that the number of queries for denoising-based methods (i.e., RSMI, RMLM, and DiffuseDef) is generally higher than adversarial training-based methods (i.e., InfoBERT, FreeLB++) This is because denoising-based methods transform the hidden representations of the adversarial texts into a non-deterministic representation. The introduction of randomness in hidden states results in uncertainty in model logits, thus increasing the difficulty of finding vulnerable words to attack (Wang et al., 2023).
+
+# 5.2 Robustness against White-box Attacks
+
+In addition to black-box attacks, we also measure the robustness against white-box attacks, where the attackers can access the defender model's gradients and parameters, which is a more challenging adversarial attack scenario than black-box attacks. In Table 2, we report AUA score under T-PGD (Yuan et al., 2023) and SemAttack (Wang et al., 2022), the former being a variant of PGD attack (Madry et al., 2019) for text, while the latter adopts C&W
+
+attack (Carlini and Wagner, 2017) on embedding layers. We observe that white-box attacks result in lower AUA for all defense methods. For example, FreeLB++ performs poorly on IMDB when attacked by SemAttack, with the AUA score being 0.03. However, applying diffusion training still significantly improves adversarial robustness to white-box attacks. Note that even RSMI shows very robust performance against T-PGD<sup>4</sup>, it can still further improve from DiffuseDef.
+
+<table><tr><td rowspan="2">Method</td><td colspan="2">AGNews</td><td colspan="2">IMDB</td></tr><tr><td>T-PGD</td><td>Sem</td><td>T-PGD</td><td>Sem</td></tr><tr><td>Fine-Tuned</td><td>8.8</td><td>41.5</td><td>3.0</td><td>1.3</td></tr><tr><td>InfoBERT</td><td>32.1</td><td>47.1</td><td>25.4</td><td>2.0</td></tr><tr><td>FreeLB++</td><td>19.6</td><td>58.2</td><td>15.5</td><td>3.4</td></tr><tr><td>RSMI</td><td>79.6</td><td>n/a</td><td>43.3</td><td>n/a</td></tr><tr><td>DiffuseDef-FreeLB</td><td>59.4</td><td>68.1</td><td>50.3</td><td>28.2</td></tr><tr><td>DiffuseDef-RSMI</td><td>81.7</td><td>n/a</td><td>48.4</td><td>n/a</td></tr></table>
+
+# 5.3 Ablation analysis
+
+To understand how each component contributes to DiffuseDef, we conduct an ablation analysis on the AGNews dataset by disabling ensembling, diffusion denoising, and adversarial training one at a time. Table 3 shows the ablation results, and we find that removing either component (e.g., ensembling and diffusion denoising) at inference time leads to a sharp decrease in accuracy under attack, which indicates that both denoising and ensembling contribute significantly to the improved robustness. Nevertheless, removing adversarial training, though also introducing a degraded AUA score, is less detrimental than the other inference time components, showing that DiffuseDef is not dependent on a specific adversarial training method.
+
+Table 2: Accuracy under white-box attacks.  
+
+<table><tr><td>Method</td><td>TF</td><td>TB</td><td>BA</td></tr><tr><td>DiffuseDef</td><td>84.5</td><td>86.0</td><td>84.6</td></tr><tr><td>w/o ensembling</td><td>64.2</td><td>65.8</td><td>54.9</td></tr><tr><td>w/o diffusion denoising</td><td>57.1</td><td>58.4</td><td>47.7</td></tr><tr><td>w/o adv training</td><td>80.3</td><td>80.9</td><td>80.5</td></tr></table>
+
+Table 3: Ablation results for DiffuseDef's accuracy under attack on AGNews datasets.
+
+# 5.4 DiffuseDef + Adversarial Augmentation
+
+In Figure 2, we combine DiffuseDef with adversarial data augmentation and evaluate the robustness on AGNews. We first generate adversarial augmentation data using TextFooler and adversariely train the classifier with augmented training data. The augmented classifier is further performed with diffusion training, resulting in a DiffuseDef variant. We notice that adversarial training on augmented data helps improve the performance on all three attacks. After applying diffusion training, the adversarial performance significantly improves over the augmented model, which proves the flexibility of DiffuseDef to be applied with different types of adversarial training methods.
+
+![](images/76794dc530520391601d1ef9bd07ddb802f123980ad50ceed5916fb2e0bae3ba.jpg)  
+Figure 2: Robustness of DiffuseDef with textual adversarial augmentation method.
+
+# 5.5 Effect of Additional Denoising Steps
+
+In Figure 3, we study how the inference denoising steps  $t'$  can affect the adversarial performance. For the DiffuseDef model without ensembling, both the AUA score and the number of queries required to attack increase as the inference denoising step is larger. As the denoising step  $t'$  grows from 1 to 30, the AUA score improves from 58 to 65 while the number of attack queries grows from 430 to 780. In contrast, for DiffuseDef with ensembling, the model maintains a stable but robust performance in AUA and number of queries, regardless of the increase of  $t'$ . Considering that the ensembling introduces a notable performance increase, the DiffuseDef model is likely to be hitting an upper bound in both metrics, thus no further improvement is reached by increasing the denoising steps. However, it also shows that with ensembling, DiffuseDef can be applied with a smaller  $t'$  for better efficiency while maintaining a robust adversarial performance.
+
+![](images/ab42153bd199f86236dfd007b801bf7d0aa8ee002efd979f42d325db65f678ae.jpg)  
+Figure 3: AUA and #Query (Text,Follower) w.r.t inference denoising step for DiffuseDef w/ and w/o ensembling.
+
+# 5.6 Ensembling Diffused Hidden Representations
+
+In DiffuseDef, the text hidden state is diffused and ensembled to form a denoised hidden representation, which contributes significantly to the improved adversarial robustness. In this section, we study how the ensembling diffused hidden representation helps defend against adversarial attacks.
+
+![](images/8d7665cba3383afe9b14094e8822ed914942dbdf5a348c638d830d6a4367b12a.jpg)  
+Figure 4: Distribution of max token importance score in the AGNews test set.
+
+As mentioned in Section 2.1, attack methods need to first rank token importance based on its influence on prediction. Specifically, the importance score is calculated by comparing the changes in model prediction probabilities after removing each word. In Figure 4, we compare the density distribution of the max token importance score between FreeLB++ and its DiffuseDef counterpart. DiffuseDef shows a notably lower percentage than FreeLB++ when the max importance score is high, where the attacker can easily find the vulnerable token to construct adversarial examples. This difference shows that DiffuseDef can complicate the process of important word searching, which ac
+
+counts for the increased number of queries required for a successful attack.
+
+<table><tr><td>Method</td><td>L2</td><td>Cosine</td></tr><tr><td>FreeLB++</td><td>12.53</td><td>0.35</td></tr><tr><td>DiffuseDef-FreeLB++</td><td>10.66</td><td>0.27</td></tr><tr><td>RSMI</td><td>9.72</td><td>0.24</td></tr><tr><td>DiffuseDef-RSMI</td><td>8.61</td><td>0.21</td></tr></table>
+
+In addition, DiffuseDef mitigates the difference between clean and adversarial texts by reducing the distance between their hidden states. In Table 4, we report the L2 and cosine distance between clean and adversarial hidden states for FreeLB++ and RSMI. Both show lower L2 and cosine distance after applying DiffuseDef, indicating that ensembling diffused representation repositions the adversarial example closer to the clean example, leading to the model maintaining its predictions.
+
+# 5.7 Efficiency of DiffuseDef
+
+Table 4: L2 and cosine distance between hidden states for clean and adversarial texts.  
+
+<table><tr><td>Method</td><td>Params</td><td>FLOPS</td><td>Train</td></tr><tr><td>Fine-Tuned (BERT)</td><td>110M</td><td>46G</td><td>1x</td></tr><tr><td>EarlyRobust</td><td>82M</td><td>32G</td><td>0.8x</td></tr><tr><td>FreeLB++</td><td>110M</td><td>46G</td><td>10.5x</td></tr><tr><td>InfoBERT</td><td>110M</td><td>46G</td><td>6.5x</td></tr><tr><td>RSMI</td><td>110M</td><td>92G</td><td>1.25x</td></tr><tr><td>RanMask (k=10)</td><td>110M</td><td>459G</td><td>1.2x</td></tr><tr><td>SAFER (k=10)</td><td>110M</td><td>459G</td><td>1x</td></tr><tr><td>DiffuseDef (t&#x27; = 1, k = 10)</td><td>120M</td><td>96G</td><td>1.1x</td></tr><tr><td>DiffuseDef (t&#x27; = 5, k = 10)</td><td>120M</td><td>267G</td><td>1.1x</td></tr></table>
+
+Table 5: Efficiency comparison of DiffuseDef and other methods. Params: number of model parameters. FLOPS: number of floating point operations per second at inference time, calculated with batch size of 1 and sequence length of 256. Train: training time.
+
+Given that DiffuseDef adds additional denoising and ensembling steps during inference, it inevitably increases the computation time compared to its base model. To study its efficiency, we report the number of model parameters, inference FLOPS, and training time in Table 5. In addition to the defense methods in Table 1, we also compare the efficiency of DiffuseDef with two other SOTA ensembling-based defense methods, i.e., RanMask (Zeng et al., 2023) and SAFER (Ye et al., 2020).
+
+All SOTA models have the same number of parameters as the fine-tuned BERT model, except EarlyRobust, which applies attention head pruning for
+
+better efficiency. DiffuseDef requires more inference FLOPS than non-ensembling-based baselines such as FreeLB++ and EarlyRobust, due to the diffusion steps. With  $t' = 1$  and  $k = 10$ , the FLOPS for DiffuseDef doubles from 46G to 96G, nevertheless, this number is close to the RSMI model as it requires gradient information during inference. Despite this increase, DiffuseDef is more efficient than ensembling-based methods like RanMask and SAFER, which require a full forward pass for all ensembles. With the same ensembling number of 10, both RanMask and SAFER require 459G FLOPS, which is 10x the number for the BERT baseline. In contrast, even with  $t'$  increased to 5, DiffuseDef can be run faster with 267G FLOPS, mitigating the efficiency problem from ensembling while maintaining the benefit of improved robustness.
+
+Regarding training time, although training the diffusion layer requires more epochs, it costs similar time as training the classifier due to the small size of the diffusion layer. By contrast, adversarial training methods such as FreeLB++ and InfoBERT require much longer training time due to increased iterations of back-propagation per training batch.
+
+# 6 Conclusions
+
+We propose a novel adversarial defense method, DiffuseDef, which combines adversarial training, diffusion training, and ensembling to improve model robustness to adversarial attacks. DiffuseDef can build on any existing adversarial training method, training an additional diffusion layer to predict and remove randomly sampled noise at a given timestep. During inference, the diffusion layer is used to denoise the adversarial hidden states, which are ensembled to construct a robust text representation. Our experiments validate the effectiveness and efficiency of DiffuseDef, which significantly outperforms SOTA on common blackbox and white-box adversarial attack methods, and maintains the performance on clean texts. Analysis shows that DiffuseDef makes it difficult to find vulnerable tokens to attack, and also reduces the difference between the hidden representations of clean and adversarial texts.
+
+# Limitations
+
+Despite the fact that DiffuseDef is more efficient than existing ensembling-based methods, it still requires more model parameters and inference FLOPS than non-ensembling-based models
+
+to achieve better robustness. Future directions of this work might involve efforts to reduce the size of the diffusion layer and the number of ensembles to make DiffuseDef more efficient.
+
+# Ethical Considerations
+
+In this paper, we propose a new method DiffuseDef, which uses a diffusion layer as a denoiser to provide robust and efficient text representation. We demonstrate that the proposed method could significantly improve the robustness of NLP systems to adversarial attacks. However, DiffuseDef cannot defend against all adversarial attacks without limitations (e.g., number of perturbed words, semantic similarity between original and adversarial examples). Potential risks might include the creation of new adversarial attacks devised specifically for DiffuseDef.
+
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+
+# A Data Preparation
+
+<table><tr><td>Dataset</td><td>Train</td><td>Valid</td><td>Test</td><td>Avg Len</td></tr><tr><td>AGNews</td><td>108K</td><td>12K</td><td>7K</td><td>51.3</td></tr><tr><td>IMDB</td><td>40K</td><td>5K</td><td>5K</td><td>311.9</td></tr><tr><td>QNLI</td><td>94K</td><td>10K</td><td>5K</td><td>47.2</td></tr></table>
+
+Table 6: Dataset statistics. The average text length is counted with BertTokenizer.
+
+Table 6 presents the number of examples in train-valid/test splits and the average token length for the three datasets used in the experiments. For QNLI and AGNews datasets, we randomly split the training set into our train-valid splits, with a ratio of  $0.9 / 0.1$ , and use their test sets as our test split. For IMDB dataset, we randomly split the dataset
+
+into train-valid/test splits with a ratio of 0.8/0.1/0.1. All train-valid/test splitting is performed using a random seed of 42.
+
+# B Evaluation Constraints
+
+<table><tr><td>Dataset</td><td>εmin</td><td>Kmax</td><td>ρmax</td></tr><tr><td>AGNews</td><td>0.84</td><td>50</td><td>0.3</td></tr><tr><td>IMDB</td><td>0.84</td><td>50</td><td>0.1</td></tr><tr><td>QNLI</td><td>0.84</td><td>50</td><td>0.2</td></tr></table>
+
+When evaluating with adversarial attack, we follow the parameter settings for TextAttack as suggested in (Li et al., 2021). The minimum semantic similarity  $\varepsilon_{\mathrm{min}}$  between the clean text and adversarial text is set to 0.84, with the score computed using Universal Sentence Encoder (Cer et al., 2018). The maximum number of candidate substitutions  $\mathbf{K}_{\mathrm{max}}$  from the attacker is 50, thus the maximum number of queries  $\mathbf{Q}_{\mathrm{max}} = \mathbf{K}_{\mathrm{max}} \times \mathbf{L}$  where  $\mathbf{L}$  is the number of tokens. Finally, the maximum percentage of changed tokens  $\rho_{\mathrm{max}}$  is set to 0.3/0.1/0.2 for AGNews, IMDB, and QNLI datasets respectively.
+
+# C Training
+
+Table 7: Evaluation parameters for each dataset.  
+
+<table><tr><td></td><td>AGNews</td><td>IMDB</td><td>QNLI</td></tr><tr><td>Epochs</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Batch size</td><td>64</td><td>64</td><td>64</td></tr><tr><td>Sequence len</td><td>128</td><td>256</td><td>256</td></tr><tr><td>Dropout</td><td>0.1</td><td>0.1</td><td>0.1</td></tr><tr><td>Optimizer</td><td>AdamW</td><td>AdamW</td><td>AdamW</td></tr><tr><td>Lr</td><td>2e-5</td><td>2e-5</td><td>2e-5</td></tr><tr><td>t</td><td>30</td><td>10</td><td>30</td></tr><tr><td>t&#x27;</td><td>5</td><td>5</td><td>5</td></tr><tr><td>k</td><td>10</td><td>10</td><td>10</td></tr></table>
+
+The details on hyperparameters of diffusion training can be found in Table 8. All models are trained on a single RTX A6000 GPU. The diffusion training of 100 epochs takes  $6/4/3$  hours on AGNews, IMDB, and QNLI datasets respectively.
+
+# D License for Scientific Artifacts
+
+Table 9 lists the scientific artifacts, including data, codes, and models used in this paper. The use of these artifacts in this paper is consistent with their intended use, i.e., for scientific research only. The data used in the experiment is in English and does
+
+Table 8: Hyperparameters for training DiffuseDef.  
+
+<table><tr><td>Artifact</td><td>License</td></tr><tr><td>AGNews (Zhang et al., 2015b)</td><td>Custom (non-commercial)</td></tr><tr><td>IMDB (Maas et al., 2011b)</td><td>-</td></tr><tr><td>QNLI (Wang et al., 2018)</td><td>CC BY-SA 4.0</td></tr><tr><td>transformers (Wolf et al., 2020)</td><td>Apache License 2.0</td></tr><tr><td>TextAttack (Morris et al., 2020)</td><td>MIT License</td></tr><tr><td>BERT (Devlin et al., 2019)</td><td>Apache License 2.0</td></tr><tr><td>RoBERTa (Liu et al., 2019)</td><td>MIT License</td></tr></table>
+
+Table 9: Licenses of scientific artifacts used in this paper.
+
+not contain personally identifying information or offensive content.
+
+# E Robustness with RoBERTa
+
+In Table 10, we report the black-box attack results on RoBERTa-based models. A similar conclusion holds for BERT-based models, and DiffuseDef significantly outperforms other defense methods on all 3 datasets.
+
+# F Robustness w.r.t Token Length
+
+![](images/0823f9d47f90568e2d3ee61ecbba93a088656e7b7e1a6e48a2a5a4992d98112c.jpg)  
+Figure 5: Defense rate (against TextFollower) w.r.t token length for different models on IMDB dataset.
+
+Figure 5 provides a comparison of the defense rate for different models by token length on the IMDB dataset. The defense rate is calculated as the percentage of test examples in which TextFooler fails to construct a successful attack. All models except RSMI show a consistent trend that the defense rate declines as the texts lengthen. This trend can be attributed to the nature of adversarial attacks, as longer texts allow for the generation of more adversarial examples. Specifically, adversarial training defense methods like InfoBERT and FreeLB++ show poor performance on longer texts (more than 300 tokens), with the defense rate reduced to nearly 0. This drastic decline indicates
+
+<table><tr><td rowspan="2">Dataset</td><td rowspan="2">PLM</td><td rowspan="2">Method</td><td rowspan="2">Clean%</td><td colspan="3">AUA %</td><td colspan="3">#Query</td></tr><tr><td>TF</td><td>TB</td><td>BA</td><td>TF</td><td>TB</td><td>BA</td></tr><tr><td rowspan="6">AGNews</td><td rowspan="6">RoBERTa-base</td><td>Fine-Tuned</td><td>94.9</td><td>34.1</td><td>36.9</td><td>43.6</td><td>372</td><td>396</td><td>410</td></tr><tr><td>InfoBERT</td><td>95.5</td><td>40.2</td><td>45.2</td><td>48.6</td><td>392</td><td>421</td><td>430</td></tr><tr><td>FreeLB++</td><td>95.4</td><td>57.5</td><td>62.9</td><td>55.9</td><td>444</td><td>467</td><td>447</td></tr><tr><td>RSMI</td><td>93.1</td><td>64.2</td><td>66.4</td><td>67.4</td><td>774</td><td>861</td><td>808</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>95.3</td><td>85.6</td><td>87.6</td><td>85.3</td><td>880</td><td>976</td><td>906</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>92.9</td><td>82.9</td><td>83.5</td><td>82.2</td><td>905</td><td>925</td><td>1047</td></tr><tr><td rowspan="6">IMDB</td><td rowspan="6">RoBERTa-base</td><td>Fine-Tuned</td><td>94.6</td><td>21.3</td><td>17.9</td><td>13.6</td><td>587</td><td>671</td><td>493</td></tr><tr><td>InfoBERT</td><td>94.8</td><td>30.9</td><td>27.9</td><td>21.8</td><td>681</td><td>760</td><td>549</td></tr><tr><td>FreeLB++</td><td>95.3</td><td>46.0</td><td>42.1</td><td>33.9</td><td>829</td><td>974</td><td>637</td></tr><tr><td>RSMI</td><td>92.7</td><td>77.9</td><td>74.3</td><td>70.6</td><td>3443</td><td>4342</td><td>2619</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>95.0</td><td>86.2</td><td>85.9</td><td>86.8</td><td>3573</td><td>4663</td><td>2941</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>92.4</td><td>84.7</td><td>84.1</td><td>84.3</td><td>3673</td><td>4782</td><td>3007</td></tr><tr><td rowspan="6">QNLI</td><td rowspan="6">RoBERTa-base</td><td>Fine-Tuned</td><td>92.8</td><td>26.6</td><td>22.5</td><td>20.3</td><td>204</td><td>219</td><td>188</td></tr><tr><td>InfoBERT</td><td>92.5</td><td>29.1</td><td>25.8</td><td>20.3</td><td>205</td><td>223</td><td>189</td></tr><tr><td>FreeLB++</td><td>92.8</td><td>33.9</td><td>27.7</td><td>20</td><td>227</td><td>244</td><td>200</td></tr><tr><td>RSMI</td><td>89.3</td><td>38.8</td><td>33.2</td><td>30.0</td><td>340</td><td>375</td><td>343</td></tr><tr><td>DiffuseDef-FreeLB++ (Ours)</td><td>92.7</td><td>64.6</td><td>64.3</td><td>61.5</td><td>473</td><td>579</td><td>524</td></tr><tr><td>DiffuseDef-RSMI (Ours)</td><td>88.8</td><td>57.7</td><td>55.7</td><td>53.9</td><td>469</td><td>578</td><td>518</td></tr></table>
+
+Table 10: Main adversarial robustness results on classification tasks with RoBERTa. Clean: accuracy on clean test set. TF: TextFooler. TB: TextBugger. BA: BertAttack.
+
+that, given an adequate number of queries, the attacker is guaranteed to find a successful attack to fool these models. Similarly, EarlyRobust exhibits a performance drop on long texts as it is based on FreeLB training. RSMI, however, performs worse on short texts, but its defense rate increases as the text length grows. Compared to all SOTA defense approaches, the two DiffuseDef variants show a more steadily declining trend and maintain a higher defense rate across all token lengths, i.e., DiffuseDef is more robust to input text length.
+
+# G Example of noising and denoising in DiffuseDef
+
+Adding and removing noise to hidden states are essential features in DiffuseDef, which contribute to the improved adversarial robustness. To study how adding or removing noise can affect the semantic meaning of the text, we feed the hidden states to the pretrained BERT model with a masked language modeling (MLM) head to generate the text output.
+
+In Table 11, we present the MLM outputs from hidden states added with different steps of noise and the MLM outputs from noise hidden states denoised with the same number of steps. In the example shown, with more noise added some semantic information can be lost and replaced by symbols or function words like “.” or “the”. In contrast, denoising for the same number of steps helps alleviate such information loss. For example, the word “IBM” can be recovered from the noise.
+
+However, in practice, it is not possible to assume
+
+the number of denoising steps; therefore in Table 12 we show the MLM outputs of denoised hidden states directly from clean and adversarial texts. On clean text, we observe that a higher number of denoising steps can result in more abstraction of the texts. For example, more words are replaced with "the" in the MLM outputs as  $t'$  grows. However, words related to the topic (e.g., "Manchester United", "Liverpool") are kept during the denoising process, thus the model can predict correctly. Similarly, the trend of abstraction can also be found on adversarial text while we observe that the denoising can help remove the adversarial noise/perturbation and recover the word "united" from "nation", thus resulting in its correct prediction on the adversarial text.
+
+# H Confusion Matrix under Attack
+
+Figure 6 and 7 present the confusion matrices of models' predictions on clean text and on adversarial texts (successful attack example) on AGNews and IMDB test sets respectively.
+
+<table><tr><td>t&#x27;</td><td>MLM Output (add noise)</td><td>MLM Output (add noise then denoise)</td></tr><tr><td>0</td><td>IBM Chips May Someday Heal Themselves New technology applies electrical fuses to help identify and repair faults.</td><td>-</td></tr><tr><td>5</td><td>the.ibm chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td><td>the.ibm chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td></tr><tr><td>6</td><td>)IBM chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td><td>the.ibm chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td></tr><tr><td>7</td><td>the.chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td><td>the.chips may someday heal themselves new technology uses electrical fuses to help identify and repair faults.</td></tr><tr><td>8</td><td>).. may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td><td>the.ibm. may someday heal themselves new technology uses electrical fuses to help identify and repair faults.</td></tr><tr><td>9</td><td>the.ibm chips may someday heal themselves new technology uses electrical fuses to help identify and repair faults.</td><td>the.ibm chips may someday heal themselves new technology introduces electrical fuses to help identify and repair faults.</td></tr><tr><td>10</td><td>the.chips may someday heal themselves new technology extends electrical fuses to help identify and repair faults.</td><td>the.ibm. may someday heal themselves new technology develops electrical fuses to help identify and repair faults.</td></tr></table>
+
+Table 11: MLM outputs from hidden states with noise added and hidden states with first noise added but then denoised. We only report  $t'$  above 5 as the MLM outputs with smaller  $t'$  are identical to the clean text.  
+
+<table><tr><td>t&#x27;</td><td>Clean Text / MLM Output</td><td>Adv Text / MLM Output</td><td>Pred clean</td><td>Pred adv</td></tr><tr><td>0</td><td>United Apology over Website Abuse Manchester United have been forced to issue an embarrassing apology to Liverpool for an ill-advised attack on the Anfield outfit on its own website.</td><td>United Apology over Website Abuse Manchester Nations have been forced to issue an embarrassing apology to Liverpool for an ill-advised attack on the Anfield outfit on its own website.</td><td>Sports</td><td>World</td></tr><tr><td>1</td><td>football: apology over website abuse manch-ester united have been - to issue an embar-rassing apology to liverpool for an the - advised attack on the anfield outfit on its own website.</td><td>the: apology over website abuse manchester nations have been the to issue an embarrass-ing apology to liverpool for an the - advised attack on the anfield outfit on its own web-site.</td><td>Sports</td><td>World</td></tr><tr><td>2</td><td>the: apology over website abuse manch-ester united have been - to issue an embarrassa-ing apology to liverpool for an the - advised at-tack on the anfield outfit on its own website.</td><td>the: apology over website abuse manchester nations have been the to issue an embarrassa-ing apology to liverpool for an the - advised attack on the anfield outfit on its own web-site.</td><td>Sports</td><td>World</td></tr><tr><td>3</td><td>the: apology over website abuse manch-ester united have been the to issue an embarrassa-ing apology to liverpool for an the - advised attack on the anfield outfit on its own web-site.</td><td>the: apology over website abuse manchester s have been the to issue an embarrassa-ing apology to liverpool for an the - advised at-tack on the anfield outfit on its own website.</td><td>Sports</td><td>Sports</td></tr><tr><td>4</td><td>the: apology over website abuse manch-ester united have the - to issue an embarrassa-ing apology to liverpool for an the - advised at-tack on the anfield outfit on its own website.</td><td>the: apology over website abuse manchester s have been the to issue an embarrassa-ing apology to liverpool for an the - advised at-tack on the anfield outfit on its own website.</td><td>Sports</td><td>Sports</td></tr><tr><td>5</td><td>the: apology over website abuse manch-ester united have the the to issue an a apology to liverpool for an the - advised attack on the anfield outfit on its own website.</td><td>the: apology over website abuse manchester united have been the to issue an the apology to liverpool for an - advised attack on the anfield outfit on its own website.</td><td>Sports</td><td>Sports</td></tr></table>
+
+Table 12: MLM outputs and FreeLB++ model predictions from ensembling diffused hidden states at different denoising steps.
+
+![](images/cbf38a4fcd2ea29586b521d884f415d18c6210e9bcf37d4dae3a69c3d4aeebfc.jpg)
+
+![](images/6c04aa12f89b8c77f44cb42646dae16bc076ed4349f54afef3af62239130b901.jpg)
+
+![](images/e2db8de4d178423a3fe7fcd28c055cba1cc57a6dad78263958b3e81fb90799e9.jpg)  
+Figure 6: Confusion matrix of models under attack on AGNews test set.
+
+![](images/8c8150ff49d8522dc4c533951338931859bd2a24cadb7d513f19353166df77a3.jpg)
+
+![](images/776488d414b689c0b6845960dbeb430b84d452ab1d4f2598c653805f35019974.jpg)
+
+![](images/6b2a92741011dd735f7d652ecc290fa244c364554fe60cd53b295343216db5a0.jpg)
+
+![](images/05b6617a41a744ee744c3272d609dba308b972f4a3c532403e6b64692c9a2a70.jpg)  
+Figure 7: Confusion matrix of models under attack on IMDB test set.
+
+![](images/bbbed12af8ae8dff04e989a3b38a8b26028541ff349248752aec9114a0f2d93a.jpg)

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+# Diffusion Models Through a Global Lens: Are They Culturally Inclusive?
+
+Zahra Bayramli $^{1,3*}$  Ayhan Suleymanzade $^{1*}$  Na Min An $^{2}$  Huzama Ahmad $^{2}$  Eunsu Kim $^{1}$  Junyeong Park $^{1}$  James Thorne $^{2‡}$  Alice Oh $^{1‡}$
+
+$^{1}$ School of Computing, KAIST  $^{2}$ Graduate School of AI, KAIST  $^{3}$ SOCAR
+
+# Abstract
+
+Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent various cultural nuances remains an open question. In our work, we introduce CULTDIFF benchmark, evaluating whether state-of-the-art diffusion models can generate culturally specific images spanning ten countries. We show that these models often fail to generate cultural artifacts in architecture, clothing, and food, especially for underrepresented country regions, by conducting a fine-grained analysis of different similarity aspects, revealing significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images. With the collected human evaluations, we develop a neural-based image-image similarity metric, namely, CULTDIFF-S, to predict human judgment on real and generated images with cultural artifacts. Our work highlights the need for more inclusive generative AI systems and equitable dataset representation over a wide range of cultures.
+
+# 1 Introduction
+
+Text-to-image (T2I) diffusion models recently have shown a significant advance in generating high-quality images from text prompts(Saharia et al., 2022; Ramesh et al., 2022; Betker et al., 2023; Podell et al., 2023; Blattmann et al., 2023; Esser et al., 2024). Remarkable success has been demonstrated in producing realistic and detailed visual representations, ranging from generating complex scenes (Liu et al., 2024c) to performing style transfer using diffusion models (Wang et al., 2023; Zhang et al., 2023). While it is crucial to address
+
+the challenge of generating culture-aware imagery, current diffusion models have yet to be evaluated on their ability to create a distribution of accurate and fair representations of images (Cao et al., 2024b; Liu et al., 2023).
+
+A recent study, Basu et al. (2023) reveals that while current diffusion models can generate realistic and contextually relevant images, they frequently fail to achieve geographical inclusiveness, generating globally representative images. Furthermore, a critical gap remains in these models' ability to produce culturally accurate representations, failing to create concepts or artifacts for particular cultures, thus, lacking in culture awareness (Kannen et al., 2024). While benchmark datasets have been developed to capture cultural diversity (Jha et al., 2023; Myung et al., 2024; Li et al., 2024c), to the best of our knowledge, there is limited prior work exploring the ability of diffusion models to generate culturally specific artifacts, particularly those representing underrepresented cultures (Kannen et al., 2024; Nayak et al., 2024).
+
+The primary motivation of our study is to investigate the capability of state-of-the-art diffusion models in accurately representing cultural themes for both overrepresented and underrepresented cultures. We consider culture as societal constructs tied to country boundaries and use 'culture' and 'country' interchangeably in this paper, similar to prior approaches (Kannen et al., 2024; Li et al., 2024b). The overrepresented cultures defined here include countries from Western, European, Industrialized, Rich, and Democratic (WEIRD) regions (Henrich et al., 2010) (e.g., United States, United Kingdom), as well as countries from Asia, based on their Asia Power Index<sup>1</sup> (e.g., China, South Korea). In contrast, countries where the primary languages (e.g., Amharic, Azerbaijani) have limited resource availability, as defined in Joshi et al. (2020), are
+
+considered underrepresented (e.g., Ethiopia, Azerbaijan).
+
+In this paper, we present a new benchmark dataset designed to evaluate these models' ability to generate images that accurately reflect both high-resource and low-resource cultures<sup>2</sup>. Our benchmarking dataset includes a curated collection of prompts for architectural landmarks, clothing, and food from various countries to ensure comprehensive coverage of cultural diversity. Since existing image similarity metrics (e.g., FID (Heusel et al., 2017)) struggle to evaluate cultural nuances accurately, we propose a model-based metric trained and validated with human feedback and compare its correlation. Our contributions can be summarized as follows:
+
+- CultDiff benchmark dataset: We introduce a novel dataset, CultDiff, specifically designed to evaluate diffusion models on their ability to generate culturally accurate images across ten countries with varying resource levels, incorporating human evaluations.  
+- Analysis on various culture representations of generated images: We explore how well diffusion models represent low- and high-resource cultures, providing a fine-grained analysis of different aspects of similarity (e.g., image-image, image-description).  
+- An automatic image-image similarity evaluation metric: We present a similarity metric trained and validated with human feedback and compare its capabilities to existing similarity metrics, demonstrating its potential to better capture cultural nuances in generated images.
+
+# 2 Related Works
+
+# 2.1 Cultural-Aware Datasets
+
+Beginning with the benchmark to evaluate image quality from simpler text prompts using text-to-image (T2I) diffusion models, such as HPDv2 (Wu et al., 2023) using human preferences, more recently advanced efforts include creating comprehensive benchmarks such as T2I-CompBench (Huang et al., 2023) and GenAI-Bench (Li et al., 2024a). These prior benchmarks assess the performance of T2I models across various aspects,
+
+including realism, fidelity, and compositional text-to-image generation. Although a very recent study (Jha et al., 2024) has started investigating global representation in T2I models, their emphasis has primarily been on regional social stereotypes. From another perspective, Khanuja et al. (2024) proposed the task of image transcreation for cultural relevance demonstrating that image-editing models struggle to adapt images meaningfully across cultures.
+
+On the other hand, many widely-used datasets for training T2I synthesis models, such as LAION-400M (Schuhmann et al., 2021), tend to exhibit Anglo-centric and Euro-centric biases, as noted by Birhane et al. (2021). These biases skew the representation of cultures in generated images, often favoring Western perspectives (Mihalcea et al., 2024). In response to this, several researchers have worked to create datasets that better represent diverse cultures. For example, the MaRVL dataset (Liu et al., 2021) was specifically designed to include a broader array of languages and cultural concepts, covering regions such as Indonesia, Swahili-speaking Africa, Tamil-speaking South Asia, Turkey, and Mandarin-speaking China and addressing biases in datasets that predominantly focused on North American and Western European cultures. Likewise, Culturally-aware Image Captioning (CIC) framework (Yun and Kim, 2024) was proposed to enhance cultural diversity in image descriptions.
+
+In the Large Language Models (LLMs) domains, SeeGULL dataset (Jha et al., 2023) broadens stereotype benchmarking to encompass global and regional diversity, and BLEnD benchmark (Myung et al., 2024) evaluates the cultural knowledge of LLMs across various languages, including lowresource ones. Similarly, the Dollar Street dataset (Gaviria Rojas et al., 2022) sought to capture every day life across a wide variety of socioeconomic backgrounds, presenting a more globally inclusive view. Furthermore, CulText2I (Ventura et al., 2023) was introduced as a multilingual benchmark and evaluation framework to analyze cultural encoding in TTI models across languages, cultural concepts, and domains, highlighting how language impacts cultural representation in generated images. In addition, Liu et al. (2023) introduced the CCUB dataset, which was developed to promote cultural inclusivity by collecting images and captions representing the cultural contexts of different countries. Liu et al. (2024a) introduced a Chal
+
+![](images/35fbf315bf64c5ee31a05a9f5e0aeb338ae79825c98d643eaa17d3f5d0cfaff0.jpg)  
+Figure 1: The overall framework for image generation, annotation, and evaluation, consisting of six steps. (1) 50 text prompts for three categories—architecture, food, and clothing—across 10 countries were generated. (2) Reference images were collected using Bing search. (3) Three diffusion models (SD-XL, SD-3-Medium, FLUX.1-dev) were used to generate images. (4) Human annotators evaluated images based on similarity, description match, and realism. (5) Human feedback was used to create positive and negative pairs for contrastive learning. (6) Model performance was assessed by measuring agreement with human scores.
+
+lenging Cross-Cultural (C3) benchmark to evaluate failures in culturally specific generation and proposed fine-tuning on curated data. In the multimodal large language model (MLLM) domain, recent benchmarks such as CVQA (Romero et al., 2024) focused on culturally aware question answering by capturing nuances across a diverse set of languages. Most recently, CUBE (Kannen et al., 2024) is a large-scale dataset of cultural artifacts spanning 8 countries across different geo-cultural regions for evaluating cultural diversity. Our work further contributes to dataset creation by expanding the focus to include low-resource cultures, thereby addressing gaps between overrepresented and underrepresented cultural contexts.
+
+# 2.2 Diffusion Model Evaluation
+
+Several metrics have been developed and widely used to evaluate the quality of images generated by T2I models. These include measures of realism such as the Inception Score (IS) (Salimans et al.,
+
+2016), Fréchet Inception Distance (FID) (Heusel et al., 2017), and Image Realism Score (IRS) (Chen et al., 2023). In particular, IS evaluates image quality and diversity based on classification probabilities, FID quantifies the similarity between generated and real image distributions, and IRS mainly analyzes basic image characteristics to measure the realism of the visual content. In addition, the alignment between generated images and the corresponding prompts has been evaluated using various metrics, such as CLIPScore (Hessel et al., 2021), VQA Score (Lin et al., 2025), and ImageReward (Xu et al., 2023), which incorporates human feedback to enhance T2I models further. Although Kannen et al. (2024) has explored cultural diversity in text-to-image model generations using the Vendi Score (Nguyen and Dieng, 2024), measuring cultural accuracy in generated images has not yet been successfully achieved with existing metrics. As a result, the most reliable approach remains relying on human participants, as demonstrated in several
+
+works (Kannen et al., 2024; Nayak et al., 2024). In our work, we also utilize human annotation; however, due to the time and cost associated with it, there is a pressing need for automatic evaluation. To address this, we present a metric specifically trained on a culture-aware dataset to observe its potential for aligning with human preferences.
+
+# 3 CULTDIFF Benchmark
+
+This section outlines the process of building our CULTDIFF benchmark, including the data construction (steps 1-3 in Figure 1 and Section 3.1), human annotation (step 4 in Figure 1 and Section 3.2), and design of the architectural framework for the metric evaluation (steps 5-6 in Figure 1 and Section 3.3).
+
+# 3.1 CULTDIFF Dataset Construction
+
+In this section, we present the construction of the CULTDIFF dataset, which is designed to evaluate the performance of diffusion models in generating culturally relevant images across various artifact categories and countries. The dataset creation process is composed of three key steps as outlined in Figure 1: (1) prompt generation, (2) collection of real images, and (3) synthetic image generation.
+
+Prompt Generation. To create the benchmark dataset for evaluating the diffusion models, we focused on three artifact categories: architecture (or landmarks), clothing, and food across ten countries. The prompt generation process involved the following steps: (1) Artifact Collection: Compiled a list of 50 artifact names per category and country using Wikipedia, cultural heritage websites, and travel platforms<sup>3</sup> for landmarks. (2) Prompt Engineering: Created custom-generated prompts for each artifact which are simple, structured prompts (e.g., "A panoramic view of {landmark} in {country}", realistic.) similar to the prompts used in Kannen et al. (2024) (refer to Appendix A.1 for the list of prompts).
+
+Image Collection and Generation. To acquire real images for training and evaluation, we collected images for each artifact across all categories and countries from the internet using the generated prompts. Recognizing that real-world images vary significantly due to factors such as location, camera angle, lighting, and weather conditions, we aimed to minimize potential biases by selecting five images instead of just one for each artifact.
+
+![](images/f2fb757572d076d0ad06b3773c64a2ecff9db9af7c8ce58e84be4654eb8db99d.jpg)  
+Figure 2: Proposed contrastive learning pipeline with image pairs using Vision Transformer (ViT).
+
+The images were scraped using Bulk Bing Image Downloader (BBID) $^4$ , an open-source Python tool that enables fast downloads and filters out adult content.
+
+The image generation process involved synthesizing images based on the generated prompts. We used three diffusion models: Stable Diffusion XL (Podell et al., 2023), Stable Diffusion 3 Medium Diffusers (Esser et al., 2024), and FLUX.1-dev $^5$  (Labs, 2023).
+
+# 3.2 Human Evaluation
+
+A critical part of our experimental design involved human annotation to assess the cultural and contextual relevance of AI-generated images. We first created a survey comprising 150 images for all ten countries (e.g., United States, China, Azerbaijan, and Indonesia), where the images for each country were equally divided across three categories: architecture, clothing, and food items. Each survey included a set of four images for each artifact: three real images (collected via web scraping) and one AI-generated image (produced by one of the three diffusion models).
+
+To prevent model bias, each human annotator was distributed with randomly selected images generated from one of three diffusion models for each question. Hence, the degree of annotator agreement implies varying image quality generated by different models (Appendix A.3 for details). Three human annotators from each country participated in the study, ensuring cultural familiarity and context-specific insights. All 30 annotators were asked to evaluate each image based on the following multiple-choice questions:
+
+Image-image similarity scores are collected for each category as follows:
+
+- Architecture: How similar is the rightmost image (i.e., AI-generated image) to the first three images (i.e., real reference images) in terms of overall similarity, shapes, materials/textures, and background/surroundings?  
+- Clothing: How similar is the rightmost image to the first three images in terms of overall similarity, color/texture, design/patterns, and background elements?  
+- Food: How similar is the rightmost image to the first three images regarding overall similarity, presentation/plating, colors/textures, and ingredients/components?
+
+Image-text alignment scores are calculated how well the description (i.e., prompts mentioned in Section 3.1) matches the content of the AI-generated image?
+
+Realism scores are assessed based on how realistic the AI-generated image appears.
+
+Each question for these three multiple-choice subquestions was rated using a Likert scale of 1 to 5: (1) Not at all, (2) Slightly, (3) Somewhat, (4) Mostly, (5) Completely, allowing annotators to express their level of agreement or similarity with the presented image (detailed in Appendix A.3). We recruited 30 annotators from 10 countries: Azerbaijan, Pakistan, Ethiopia, South Korea, Indonesia, China, Spain, Mexico, the United States, and the United Kingdom. Annotators from the first five countries were recruited through college communities, while those from the remaining countries were recruited via the Prolific crowdsourcing platform. The recruitment on Prolific involved screening participants based on their location, nationality, and birthplace.
+
+# 3.3 Model Evaluation
+
+# 3.3.1 Pair and Dataset Creation
+
+Our training data consists of real image and realistic image pairs, ensuring a rich, wide variety of training samples. Specifically, we constructed the following image pairs:
+
+Real image pairs: Positive pairs consist of real images of the same artifact (e.g., two images of the Flame Towers in Azerbaijan), while negative
+
+pairs include real images of different artifacts from the same country or any artifacts from different countries (e.g., the Flame Towers vs. the Empire State Building).
+
+Real-synthetic image pairs: The positive image pairs generated by diffusion models (as described in Section 3.1) with an average image-image similarity score  $\geq 3$ , paired with three real images used in the human evaluation. The negative pairs consist of AI-generated images with an average image-image similarity score  $< 3$ , paired with three real images similar to the positive pairs.
+
+We split our dataset using disjoint prompts for each set to ensure no overlap between the training, validation, and test sets. We selected 30 random prompts for the training set, 10 for the validation set, and 10 for the test set, along with their respective pairs from each model, across all categories and countries. In total, the training set consists of approximately  $11\mathrm{k}$  image pairs, while both the validation and test sets contain  $2.7\mathrm{k}$  image pairs each. The training set includes both real image pairs and real-synthetic image pairs, while the validation and test sets consist solely of real-synthetic image pairs, with no overlapping images between the sets.
+
+# 3.3.2 Similarity Metric
+
+To build a model-based image-image similarity metric, CULTDIFF-S, we adopted a contrastive learning approach, utilizing Vision Transformers (ViT) (Alexey, 2020) to learn embeddings for both real and AI-generated images, as illustrated in Figure 2. The global attention mechanism in ViT enables effective "local-to-global" correspondence, enhancing self-supervised learning (Caron et al., 2021; Mo et al., 2023). This ViT property is particularly important in our work, where it helps evaluate the cultural and contextual relevance of images by capturing both local features and their broader context. To achieve this, we employed a weighted margin loss during the training process, utilizing the scores (collected from our human annotation process explained in Section 3.2) as weights ( $w_i$ ,  $i = 1, 2, \dots, N$  in Eq. 3.3.2), motivated by previous studies that utilized GPT feedback scores as the weights in building sentence embedding models (Cheng et al., 2023; An et al., 2024). These scores are normalized to 0 to 1 and applied to their corresponding real-synthetic (human-annotated) positive and negative image pairs. For real-image pairs that were not annotated by humans, we assigned a default weight of  $w = 1$ . Our margin loss is
+
+<table><tr><td>Diffusion Models</td><td colspan="3">Stable Diffusion XL</td><td colspan="3">Stable Diffusion 3 Medium Diffusers</td><td colspan="3">FLUX.1 dev</td></tr><tr><td>Category</td><td>Architecture</td><td>Clothing</td><td>Food</td><td>Architecture</td><td>Clothing</td><td>Food</td><td>Architecture</td><td>Clothing</td><td>Food</td></tr><tr><td rowspan="5">Q1.1-Highest Score</td><td>S. Korea</td><td>Pakistan</td><td>USA</td><td>Azerbaijan</td><td>Mexico</td><td>Mexico</td><td>UK</td><td>Mexico</td><td>USA</td></tr><tr><td>UK</td><td>S. Korea</td><td>Spain</td><td>Mexico</td><td>UK</td><td>USA</td><td>S. Korea</td><td>Pakistan</td><td>Pakistan</td></tr><tr><td>Mexico</td><td>Mexico</td><td>Pakistan</td><td>S. Korea</td><td>S. Korea</td><td>Pakistan</td><td>Mexico</td><td>S. Korea</td><td>Mexico</td></tr><tr><td>USA</td><td>USA</td><td>Mexico</td><td>UK</td><td>Pakistan</td><td>Spain</td><td>USA</td><td>UK</td><td>UK</td></tr><tr><td>Ethiopia</td><td>UK</td><td>Indonesia</td><td>Ethiopia</td><td>Indonesia</td><td>UK</td><td>Ethiopia</td><td>USA</td><td>Spain</td></tr><tr><td rowspan="5">Q1.1-Lowest Score</td><td>Spain</td><td>Indonesia</td><td>UK</td><td>USA</td><td>USA</td><td>Indonesia</td><td>Azerbaijan</td><td>Indonesia</td><td>Azerbaijan</td></tr><tr><td>Azerbaijan</td><td>Azerbaijan</td><td>Azerbaijan</td><td>Indonesia</td><td>Azerbaijan</td><td>Azerbaijan</td><td>China</td><td>Azerbaijan</td><td>China</td></tr><tr><td>Pakistan</td><td>Spain</td><td>S. Korea</td><td>China</td><td>Spain</td><td>Ethiopia</td><td>Indonesia</td><td>Spain</td><td>Indonesia</td></tr><tr><td>Indonesia</td><td>Ethiopia</td><td>Ethiopia</td><td>Spain</td><td>Ethiopia</td><td>China</td><td>Pakistan</td><td>China</td><td>S. Korea</td></tr><tr><td>China</td><td>China</td><td>China</td><td>Pakistan</td><td>China</td><td>S. Korea</td><td>Spain</td><td>Ethiopia</td><td>Ethiopia</td></tr><tr><td rowspan="5">Q1.2-Highest Score</td><td>S. Korea</td><td>S. Korea</td><td>USA</td><td>USA</td><td>UK</td><td>USA</td><td>USA</td><td>S. Korea</td><td>USA</td></tr><tr><td>Mexico</td><td>UK</td><td>Spain</td><td>Mexico</td><td>S. Korea</td><td>Spain</td><td>UK</td><td>Indonesia</td><td>Spain</td></tr><tr><td>UK</td><td>Mexico</td><td>Pakistan</td><td>S. Korea</td><td>Mexico</td><td>Pakistan</td><td>Mexico</td><td>Mexico</td><td>Azerbaijan</td></tr><tr><td>USA</td><td>USA</td><td>Indonesia</td><td>Azerbaijan</td><td>Indonesia</td><td>Mexico</td><td>S. Korea</td><td>Pakistan</td><td>Pakistan</td></tr><tr><td>Spain</td><td>Indonesia</td><td>Azerbaijan</td><td>UK</td><td>Pakistan</td><td>China</td><td>Ethiopia</td><td>USA</td><td>Mexico</td></tr><tr><td rowspan="5">Q1.2-Lowest Score</td><td>Ethiopia</td><td>Pakistan</td><td>S. Korea</td><td>Ethiopia</td><td>USA</td><td>Indonesia</td><td>Azerbaijan</td><td>UK</td><td>UK</td></tr><tr><td>Azerbaijan</td><td>Azerbaijan</td><td>Mexico</td><td>Pakistan</td><td>Azerbaijan</td><td>Azerbaijan</td><td>China</td><td>Azerbaijan</td><td>China</td></tr><tr><td>Pakistan</td><td>Spain</td><td>UK</td><td>China</td><td>China</td><td>UK</td><td>Pakistan</td><td>Spain</td><td>S. Korea</td></tr><tr><td>China</td><td>Ethiopia</td><td>Ethiopia</td><td>Spain</td><td>Ethiopia</td><td>S. Korea</td><td>Spain</td><td>China</td><td>Indonesia</td></tr><tr><td>Indonesia</td><td>China</td><td>China</td><td>Indonesia</td><td>Spain</td><td>Ethiopia</td><td>Indonesia</td><td>Ethiopia</td><td>Ethiopia</td></tr></table>
+
+Table 1: Ranking of countries by average Q1.1 scores (top half) and Q1.2 scores (bottom half), organized from highest to lowest. The table presents scores across three models for the categories of Architecture, Clothing, and Food. Overrepresented countries are highlighted in red, while underrepresented countries are highlighted in blue.
+
+defined as:
+
+$$
+\mathcal {L} = \frac {1}{N} \sum_ {i = 1} ^ {N} w _ {i} \cdot \left(y _ {i} \cdot d _ {i} ^ {2} + (1 - y _ {i}) \cdot \max  (0, m - d _ {i}) ^ {2}\right),
+$$
+
+where  $w_{i}$  denotes the weight derived from the normalized human score when available (default is 1.0),  $d_{i}$  represents the Euclidean distance between the embeddings of a pair of images  $(d_{i} = \| f(x_{i1}) - f(x_{i2})\| _2)$ , and  $f(x)$  is the embedding function learned by the Vision Transformer. The binary label  $y_{i}\in \{0,1\}$  indicates whether a pair is positive  $(y_{i} = 1)$  or negative  $(y_{i} = 0)$ . The margin  $m$  enforces a minimum distance between embeddings of negative pairs, while  $N$  is the total number of image pairs in the batch.
+
+This loss function is optimized to make the model learn to (1) push the positive image pairs together and pull the negative image pairs apart and (2) assign importance to pairs with human judgment scores. The second goal guides the model to attend more to the image pairs with higher human scores and less with lower human scores.
+
+# 4 Results
+
+# 4.1 Benchmark Results and Analysis
+
+# 4.1.1 Overall Image-Image Similarity
+
+We first analyze the similarity results (image-image similarity scores) of the real and generated images from the survey's first question (Q1), which examined various aspects of similarity. In particular,
+
+Q1.1 asked participants to rate the overall similarity. Table 1 highlights the average scores for all countries across three models. The first half of Table 1 summarizes the country results: overrepresented countries (South Korea, USA, UK, Mexico) predominantly occupy the top half (highest scores), while underrepresented countries (Azerbaijan, Ethiopia) are mostly in the lower half, with some exceptions.
+
+Q1.2 specifically addressed distinct features: shapes for architectural structures, color/texture for clothing, and presentation/plating for food. The results, summarized in Table 1, highlight that the USA, UK, and South Korea consistently ranked among the highest-scoring countries across all categories and models. In contrast, Ethiopia and Indonesia frequently appeared in the lower half of the rankings. Interestingly, China also consistently received some of the lowest scores in this analysis. These findings suggest a need for the development of diffusion models that better represent underrepresented countries, such as Ethiopia and Indonesia, to improve their cultural and contextual accuracy compared to overrepresented countries like the USA, UK, and South Korea.
+
+# 4.1.2 Image-Description Match
+
+Our analysis focused on the responses to the second question (Q2), which assessed the fidelity of the generated content to the provided description. Figure 3 presents the results for the FLUX model's
+
+![](images/74325ec1e5022df8b4ab9bbdb0eff555ae2d05732eb91fd73e67e82a3c0c8583.jpg)  
+Figure 3: Average description match scores for the FLUX model's outputs across three categories: Architecture (left), Clothing (center), and Food (right). Each bar plot represents the average description match scores for countries within the respective category, ranked from highest to lowest. Error bars indicate the standard error of the mean.
+
+![](images/51c232721376e1ab2e1dc1c31ed07973048ca57f1124d94e444d43aa7208d34a.jpg)
+
+![](images/f1461708768fc2104634de5c37157dc57855d272347e893f12d14a1ac2c06315.jpg)
+
+![](images/a1d411ed76fd20da1557b99f59fde4ad65f97a1d626b9547bb18835a12297155.jpg)  
+Figure 4: Scatter plots of Q1.1 (horizontal axis) vs. Q2 (vertical axis) scores for Food images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+outputs across architecture, clothing, and food, respectively. The United States and the United Kingdom consistently achieved the highest average fidelity scores across all three categories. In contrast, Azerbaijan, South Korea, and Ethiopia recorded the lowest average scores of 2.28, 1.55, and 1.54 in the architecture, food, and clothing categories, respectively. It is important to note that there is no significant statistical difference between the overrepresented and underrepresented countries in our study, likely due to the small dataset. However, the country rankings remain relevant. In fact, these average scores reveal a noticeable gap between the scores of the United States and those of other countries across all categories. These results suggest a potential bias in the model, favoring countries with a more extensive cultural presence on the internet. Addressing this limitation by incorporating more diverse and balanced datasets could improve the model's fidelity for underrepresented countries and
+
+enhance its overall cultural accuracy (Ananthram et al., 2024; Liu et al., 2025).
+
+Results for Stable Diffusion XL and Stable Diffusion 3 Medium Diffusers (see Appendix A.4 for corresponding graphs) also indicate that the United States consistently receives higher scores. Conversely, countries like Azerbaijan and Ethiopia frequently appear in the lower half of the rankings. However, the relative ordering of countries varies across models.
+
+We also visualized the correlations between responses to the overall similarity question (Q1.1) for real and generated images and the text-image alignment score (Q2). As shown in Figure 4, most countries exhibit a strong positive Pearson correlation coefficient  $(r)$  in the Food category (refer to Appendix A.5 for results on architecture and clothing), indicating consistent agreement between Q1.1 and Q2 responses across most countries.
+
+![](images/ca0ac40376449cc751a8a52811d5b99ab9ddfdf9d8242cc5cb51ecdcd3696344.jpg)  
+Figure 5: Average realism scores across three models: Stable Diffusion XL (left), Stable Diffusion 3 Medium Diffusers (center), and FLUX (right). The bar plots show realism scores for three categories—Architecture, Clothing, and Food—averaged across countries for each model. Error bars indicate the standard error of the mean.
+
+![](images/e756e3b40136f63656f1f8b2a6280342a5bcd6d933ce5006601ae1d860d97167.jpg)
+
+![](images/e4540d458c44e56030f0cfe8428ab6ff7ec770f15c8a89cb1135714a22cb89e9.jpg)
+
+# 4.1.3 Realism
+
+The third question, which focused on rating the realism of the generated images, is visualized in the bar graphs in Figure 5. The FLUX model's output shows that realism scores are relatively higher for the United Kingdom, United States, and Mexico, while Spain, Ethiopia, and South Korea have lower scores. In contrast, the Stable Diffusion 3 Medium model exhibits relatively consistent realism scores across all countries. Meanwhile, the Stable Diffusion XL model demonstrates slightly lower average realism scores across all countries and categories compared to the other two models.
+
+# 4.2 Similarity Metric Analysis
+
+After training the similarity model, we evaluated its performance using our test dataset pairs. For each evaluation pair, we computed additional similarity metrics, including Fréchet Inception Distance (FID) (Heusel et al., 2017), Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al., 2018), and the Structural Similarity Index Measure (SSIM) (Wang et al., 2004). Additionally, we calculated the correlation between image-image similarity scores—derived by averaging the responses to the first four questions in the survey—and each of these similarity metrics. In our metric model, we compute the cosine similarity of the embeddings produced by the model during evaluation as the final similarity score. The results of these correlations are summarized in Table 2. The proposed metric, CULTDIFF-S, achieves notably higher correlation values, suggesting the usefulness of using our automatic pre-trained image-image metric, showing potential to replace the previous metrics. However, we leave future work to further enhance model correlations with human judgments.
+
+<table><tr><td>Similarity Metrics</td><td>Spearman ρ</td><td>Pearson r</td><td>Kendall τb</td><td>Kendall τc</td></tr><tr><td>SISM</td><td>0.0458</td><td>0.0464</td><td>0.0315</td><td>0.0324</td></tr><tr><td>FID</td><td>-0.1078</td><td>-0.1085</td><td>-0.0768</td><td>-0.0783</td></tr><tr><td>LPIPS</td><td>-0.1352</td><td>-0.1376</td><td>-0.1002</td><td>-0.1029</td></tr><tr><td>CULTDIFF-S</td><td>0.1848</td><td>0.1559</td><td>0.1278</td><td>0.1312</td></tr></table>
+
+Table 2: Correlation between human judgment scores and similarity values produced using different metrics.
+
+# 5 Discussion
+
+# 5.1 Human-Aligned Image-Image Similarity Evaluation Metric
+
+Designing an image-image similarity metric that closely correlates with human rankings is inherently difficult due to the complexity of cultural attributes and subjective interpretations. Culture itself is a broad and multifaceted concept, making it challenging to define in a universally accepted manner (Liu et al., 2024b). In fact, many previous studies in related domains that incorporate cultural aspects either provide only a high-level definition or avoid in-depth discussions on its nuances (Adilazuarda et al., 2024; Huang and Yang, 2023; Zhou et al., 2023; Cao et al., 2024a; Wan et al., 2023). In our study, we treated countries as representatives of distinct cultures. However, individuals, even within the same country, may have diverse interpretations of cultural artifacts, reflecting regional, ethnic, and personal differences in cultural perception, which can lead to subjective similarity scores given by human annotators. Additionally, cultural artifacts often evolve over time, influenced by various internal or external factors, further complicating the task of cultural representations. As a result, an effective similarity metric that correlates more closely with human perception must account for both objective attributes, such as structural and visual features, and subjective factors, including historical context, regional variations, and personal
+
+interpretations. We believe that developing such a metric necessitates integrating human feedback into the process, combining both data-driven approaches and human-centered evaluations to bridge the gap, as has been demonstrated in other contexts and domains in prior works (Xu et al., 2023; Stiennon et al., 2020; Ouyang et al., 2022), as well as in our research.
+
+# 5.2 Culturally Aware Diffusion Models
+
+Recent advancements in fine-tuning diffusion models have demonstrated significant improvement (Ruiz et al., 2023). Following similar approaches in cultural contexts, fine-tuning diffusion models on smaller, culturally-curated datasets has been shown to lead to more culturally relevant image generation (Liu et al., 2023). While approaches relying on large culture-specific datasets, such as Japanese Stable Diffusion (Shing and Sawada, 2022), have been generally successful in enhancing cultural awareness for specific cultures, the limited resource presence of underrepresented cultures creates a challenge for applying similar methods. In this context, our dataset further provides a foundation for incorporating data from underrepresented cultures/countries, broadening the reach of culturally inclusive image generation. We hope that our work will inspire further research into improving the fairness of T2I generation models, particularly by expanding representation for low-resource cultures in both model training and evaluation.
+
+# 6 Conclusion
+
+In this paper, we assessed the cultural awareness diffusion models spanning 10 countries and 3 categories (i.e., 2015) for AI-generated images to real counterparts, description fidelity, and realism—revealed that the diffusion models exhibit better cultural awareness for high-resource countries than low-resource countries, such as Ethiopia, Azerbaijan, and Indonesia. Furthermore, we proposed an automatic image-image similarity metric that shows improvement in correlating with human judgments to evaluate cultural aspects.
+
+# Limitations
+
+The main limitation of our work lies in recruiting only three annotators per country due to budget constraints, similar to most human experiments
+
+in other studies. While we could derive valuable insights from the human evaluation results, increasing the number of survey participants per country would enhance the robustness and generalizability of our findings.
+
+# Ethical Statement
+
+This study was conducted with prior approval from the Institutional Review Board (IRB), ensuring adherence to ethical research standards. All participants recruited through Prolific were compensated at a competitive rate (18 pounds  $\sim$  22.38 USD in January 2025), meeting or exceeding ethical guidelines. Similarly, participants recruited directly, primarily from low-resource countries, were fairly compensated (30K KRW  $\sim$  20.89 USD in January 2025).
+
+To ensure the appropriateness of the materials, all generated images were reviewed by the authors before being provided to the annotators. Additionally, we included a question at the end of the survey asking whether each image was inappropriate or disturbing. All annotators responded "No" to this question, confirming the suitability of the content used in the study.
+
+# Acknowledgments
+
+This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the National Program for Excellence in SW), supervised by the IITP (Institute of Information & communications Technology Planing & Evaluation) in 2024 (2022-0-01092) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.RS-2024-00406715), and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2024-00509258 and No. RS-2024-00469482, Global AI Frontier Lab).
+
+This research project has benefited from the Microsoft Accelerate Foundation Models Research (AFMR) grant program through which leading foundation models hosted by Microsoft Azure along with access to Azure credits were provided to conduct the research.
+
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+
+# A Appendix
+
+# A.1 Prompt Details
+
+For each category, we used prompts in the following format:
+
+1. "A panoramic view of {landmark} in {country}, realistic"  
+2. "An image of {clothes} from {country} clothing, realistic"  
+3. "An image of {food} from {country} cuisine, realistic"
+
+# For example:
+
+1. "A panoramic view of the Empire State Building in the United States, realistic"  
+2. "An image of Hanfu from Chinese clothing, realistic"  
+3. "An image of plov from Azerbaijani cuisine, realistic"
+
+Additionally, we experimented with various prompting techniques, including GPT-4-generated detailed prompts. However, we observed that these prompts occasionally introduced hallucinations leading to inaccurate image generation. Through empirical analysis, we found that simpler prompts tended to provide more accurate and culturally relevant generations.
+
+# A.2 Model Parameters
+
+# A.2.1 Image Generation
+
+The models used for image generation have the following parameter counts: Stable Diffusion XL (Podell et al., 2023) has 2.6 billion U-Net parameters, Stable Diffusion 3 Medium (Esser et al., 2024) has 2 billion parameters, and FLUX.1-dev (Labs, 2023) is a 12-billion-parameter rectified flow transformer.
+
+# A.2.2 Model Training
+
+For model training, we used ViT-Base (Alexey, 2020), which has 86 million parameters, 12 layers, a hidden size of 768, an MLP size of 3072, and 12 attention heads. We trained our contrastive learning model using ViT-Base for 10 epochs with a learning rate of  $1 \times 10^{-4}$ , a batch size of 32, and an image resolution of  $224 \times 224$ .
+
+# A.3 Survey Question Details
+
+This section provides additional details regarding the survey questions and instructions used during the study. Annotators received clear guidance to ensure consistent evaluations of both real and AI-generated images (Figure 6 left). Examples of spe
+
+cific survey questions for cultural contexts, such as those related to Azerbaijan, are shown in Figure 6 right top and right bottom. The estimated time to complete the survey was approximately 1.5 to 2 hours. Table 3 presents the Fleiss' Kappa scores, reflecting annotator agreement across different diffusion models (Stable Diffusion XL, Stable Diffusion 3 Medium Diffusers, FLUX). The relatively low agreement scores may stem from the fact that annotators from the same country evaluated images generated by different models rather than assessing a shared set of images. Note that we showed a comparative analysis across three models in terms of image-description match (Section 4.1.2) and realism (Section 4.1.3).
+
+<table><tr><td>Country</td><td>Fleiss&#x27; Kappa</td></tr><tr><td>Azerbaijan</td><td>0.14</td></tr><tr><td>China</td><td>0.13</td></tr><tr><td>Ethiopia</td><td>0.13</td></tr><tr><td>Indonesia</td><td>0.10</td></tr><tr><td>Mexico</td><td>0.10</td></tr><tr><td>Pakistan</td><td>0.11</td></tr><tr><td>South Korea</td><td>0.17</td></tr><tr><td>Spain</td><td>0.11</td></tr><tr><td>United Kingdom</td><td>0.12</td></tr><tr><td>United States</td><td>0.07</td></tr></table>
+
+Table 3: Inter-annotator agreement across diffusion models, measured by Fleiss' Kappa, for each country.
+
+# A.4 Survey Responses
+
+We analyzed the scores for each image quadruple by averaging the scores from six questions, assigning the resulting value as the final score for each image. We then calculated the average of these final scores for each category and country, as shown in Figure 7.
+
+The description match scores for Stable Diffusion XL and Stable-Diffusion 3 Medium Diffusers are presented in Figures 8 and 9 respectively.
+
+# A.5 Survey Response Correlations
+
+We calculated the correlations between the scores for Q1.1 (overall similarity) and Q1.2 for three distinct image categories: Architecture, Clothing, and Food. For each image, we first averaged the responses across the models to compute a unified score. These scatter plots (Figure 10, 11, 12) depict the relationship between the two survey questions. Similarly, the correlation results for the Architecture and Clothing categories for Q1.1 (over
+
+# Azerbaijan 1
+
+# Survey Instructions and Rating Scale
+
+In this survey, you will assess the rightmost image compared to the first three images based on three categories: Image Similarity, Description Match, and Realism. Each category has specific definitions for the rating scale:
+
+# 1. Image Similarity (in terms of the given aspects)
+
+- Not at all (1): The images look entirely different, with almost no overlap.  
+- Slightly (2): The images share some elements, but differences are clear.  
+- Somewhat (3): The images have a fair amount of overlap, though differences are noticeable.  
+- Mostly (4): The images appear largely similar, with only minor differences.  
+- Completely (5): The images are nearly the same, with very few, if any, differences.
+
+# 2.Description Match
+
+- Not at all (1): The image does not match the description at all, with no recognizable overlap.  
+- Slightly (2): Only a few aspects of the description are present in the image, and it mostly doesn't align.  
+- Somewhat (3): There is some alignment between the description and the image, but several key elements are missing or differ.  
+- Mostly (4): The description mostly fits the image, with only minor details missing or slightly different.
+
+- Completely (5): The description perfectly aligns with the image, capturing every detail accurately.
+
+# 3. Realism
+
+- Not at all (1): The image appears highly artificial or cartoonish, lacking any elements of realism.  
+- Slightly (2): The image has some realistic elements, but overall it seems unrealistic or exaggerated.  
+- Somewhat (3): The image is moderately realistic, though it contains noticeable discrepancies that detract from its overall believability.  
+- Mostly (4): The image is mostly realistic, with only minor details that may seem off or less believable.  
+- Completely (5): The image appears very realistic, closely resembling actual photographs or real-life scenes.
+
+Note: Use these definitions consistently as you rate each question to ensure accuracy and reliability in your responses.
+
+Next
+
+Page 1 of 17
+
+Clear form
+
+![](images/77dbeb4802a5e6da3298547c7b37f47cbe1c724b124e32dfc336d325926958d7.jpg)  
+Image 2
+
+How similar do you think the rightmost image is to the first three images in terms of the following aspects?
+
+<table><tr><td></td><td>Not at all</td><td>Slightly</td><td>Somewhat</td><td>Mostly</td><td>Completely</td></tr><tr><td>Overall similarity: The overall resemblance between the two images.</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Shapes: The shapes of the structures in the images.</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Materials/Textures: The materials or textures used in the architecture (e.g., glass, concrete).</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr></table>
+
+Background/Surroundings:
+
+The surroundings,
+
+including elements like
+
+natural features, in context, or other.
+
+context, or other background details.
+
+How well does the description match the content of the rightmost image? *
+
+"A panoramic view of the Palace of Shaki Khans in Azerbaijan, realistic"
+
+![](images/10f173f99e10ce58684ec66b06fadb007068be4f1f1afb7371da908f8daacf2f.jpg)
+
+![](images/2880ad382ed8e24b5744a0b3320e4cdbda13bd29e8103fe241474c3b446106ac.jpg)
+
+![](images/3a624d0b6f4d3a95cc75ac9ade72c2f9618f42018d6f88d75e09e87083438273.jpg)
+
+![](images/4dc5aae57965aa12306d89393ba26f231d244e58f9b8503a2df02d720be819ee.jpg)
+
+![](images/0d26e0fb304ca68dfec94f9a0b992bc7021cc6d5a14db9830c46f0c3599e3be2.jpg)
+
+![](images/c77f1b80899646054c2110cc1d9434c0d57678ab333ab7b851798878303e40cc.jpg)
+
+![](images/36c26169d962c25e9c87f9177345167b20fe5cb63d7c0e56e169ea8d8974c736.jpg)
+
+![](images/2dbd48afb0148a79a4a9e83b202b7a3ec9dc8bace6e40490b0ee43f0ff80d0f0.jpg)
+
+![](images/f6edb3c54c02536603eade64ed3bf5c53217fdd7670f0ca3cd915b88f334fad5.jpg)
+
+![](images/19518e5d0cb6882ecfded0216dc2e348b141b1ad6166093f58719035d68f7c5d.jpg)
+
+How realistic does the rightmost image appear? *
+
+![](images/c89f7163d85e9917324b859e38e6cccfe00e8533ad75a439d8aa00f3142e8924.jpg)
+
+![](images/ff7743767ea6a97b1e5555932a30188457f3e0fac2fa0506a2a328f8c5b021d1.jpg)
+
+![](images/cddf101a5b66b6640f070306dd1aa0ee32f41197e9bb65cca3a54c7a5ddcd177.jpg)
+
+![](images/0558891612c402dc61da457f09d8c982e76aa4a0677cd9e0f414611f673dfc7e.jpg)
+
+![](images/25a9b99cad9b70d8ec6a5c075efb079c04db5ef7107269ff69dea6b939e6a827.jpg)
+
+![](images/daf336283d8a3fda7ade55ea7f1d9731295d804da37e00cb983c171f849e2372.jpg)  
+Figure 6: Survey setup illustrating instructions, an image comparison task, and rating questions. The left image shows the survey instructions outlining the tasks and evaluation criteria, while the right side presents examples of the survey questions.
+
+![](images/3f23b9f6efeb727958b70f5673fd55fecaa570402426278ca9cd8f7bb4102289.jpg)
+
+![](images/20129cb4350a3fdcdc32e9f69ea949f2485ab20d56b0b69d1c1c34506486fe01.jpg)
+
+![](images/941cbc00c067b4c3bfb663978d90aaa0b4d12016d3b7b9ebaf20f986be5fed8c.jpg)
+
+![](images/c7b61580addb99e3a4f01f04846662fa9b5d32b81ea433e86056b8b197c06017.jpg)
+
+all similarity) and Q2 (description match) are presented in Figures 13 and 14, respectively. Moreover, we present the correlation scores for all three categories for Q2 (description match) and Q3 (realism) in Figures 15, 16, and 17.
+
+# A.6 Common Failure Patterns
+
+We provide a brief analysis of common failure patterns observed in the generated images. While our primary focus is on evaluating cultural accuracy, understanding these recurring errors offers insight into model limitations:
+
+- Azerbaijani Clothing: Models often produced garments resembling Central Asian (Uzbek, Kyrgyz) attire rather than accurately reflecting Azerbaijani traditional clothing.  
+- Ethiopian Architecture: Generated outputs frequently featured elements drawn from other African regions instead of distinctively
+
+Ethiopian structures, such as the rock-hewn churches of Lalibela.
+
+- Korean Clothing: Models sometimes confused Korean traditional clothing with Chinese hanfu or Japanese kimono, reflecting difficulty in distinguishing nuanced East Asian cultural features.  
+- Pakistani Food: The models frequently generated Indian dishes such as dosa or idli when prompted for Pakistani cuisine.
+
+![](images/589ad506ddf966d6309c409980a8d7aac87799dd3da7c9fea037ff12fff58add.jpg)  
+Figure 7: Average mean scores across categories for the outputs of three models: Stable-Diffusion XL (left), Stable-Diffusion 3 Medium Diffusers (center), and FLUX (right). The bar plots represent the average scores for Architecture, Clothing, and Food categories.
+
+![](images/516527005c8fc2348411de9958c3107f70e481a9b0b97bbb28fa2b8df4b2c715.jpg)
+
+![](images/b9f5cdee7e412612f73150b4832a38e39fbf68351635f967cabce1c02c1ae9e7.jpg)
+
+![](images/72b7f4aa9993feee5efb8fa8bc3825b6d71e643571e670ca4db48ab3df14ec3a.jpg)  
+Figure 8: Average description match scores for the Stable-Diffusion XL model's outputs across three categories: Architecture (left), Clothing (center), and Food (right). Each bar plot represents the average match scores for countries within the respective category, ranked from highest to lowest. Error bars indicate the standard error of the mean.
+
+![](images/6806e2a2de35ae63f5ed8567c63da1a110cc9a752a95413f79508e042e9d5575.jpg)
+
+![](images/2b7eeaa1e81a63df0fec974663a17af8d3061448305999a021e922147f9da827.jpg)
+
+![](images/b6243a6175e4a267a1f2acafacb9386a544ed227872fd30c84aec11b7984eee8.jpg)  
+Figure 9: Average description match scores for the Stable-Diffusion 3 Medium Diffusers model's outputs across three categories: Architecture (left), Clothing (center), and Food (right). Each bar plot represents the average match scores for countries within the respective category, ranked from highest to lowest. Error bars indicate the standard error of the mean.
+
+![](images/8b0fe261600b333e9d5651501c6ea7f1ba07006a2ea8d48f1995611c28ae46de.jpg)
+
+![](images/774695a316d0f3f720e5c9bd55272c79b0267ab80ce04156832fec9d5a515592.jpg)
+
+![](images/333ae224f322ddd71d7f1cd54acc6101e892ba5d45743a6c954222c4a1b68b1f.jpg)  
+Figure 10: Scatter plots of Q1.1 (horizontal axis) vs. Q1.2 (vertical axis) scores for Architecture images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ . Dots represent individual image averages.
+
+![](images/df08ca262289d0f3f56a1b18a4d42757341ea477a8b9b612f5da5fe45e431087.jpg)  
+Figure 11: Scatter plots of Q1.1 (horizontal axis) vs. Q1.2 (vertical axis) scores for Clothing images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/ed805872cddf476f740544ad97193115a4434442155d303ecaf41ec2483fdb0b.jpg)  
+Figure 12: Scatter plots of Q1.1 (horizontal axis) vs. Q1.2 (vertical axis) scores for Food images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/1fa131d55348d86e8f638be41ddb08f71b9d6e1ea93cfe6988062b666c2bb8fb.jpg)  
+Figure 13: Scatter plots of Q1.1 (horizontal axis) vs. Q2 (vertical axis) scores for Architecture images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/152cd887a7ef3e16218098cb3a7bd80f3137ee5071ad14228710c483a203a8a0.jpg)  
+Figure 14: Scatter plots of Q1.1 (horizontal axis) vs. Q2 (vertical axis) scores for Clothing images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/16023f48cb638f156e7a51f1087338dbb22dbe13f1b5088851f6c2b14ce70db8.jpg)  
+Figure 15: Scatter plots of Q2 (horizontal axis) vs. Q3 (vertical axis) scores for Architecture images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/543e1cb54235677394f227b5bb9fff5c1ec4b801c2d813798ceb5e3a838b2780.jpg)  
+Figure 16: Scatter plots of Q2 (horizontal axis) vs. Q3 (vertical axis) scores for Clothing images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .
+
+![](images/15f13e79fb32038e1532ef189e4ee8cb3cabf489b7afd0b066231f9925c38d18.jpg)  
+Figure 17: Scatter plots of Q2 (horizontal axis) vs. Q3 (vertical axis) scores for Food images, categorized by country. Each subplot displays the country name and corresponding Pearson correlation coefficient  $r$ .

2025/Diffusion Models Through a Global Lens_ Are They Culturally Inclusive_/images.zip

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