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notallexpertsareequalefficientexpertpruningandskippingformixtureofexpertslargelanguagemodels/full.md

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+# Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
+
+Xudong Lu $^{*,1}$ , Qi Liu $^{*,2,4}$ , Yuhui Xu $^{3}$ , Aojun Zhou $^{\ddagger 1}$ , Siyuan Huang $^{2,4}$ , Bo Zhang $^{4}$ , Junchi Yan $^{2,4}$ , Hongsheng Li $^{\dagger 1,5}$
+
+$^{1}$ CUHK MMLab  $^{2}$ Shanghai Jiao Tong University  $^{3}$ Salesforce AI Research  $^{4}$ Shanghai Artificial Intelligence Laboratory  $^{5}$ CPII under InnoHK {luxudong@link,hsli@ee}.cuhk.edu.hk purewhite@sjtu.edu.cn {xyh6666,aojunzhou}@gmail.com
+
+# Abstract
+
+A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer active parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specifically designed hardware, this paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques. Specifically, we propose, for the first time to our best knowledge, posttraining approaches for task-agnostic and task-specific expert pruning and skipping of MoE LLMs, tailored to improve deployment efficiency while maintaining model performance across a wide range of tasks. Extensive experiments show that our proposed methods can simultaneously reduce model sizes and increase the inference speed, while maintaining satisfactory performance. Code will be made available at https://github.com/Lucky-Lance/Expert_Sparsity.
+
+# 1 Introduction
+
+Large language models (LLMs) have shown remarkable abilities across various domains (OpenAI, 2023; Zhou et al., 2024), as evidenced by the widespread use of ChatGPT and Gemini (Team et al., 2023). Recent notable advancement in this area is the introduction of the open-sourced Mixture-of-Experts (MoE) LLM, Mixtral 8x7B (Jiang et al., 2024), which sparsely activates only a portion of its parameters during the training and inference process. This model surpasses the performance of dense Transformer-based LLMs, such as LLaMA-2 70B (Touvron et al., 2023a,b), with fewer active parameters (13B) during inference.
+
+MoE LLMs achieve a reduction in on-the-fly (active) parameters by choosing only top- $k$  experts for the inference of each token, thereby enhancing inference speed (Sanseviero et al., 2023). However, the static parameters, particularly those required for constructing the MoE architecture, still demand considerable memory and storage for deployment. For example, loading the Mixtral 8x7B model in bf16 format requires at least two A100-80G GPUs. Notably, in this MoE model, the eight experts constitute around  $96\%$  (45B out of 47B) of the total number of parameters.
+
+On the other hand, not all experts are equal in the MoE model. Recent studies, such as (Chi et al., 2022), have demonstrated this discrepancy in expert training outcomes. The differing levels of training among each expert highlight the importance and practicality of identifying and pruning less significant experts, thereby improving the deployment efficiency of MoE models.
+
+Unlike existing post-training weight pruning schemes for LLMs, which primarily target unstructured sparsity and N:M semi-structured sparsity (Sun et al., 2023; Frantar and Alistarh, 2023), our approach focuses on expert-level sparsity for model sparsification. The aforementioned fine-grained weight pruning techniques are effective in reducing the total number of parameters. However, they face challenges in plug-and-play deployment due to the necessity for specific hardware designs (e.g., FPGA) (Zhou et al., 2021), which demands an extensive co-design of hardware and systems.
+
+In this paper, we systematically explore expert-level sparsity in MoE LLMs and, for the first time to our best knowledge, introduce hardware-friendly post-training methods for either permanently removing unimportant experts (expert pruning) or dynamically skipping experts during inference (dynamic expert skipping). Our proposed method significantly reduces memory usage for deploying MoE LLMs and enhances their inference speed.
+
+![](images/bcf680f081e225a8510f8e4c34de3c8260a3a57bd1c0f5c0246507a543ccfb6b.jpg)
+
+![](images/dd722131e732c260da8c36168e2a4aef214a043d3f976a9de52c1c0c421e8c4a.jpg)  
+Number of Preserved Experts Each Layer (r)  
+Figure 1: Memory usage reduction (bf16) and inference speedup illustration of our proposed post-training expert pruning and dynamic (expert) skipping methods on the Mixtral 8x7B (Jiang et al., 2024) model. Our method greatly reduces memory consumption and enhances inference speed.
+
+![](images/cf65b1c42f95f0f2ce27264fb650222a23dd871f60fc4a3363e590c7d7e18473.jpg)  
+Number of Preserved Experts Each Layer (r)
+
+![](images/532f304f65253186afaf126e106879c335a651a6f5ba4918323411dc38c763fc.jpg)
+
+Initially, we investigate how to prune less important experts while maintaining satisfactory performance, utilizing an efficient post-training approach. We aim to minimize the token reconstruction loss in a layer-by-layer manner. Given the limited number of experts in a single MoE layer of the LLM, we meticulously enumerate and choose combinations of experts that yield the lowest token reconstruction loss, subsequently, concatenating them to obtain the final pruned MoE model. This strategy significantly lowers the memory demands for deploying MoE LLMs. We examine expert-level pruning for both task-agnostic and task-specific (first in literature) models, tailoring our strategies to optimize performance across a wide range of applications.
+
+Building on this foundation, we further dive into strategies for accelerating the inference speed of MoE LLMs without compromising their robustness. Specifically, based on the model's fixed expert count, we introduce an online method for dynamically skipping certain experts. This approach, which is complementary to our expert pruning strategy, allows for on-the-fly adjustment of the number of active experts during inference, thus enhancing the inference speed. By integrating the dynamic (expert) skipping approach with expert pruning, we achieve a more streamlined and efficient deployment for MoE LLMs.
+
+Experiments on Mixtral 8x7B (Instruct) models (Jiang et al., 2024) demonstrate that our methods significantly reduce memory usage and increase inference speed. Take the example of posttraining pruning two experts. As shown in Fig. 1, we halve the number of GPUs needed, allowing deployment on a single 80G GPU and achieving a  $1.2 \times$  inference speedup. The pruning also results in mild performance loss, specifically, around 2.9 points for task-agnostic and 6.2 points (reducible to
+
+1.6 with task-specific fine-tuning) for task-specific models. Further combination of dynamic skipping with expert pruning can lead to the same inference speedup with dropping 4 experts while achieving much higher model performances. To the best of our knowledge, this study is the first to discuss expert-level sparsity and propose efficient schemes for expert pruning and skipping for MoE LLMs.
+
+# 2 Related Works
+
+# 2.1 Mixture-of-Experts Models
+
+First introduced in (Jacobs et al., 1991), a Mixture-of-Experts (MoE) model contains multiple separate networks, and each network processes a subset of the entire dataset. This separation can be viewed as a modular transformation of a multi-layer network. MoE structure is used for designing Recurrent Neural Networks (RNNs) in (Shazeer et al., 2017) and further extended to encoder-decoder Transformer-based models (Lepikhin et al., 2020). With the recent development of decoder-only GPT family of models (Brown et al., 2020; Touvron et al., 2023a,b), MoE models based on this structure gain popularity (Jiang et al., 2024). In this paper, we focus on post-training expert pruning/Skipping methodologies for MoE LLMs.
+
+# 2.2 Expert Pruning for MoE Models
+
+Expert pruning within MoE models has garnered attention in the realm of Natural Language Processing, particularly in machine translation tasks. In these contexts, the translation of specific languages often renders the expertise of other language specialists superfluous. The most activated experts are reserved in (Kim et al., 2021) to prune a machine translation MoE model, and (Koishekenov et al., 2022) proposes expert pruning metrics based on gate statistics collected during decoding. Al
+
+though these methods actively deal with expert pruning for MoE models, they are still limited to the machine translation domain with linguistic models. Researchers in (Chen et al., 2022) provide a dropping-while-training method that progressively drops the non-professional experts for target downstream tasks, and experiments are carried out on Switch Transformers models (Fedus et al., 2022). However, in the LLM era, it is usually difficult to afford such a training paradigm.
+
+# 2.3 Post-training Pruning for LLMs
+
+Post-training pruning (Kwon et al., 2022; Hubara et al., 2021) has become a popular topic for neural network sparsification in recent years. Given a trained model, post-training pruning aims at achieving the optimal model sparsification outcome by utilizing model parameters together with some calibration data. Recent works extend pruning methods to LLMs (Sun et al., 2023; Frantar and Alistarh, 2023). However, these pruning methods primarily focus on sparsifying the weight matrices of linear layers in the LLMs and require dedicated hardware. To the best of our knowledge, efficient post-training expert pruning methods have not been discussed for decoder-only LLMs with MoE structures.
+
+# 3 Method
+
+To enhance the deployment efficiency of MoE LLMs, we concentrate on expert-level model sparsity and innovatively propose post-training techniques designed to reduce memory usage and increase inference speed. In this section, we offer a comprehensive explanation of our proposed methods for expert pruning (Sec. 3.2) and dynamic (expert) skipping (Sec. 3.3), considering both memory consumption and inference speed.
+
+# 3.1 Preliminary
+
+In the decoder-only sparse MoE Transformer models, as discussed in (Gale et al., 2023), the Feed-Forward Network (FFN) sub-layers of the traditional dense model are replaced with MoE layers, each containing  $n$  experts. Specifically, within the MoE layer of the Mixtral 8x7B model, featuring 8 experts as detailed in (Jiang et al., 2024), each token  $x$  in the input sequence is routed to the top-2 experts based on the routing weights  $w$ .
+
+The inference process for each input token  $\pmb{x}$  within the MoE layer of the Mixtral 8x7B decoder layer is depicted in Fig. 2. Initially, the router computes routing logits  $l = \{l_0, \dots, l_{n-1}\}$  and routing
+
+![](images/3f61b147b0f5d8cd717581be507af1e90945fa71194a8f7331b7742286309fee.jpg)  
+Figure 2: Illustration of the MoE layer in the Mixtral 8x7B model for per-token inference. The output of the layer is the weighted sum of the outputs from selected experts over input token  $\boldsymbol{x}$ .  $\widetilde{w}_i$  denotes the normalized routing weight of each selected expert.
+
+weights  $\boldsymbol{w} = \operatorname{Softmax}(l)$  for the experts. Then the top- $k$  experts, where  $k = 2$  for the Mixtral 8x7B model, are selected based on their routing weights to process the token. Each of these  $k$  selected experts, applying a SwiGLU transformation  $\mathcal{E}_i(\cdot)$  ( $i \in \{0,1,\dots,n-1\}$ ), contributes to the final output. This output is a weighted sum of the individual expert outputs, with weights  $\widetilde{w}_i$  being the normalized values of the corresponding routing weights for the selected experts. The normalized weight for expert  $e_j$  ( $j \in \{0,1,\dots,k-1\}$ ) is calculated as follows:
+
+$$
+\widetilde {w} _ {e _ {j}} = \frac {w _ {e _ {j}}}{\sum_ {m = 0} ^ {k - 1} w _ {e _ {m}}}, \tag {1}
+$$
+
+yielding the MoE layer's output for the token  $x$  as:
+
+$$
+\boldsymbol {z} = \sum_ {j = 0} ^ {k - 1} \widetilde {\boldsymbol {w}} _ {e _ {j}} \cdot \mathcal {E} _ {e _ {j}} (\boldsymbol {x}). \tag {2}
+$$
+
+Aside from these specified mechanisms, the remaining aspects of the MoE network mirror those of a standard decoder-only Transformer model.
+
+# 3.2 Post-training Expert Pruning
+
+As demonstrated in the preliminary subsection, the parameters of experts occupy the major proportion of the whole MoE LLM model. However, for a single token, only a small subset of these experts are activated, leading to considerable inefficiencies in parameter utilization. Existing posttraining weight pruning methods for LLMs (e.g., Wanda (Sun et al., 2023), SparseGPT (Frantar and Alistarh, 2023)), while effective in reducing model
+
+![](images/8d6b6183c00d861d175c8b7ea2baaf512826128a084319557d72878332d27319.jpg)  
+Figure 3: Framework of our proposed expert pruning and dynamic skipping methods. (a) The expert pruning method evaluates the contributions of experts via a small calibration dataset and then permanently discards those with low contributions (e.g., experts with a slashed background). (b) The dynamic skipping method discard no experts instead dynamically decides whether to skip certain experts (e.g., experts with a yellow background) during inference.
+
+![](images/beab442ce1bca5a11bff7322eccb6ed99882d5e24097526898b39785861526a3.jpg)
+
+![](images/926f2a044cc2ab0915c7cbaef6680e388a0a364fa5d7e1ca8ea50d4a4b930c1c.jpg)  
+Figure 4: Expert selection comparison between C4 and MATH dataset with  $r = 6$  for Mixtral 8x7B model. Significant divergence is observed in the selection of experts across these two datasets, and identical expert combinations are observed in only four specific layers (i.e., layer 2, layer 4, layer16, and layer 31).
+
+parameters, do not support efficient deployment of MoE LLMs without specialized hardware implementations (Mishra et al., 2021). Therefore, we introduce a heuristic search method to prune the number of experts in a post-training manner.
+
+Task-agnostic Expert Pruning for General Tasks. Different from existing pruning schemes leveraging unstructured or semi-structured weight sparsity (Sun et al., 2023; Frantar and Alistarh, 2023) for LLMs, our proposed post-training expert pruning method aims at reducing the parameter numbers of MoE LLMs by permanently discarding less important experts, thereby improve the inference efficiency. It is a post-training pruning method and does not require any parameter update. Fig. 3 (a) illustrates our proposed post-training expert pruning method. We conduct expert pruning in a layer-wise manner. Specifically, the pruning method contains two steps as follows.
+
+Firstly, we set up a small calibration dataset, then perform inference on the original MoE model with all experts over the dataset, and cache the input-output token pairs of each MoE layer. We use samples from the pre-training dataset C4 (Raffel
+
+et al., 2019) as calibration data, since pre-training datasets are often more comprehensive and not dominated by knowledge specific to any particular domain (Sun et al., 2023).
+
+Secondly, after caching input-output pairs for each MoE layer, we enumerate expert combinations based on the preset parameter  $r$ , denoting the number of preserved experts. Assume the function of the MoE layer at layer  $l$  is  $\mathcal{F}(\cdot)$ , with the cached input represented by  $x$ . During each enumeration, we maintain  $r$  experts and eliminate the remaining experts along with their associated routing weights. Subsequently, we employ the pruned MoE layer, denoted as  $\mathcal{F}'(\cdot, \mathbf{C})$ , to recalculate the corresponding output, where  $\mathbf{C}$  represents a subset containing  $r$  experts selected from the original  $n$  experts. Inspired by channel pruning in CNNs (He et al., 2017), the Frobenius norm of the difference between cached output  $\mathcal{F}(x)$  and the output of pruned layer  $\mathcal{F}'(x, \mathbf{C})$  is used to quantify the discrepancy between the model before and after expert pruning, and we denote it as reconstruction loss. The expert subset corresponding to the minimum reconstruction loss is retained. The  $n - r$  experts
+
+left are considered to contribute least to the original MoE model and are thus discarded. The expert pruning process in layer  $l$  can be formulated as:
+
+$$
+\min  _ {\mathbf {C}} \| \mathcal {F} ^ {\prime} (\boldsymbol {x}, \mathbf {C}) - \mathcal {F} (\boldsymbol {x}) \| _ {F} \tag {3}
+$$
+
+$$
+\begin{array}{l} \text {s . t .} \mathbf {C} \subseteq \left\{\operatorname {e x p e r t} _ {0}, \dots , \operatorname {e x p e r t} _ {n - 1} \right\}, | \mathbf {C} | = r. \end{array}
+$$
+
+We heuristically search for expert subset  $\mathbf{C}$  with the lowest reconstruction loss in each layer separately and obtain an MoE model with  $r$  experts by the concatenation of each pruned layer. After removing the insignificant experts, the pruned model can be easily loaded using existing packages (e.g., Huggingface Transformers (Wolf et al., 2020)) with just a change of the model configuration. Especially, with 2 experts pruned, the deployment budget is reduced to a single 80G GPU for loading the Mixtral 8x7B (Instruct) model with bf16 data type.
+
+Task-specific Expert Pruning for Domain-specific Tasks. Previous research on post-training pruning for LLMs (Sun et al., 2023; Frantar and Alistarh, 2023) typically considers the performance over general tasks. For the first time, in our work, we investigate the task-specific post-training pruning for MoE LLMs. Our above-proposed expert pruning strategy is adept at conserving the knowledge encapsulated by the experts of an MoE model for general tasks. However, as a pre-training dataset, C4 spans a wide array of domains, posing challenges when pruning experts for domain-specific tasks (e.g., models tailored for mathematics (Yu et al., 2023; Wang et al., 2024)). We evaluate the 5-shot performance of the C4 pruned MoE LLM model with  $r = 6$  on math tasks (GSM8K) (Cobbe et al., 2021), resulting in a performance degradation plummeting from 58.61 to 41.02 for the Mixtral 8x7B model. To address this challenge, we propose to shift the calibration dataset from C4 to the training set of MATH (Hendrycks et al., 2021), to concentrate the pruning process on the mathematics domain.
+
+Remark. For a better comparison between general tasks and domain-specific tasks, we visualize the distribution of pruned experts selected by C4 and MATH, as shown in Fig. 4. For both the Mixtral 8x7B and Mixtral 8x7B Instruct model, identical expert combinations are observed in only four specific layers. This suggests that there are distinct differences in the distributions of pre-training datasets and domain-specific datasets. Further details and discussions are provided in Sec. 4.2.
+
+# 3.3 Dynamic Skipping During Inference
+
+Our expert pruning strategy effectively reduces memory consumption during model deployment. However, each token is still processed by  $k$  selected experts, without a reduction in runtime FLOPs. Intuitively, not all tokens require the selection of all top-  $k$  experts during the token generation process. Consequently, we introduce a scheme that dynamically skips certain experts for individual token inference, to further enhance inference efficiency.
+
+As shown in Fig. 2, during the inference process, top-  $k$  experts are chosen with routing weights  $\boldsymbol{w} = \{w_{e_0}, w_{e_1}, \dots, w_{e_{k-1}}\}$  for each token  $\boldsymbol{x}$  in an MoE layer. For simplicity, we assume  $k = 2$  (as in Mixtral 8x7B) $^2$ . Our proposed dynamic expert skipping method is illustrated in Fig. 3 (b). Without loss of generality, assume experts with indices  $e_0$  and  $e_1$  are chosen, and  $w_{e_1} < w_{e_0}$ . To accelerate inference speed, if  $w_{e_1} < \beta w_{e_0}$ , we do not assign  $\boldsymbol{x}$  to expert  $e_1$ , where  $\beta$  is a hyper-parameter separately set for each MoE layer. In our implementation, we forward the model over the sampled calibration data and set  $\beta$  as the median value of  $\frac{w_{e_1}}{w_{e_0}}$  for each MoE layer. The dynamic skipping scheme can be carried out on the fly to speed up inference, and can be used simultaneously with expert pruning. In our experiments, we observe a  $1.2 \times 1.3 \times 1.3$  inference speedup with  $r = 6$ .
+
+# 4 Experiment
+
+In this section, a series of experiments are carried out to evaluate our proposed methods. We introduce experiments of expert pruning for general tasks in Sec. 4.1, domain-specific tasks in Sec. 4.2, and dynamic expert skipping results in Sec. 4.3.
+
+# 4.1 Expert Pruning for General Tasks
+
+In this subsection, we evaluate the proposed expert pruning method on some general tasks, which can comprehensively reflect the knowledge retention of the model after expert pruning.
+
+Experiment Setup. Similar to Wanda (Sun et al., 2023), we choose calibration data from the C4 (Raffel et al., 2019) dataset and combine them into 128 token sequences, each with a length of  $2048^{3}$ . We perform expert pruning on both Mixtral 8x7B and Mixtral 8x7B Instruct models, resulting in MoE
+
+<table><tr><td>Model</td><td>Method</td><td>Sparsity</td><td>ARC-c</td><td>ARC-e</td><td>BoolQ</td><td>HellaSwag</td><td>MMLU</td><td>OBQA</td><td>RTE</td><td>WinoGrande</td><td>Average</td><td>Mem (MB)</td><td>Speedup</td></tr><tr><td rowspan="2">Mixtral 8x7B</td><td>Wanda</td><td>2:4</td><td>42.06</td><td>74.16</td><td>76.64</td><td>53.16</td><td>52.21</td><td>27.00</td><td>63.90</td><td>70.96</td><td>57.51</td><td>51,214</td><td>0.91×</td></tr><tr><td>Ours</td><td>r = 4</td><td>48.89</td><td>78.16</td><td>81.35</td><td>57.66</td><td>47.30</td><td>29.00</td><td>61.37</td><td>72.85</td><td>59.57</td><td>46,879</td><td>1.27×</td></tr><tr><td>Mixtral 8x7B</td><td>Wanda</td><td>2:4</td><td>48.89</td><td>78.70</td><td>86.27</td><td>56.24</td><td>57.84</td><td>30.40</td><td>72.20</td><td>71.82</td><td>62.80</td><td>51,210</td><td>0.92×</td></tr><tr><td>Instruct</td><td>Ours</td><td>r = 4</td><td>53.92</td><td>79.88</td><td>84.77</td><td>60.05</td><td>52.75</td><td>30.40</td><td>75.45</td><td>73.80</td><td>63.88</td><td>46,879</td><td>1.27×</td></tr></table>
+
+Table 1: Comparison with Wanda (Sun et al., 2023) at the structured 2:4 sparsity pattern. Our proposed expert pruning method  $(r = 4)$  outperforms Wanda in all aspects. Mem stands for memory usage. The original average performance of Mixtral 8x7B and Mixtral 8x7B Instruct model is 67.58 and 69.98, respectively.  
+
+<table><tr><td>Model</td><td>Method</td><td>r</td><td>ARC-c</td><td>ARC-e</td><td>BoolQ</td><td>HellaSwag</td><td>MMLU</td><td>OBQA</td><td>RTE</td><td>WinoGrande</td><td>Average</td></tr><tr><td rowspan="7">Mixtral 8x7B</td><td>None</td><td>8</td><td>57.17</td><td>84.01</td><td>85.35</td><td>64.88</td><td>67.88</td><td>35.00</td><td>70.40</td><td>75.93</td><td>67.58</td></tr><tr><td rowspan="2">Random</td><td>6</td><td>48.04</td><td>78.49</td><td>81.99</td><td>59.02</td><td>60.77</td><td>33.40</td><td>66.79</td><td>75.85</td><td>63.04</td></tr><tr><td>4</td><td>39.85</td><td>68.35</td><td>78.59</td><td>53.32</td><td>49.23</td><td>29.20</td><td>62.82</td><td>69.93</td><td>56.41</td></tr><tr><td rowspan="2">Frequency</td><td>6</td><td>49.06</td><td>78.83</td><td>77.03</td><td>59.38</td><td>55.18</td><td>33.60</td><td>57.40</td><td>75.69</td><td>60.77</td></tr><tr><td>4</td><td>43.86</td><td>73.61</td><td>76.97</td><td>54.01</td><td>41.48</td><td>26.20</td><td>57.04</td><td>73.48</td><td>55.83</td></tr><tr><td rowspan="2">Ours</td><td>6</td><td>51.62</td><td>81.94</td><td>83.64</td><td>61.60</td><td>58.72</td><td>33.00</td><td>67.87</td><td>75.37</td><td>64.22</td></tr><tr><td>4</td><td>48.89</td><td>78.16</td><td>81.35</td><td>57.66</td><td>47.30</td><td>29.00</td><td>61.37</td><td>72.85</td><td>59.57</td></tr><tr><td rowspan="7">Mixtral 8x7B Instruct</td><td>None</td><td>8</td><td>62.20</td><td>87.04</td><td>88.50</td><td>67.59</td><td>68.87</td><td>36.60</td><td>72.20</td><td>76.87</td><td>69.98</td></tr><tr><td rowspan="2">Random</td><td>6</td><td>54.52</td><td>83.04</td><td>87.25</td><td>63.21</td><td>62.70</td><td>35.40</td><td>72.92</td><td>77.19</td><td>67.03</td></tr><tr><td>4</td><td>48.81</td><td>75.46</td><td>78.47</td><td>57.48</td><td>53.68</td><td>31.80</td><td>72.56</td><td>70.96</td><td>61.15</td></tr><tr><td rowspan="2">Frequency</td><td>6</td><td>55.89</td><td>82.83</td><td>86.33</td><td>63.69</td><td>58.89</td><td>37.00</td><td>63.18</td><td>76.01</td><td>65.48</td></tr><tr><td>4</td><td>49.40</td><td>77.27</td><td>82.97</td><td>57.66</td><td>47.03</td><td>32.20</td><td>66.79</td><td>74.03</td><td>60.92</td></tr><tr><td rowspan="2">Ours</td><td>6</td><td>58.19</td><td>84.89</td><td>87.34</td><td>65.24</td><td>62.47</td><td>35.60</td><td>70.04</td><td>75.85</td><td>67.45</td></tr><tr><td>4</td><td>53.92</td><td>79.88</td><td>84.77</td><td>60.05</td><td>52.75</td><td>30.40</td><td>75.45</td><td>73.80</td><td>63.88</td></tr></table>
+
+Table 2: Zero-shot performance evaluation of different expert pruning methods with  $r$  set to 6 and 4. Random stands for randomly choosing experts to discard in each MoE layer. Frequency stands for dropping experts based on their activation frequency during the inference over calibration data. Our proposed expert pruning method leads to the least performance drop, with around 2.9 points for dropping 2 experts and 7.1 points for dropping 4 experts.
+
+models with two experts discarded ( $r = 6$ ) and four experts discarded ( $r = 4$ ) in each layer. Pruning a Mixtral 8x7B model takes about 30 minutes for  $r = 6$  and 90 minutes for  $r = 4$ .
+
+After expert pruning, we evaluate the performance of the pruned MoE models following Wanda (Sun et al., 2023). Specifically, we report zero-shot accuracies of 8 tasks from EleutherAI LM Harness (Gao et al., 2023). We also test the token generation speed $^4$ , together with the peak GPU memory usage during model inference.
+
+Comparison with Weight Pruning Methods. We compare our proposed expert pruning method with the representative weight pruning algorithm Wanda. For a fair comparison, we set  $r = 4$  in our method and test Wanda with the commonly used structured 2:4 sparsity pattern. This will lead to around  $50\%$  parameter reduction for both methods. The results are shown in Tab. 1. The inference speedup of 2:4 structured model relies on specially designed hardware (Mishra et al., 2021) and scripts. In our experiments, we even observe a lower inference speed compared with the dense weight model<sup>5</sup>.
+
+Besides, our expert pruning method excels Wanda with the 2:4 sparsity pattern in both memory usage and benchmark performance.
+
+Comparison with Other Expert Pruning Baselines. We also set up two baseline methods. One baseline is randomly dropping experts in each layer. The other method calculates the activation frequency of each expert during forward passes on the calibration data and discards those with the lowest activation frequencies in each layer. Comparison results are listed in Tab. 2. The method based on activation frequency gets the worst performance. This phenomenon implies that although the MoE model might show a tendency for expert selection during the inference process, simply carrying out expert pruning based on activation frequency might not always lead to desirable results. In contrast, our proposed method achieves better results. Compared to the origin model with 8 experts, our model achieves a 2.9-point performance drop with  $24\%$  parameter reduction and a 7.1-point performance drop with  $48\%$  parameter reduction on average without any extra training.
+
+Memory Usage and Generation Speed. The memory usage statistics are shown in Fig. 1. It takes 2 A100-80G GPUs to load and forward the
+
+<table><tr><td>Model</td><td>Method</td><td>Sparsity</td><td>GSM8K (5-shot)</td></tr><tr><td rowspan="9">Mixtral 8x7B</td><td>None</td><td>None</td><td>58.61</td></tr><tr><td>Wanda (C4)</td><td>2:4</td><td>14.10</td></tr><tr><td>Wanda (MATH)</td><td>2:4</td><td>20.39</td></tr><tr><td>Random</td><td>r = 4</td><td>0.68</td></tr><tr><td>Ours (C4)</td><td>r = 4</td><td>24.87</td></tr><tr><td>Ours (MATH)</td><td>r = 4</td><td>37.07</td></tr><tr><td>Random</td><td>r = 6</td><td>36.39</td></tr><tr><td>Ours (C4)</td><td>r = 6</td><td>41.02</td></tr><tr><td>Ours (MATH)</td><td>r = 6</td><td>51.25</td></tr><tr><td rowspan="9">Mixtral 8x7BInstruct</td><td>None</td><td>None</td><td>63.46</td></tr><tr><td>Wanda (C4)</td><td>2:4</td><td>26.69</td></tr><tr><td>Wanda (MATH)</td><td>2:4</td><td>31.31</td></tr><tr><td>Random</td><td>r = 4</td><td>0.76</td></tr><tr><td>Ours (C4)</td><td>r = 4</td><td>30.40</td></tr><tr><td>Ours (MATH)</td><td>r = 4</td><td>47.01</td></tr><tr><td>Random</td><td>r = 6</td><td>39.80</td></tr><tr><td>Ours (C4)</td><td>r = 6</td><td>48.52</td></tr><tr><td>Ours (MATH)</td><td>r = 6</td><td>58.38</td></tr></table>
+
+Table 3: 5-shot GSM8K (Cobbe et al., 2021) accuracy comparison for sampling calibration data from different datasets. Random pruning will lead to bad performance in this case. Compared with pre-training datasets, sampling from domain-specific datasets will significantly improve the performance on corresponding tasks after expert pruning. Our expert pruning strategy also outperforms Wanda with 2:4 structured sparsity.
+
+original 8-expert model with bf16 data type. After pruning 2 and 4 experts, only one 80G GPU is needed for the inference process. For token generation speed analysis, during model inference, we still need to route each token to two experts. However, reducing the number of GPUs required to load the model can decrease the time consumed by GPU intercommunication, resulting in a much higher token generation speed. We observe a  $1.20 \times$  token generation speedup for the model with 2 experts pruned and a  $1.27 \times$  speedup with 4 experts pruned.
+
+# 4.2 Expert Pruning for Domain-Specific Tasks
+
+Experiment Setup. We investigate on mathematical reasoning tasks. We randomly sample sentences from the train set of MATH (Hendrycks et al., 2021) and combine them into 128 token sequences, each with a length of 2048. We carry out expert pruning with  $r = 6$  and  $r = 4$ , then test 5-shot GSM8K (Cobbe et al., 2021) results.
+
+Baselines to Compare. We compare the 5-shot GSM8K result with randomly pruned models, models pruned using samples from C4 as calibration data, the 2:4 structured model obtained by Wanda, as well as the original MoE model with 8 experts. Vanilla Wanda uses C4 for data calibration. To test the influence of the calibration dataset on model
+
+<table><tr><td>Model</td><td>Method</td><td>r</td><td>GSM8K</td><td>MATH</td></tr><tr><td>MetaMath 70B</td><td>None</td><td>N/A</td><td>82.30</td><td>26.60</td></tr><tr><td rowspan="4">Mixtral 8x7B</td><td>None</td><td>8</td><td>81.35</td><td>34.86</td></tr><tr><td>Ours (C4)</td><td>6</td><td>79.53</td><td>32.48</td></tr><tr><td>Ours (MATH)</td><td>6</td><td>79.53</td><td>33.58</td></tr><tr><td>Ours (MATH)</td><td>7</td><td>81.20</td><td>34.40</td></tr><tr><td rowspan="4">Mixtral 8x7B Instruct</td><td>None</td><td>8</td><td>81.43</td><td>35.46</td></tr><tr><td>Ours (C4)</td><td>6</td><td>79.83</td><td>32.70</td></tr><tr><td>Ours (MATH)</td><td>6</td><td>80.06</td><td>34.10</td></tr><tr><td>Ours (MATH)</td><td>7</td><td>81.50</td><td>34.86</td></tr></table>
+
+Table 4: Zero-shot evaluation results of GSM8K (Cobbe et al., 2021) and MATH (Hendrycks et al., 2021) after training the MoE models on MetaMathQA (Yu et al., 2023) with different expert numbers and different calibration datasets for expert pruning. Using domain-specific data can result in better performance on corresponding downstream tasks. Model fine-tuning can greatly reduce the performance gaps between the pruned models and the original model.
+
+performance, we also leverage the MATH dataset for Wanda pruning.
+
+Evaluation Results. Tab. 3 illustrates the 5-shot evaluation results on the GSM8K dataset. The performance witnesses a significant drop after random pruning, or pruning with calibration data obtained from C4. However, this degradation dramatically reduces after leveraging the MATH dataset for calibration data construction. A similar phenomenon is also observed with Wanda. This indicates that when facing domain-specific tasks, using datasets corresponding to these specific tasks can yield better expert pruning results than using pre-training datasets. It also implies that our proposed method for changing the calibration dataset for domain-specific tasks can also be applied to other pruning algorithms. Besides, our expert pruning strategy  $(r = 4)$  significantly outperforms Wanda with 2:4 structured sparsity.
+
+More Discussion. Using samples from the MATH dataset can greatly improve the domain-specific performance on mathematics tasks. However, as our method follows a post-training manner without any training, the expert pruning scheme still leads to a significant performance drop. To reduce the performance gap between pruned models and original models, we fully fine-tune the MoE models with different expert numbers on the MetaMathQA (Yu et al., 2023) dataset and compare their performances. We fine-tune and compare models pruned by C4 ( $r = 6$ ), MATH ( $r = 6$ ,  $r = 7$ ) and the original 8-expert model. The zero-shot GSM8K@1 and MATH@1 accuracies after fine-tuning different MoE models are shown in Tab. 4.
+
+<table><tr><td>Model</td><td>r</td><td>Pruning</td><td>Skipping</td><td>LM-eval</td><td>Speedup</td></tr><tr><td rowspan="6">Mixtral 8x7B</td><td>8</td><td></td><td></td><td>67.58</td><td>1.00×</td></tr><tr><td>8</td><td></td><td>✓</td><td>66.37</td><td>1.08×</td></tr><tr><td>6</td><td>✓</td><td></td><td>64.22</td><td>1.19×</td></tr><tr><td>6</td><td>✓</td><td>✓</td><td>62.91</td><td>1.23×</td></tr><tr><td>4</td><td>✓</td><td></td><td>59.57</td><td>1.27×</td></tr><tr><td>4</td><td>✓</td><td>✓</td><td>57.91</td><td>1.31×</td></tr><tr><td rowspan="6">Mixtral 8x7BInstruct</td><td>8</td><td></td><td></td><td>69.98</td><td>1.00×</td></tr><tr><td>8</td><td></td><td>✓</td><td>69.03</td><td>1.08×</td></tr><tr><td>6</td><td>✓</td><td></td><td>67.45</td><td>1.20×</td></tr><tr><td>6</td><td>✓</td><td>✓</td><td>66.04</td><td>1.27×</td></tr><tr><td>4</td><td>✓</td><td></td><td>63.88</td><td>1.27×</td></tr><tr><td>4</td><td>✓</td><td>✓</td><td>62.33</td><td>1.33×</td></tr></table>
+
+Table 5: Evaluation results of combining expert pruning with dynamic skipping. We carry out expert pruning using calibration data sampled from C4, then infer the pruned models with dynamic expert skipping. We set  $\beta$  as the median value of  $w_{e_1} / w_{e_0}$  of the calibration set. The dynamic expert skipping method further enhances inference speed with a slight performance drop.
+
+As can be seen, the fine-tuning process significantly reduces the performance drop incurred by expert pruning and leads to comparable results with tuning full-expert models. Specifically, for the Mixtral 8x7B Instruct model, the accuracy of the pruned 7-expert model on the GSM8K test set exceeds that of the 8-expert model. This suggests that for certain practical downstream tasks, a large number of experts might not be a necessity for achieving good performance. Also, the pruned models using samples from MATH as the calibration dataset outperform those using C4 after tuning, further highlighting the effectiveness of adopting domain-specific calibration datasets for task-specific models.
+
+# 4.3 Dynamic Expert Skipping Results
+
+This subsection assesses the effectiveness of our proposed dynamic expert skipping approach. Additionally, we explore the combination of both methods to further improve inference efficiency.
+
+Experiment Setup. We perform tests on task-agnostic models for better representativeness. The setup is similar to Sec. 4.1. We first prune the Mixtral 8x7B and Mixtral 8x7B Instruct model using calibration data sampled from the C4 dataset and get the pruned models with  $r = 6$  and  $r = 4$ . During the testing of different benchmarks, we dynamically skip certain experts. For evaluation, we report the zero-shot accuracies of 8 tasks from EleutherAI LM Harness (Gao et al., 2023). Our proposed dynamic expert skipping method does not influence the memory usage for model inference, so we just report the inference speed in this subsection.
+
+Baselines to Compare. We suggest that our proposed dynamic expert skipping can be seamlessly
+
+<table><tr><td>Model</td><td>Method</td><td>r</td><td>LM-eval</td></tr><tr><td rowspan="4">Mixtral 8x7B</td><td>Progressive</td><td>6</td><td>64.48</td></tr><tr><td>Layer-wise (Ours)</td><td>6</td><td>64.22</td></tr><tr><td>Progressive</td><td>4</td><td>57.53</td></tr><tr><td>Layer-wise (Ours)</td><td>4</td><td>59.57</td></tr></table>
+
+Table 6: Comparison between the layer-wise pruning manner and the progressive pruning manner. The progressive scheme will lead to more performance degradation with a high expert pruning rate.
+
+integrated with the expert pruning approach. For setting up baselines, we evaluate zero-shot accuracies of the original 8-expert model without dynamic expert skipping, as well as the accuracies of models pruned to  $r = 6$  and  $r = 4$  without dynamic expert skipping. Subsequently, we incorporate dynamic expert skipping into the inference process of these models, evaluate accuracies over benchmarks, and measure token generation speedup.
+
+Evaluation Results. The evaluation results are illustrated in Tab. 5. We show the average accuracies of the 8 zero-shot tasks at the "LM-eval" column, together with the inference speedup ratio compared to the original 8-expert models. As can be seen, based on expert pruning, the dynamic expert skipping method can further enhance the inference speed with just negligible performance drops. We can achieve nearly  $90\%$  performance of the Mixtral 8x7B Instruct model with half parameters and a  $1.33 \times$  token generation speedup. Another notable observation is that the Mixtral 8x7B Instruct model using both expert pruning and dynamic skipping with  $r = 6$  achieves the same inference speedup as the model using only expert pruning with  $r = 4$  while getting a much higher accuracy over the LM-eval benchmark. This phenomenon also proves the efficiency of our dynamic expert skipping approach. For a more comprehensive evaluation, we perform dynamic skipping on task-specific models and observe similar results. Experiment details are shown in the Appendix (Sec. A.4).
+
+# 4.4 More Analysis
+
+Discussion about Inference Speed. In Fig. 1, we also observe a notable inference acceleration when pruning experts from  $r = 6$  to  $r = 4$ , a phenomenon not attributable to decreased inter-GPU communication overhead. We think that this enhancement stems primarily from improved temporal and spatial locality. This includes reduced cache misses, optimized memory prefetching, and faster block loading. Readers may refer to papers related to memory-intensive LLM inference (such
+
+as (Alizadeh et al., 2023)) to find more explanation.
+
+More Ablations. We carry out efficient layerwise expert pruning in this work. Readers may be curious about the effectiveness compared with a layer-by-layer progressive searching paradigm, where the pruning of subsequent layers is aware of the pruning result of previous layers. To this end, we compare these two pruning paradigms in Tab. 6, leveraging the Mixtral 8x7B model and calibration samples from the C4 dataset. Although the layer-by-layer progressive manner can get slightly better results with fewer experts pruned, when it comes to a high expert pruning rate (e.g.,  $50\%$  pruning rate with  $r = 4$ ), more performance drop is observed. We attribute this to the possible overfitting of the small calibration dataset.
+
+# 5 Conclusion and Discussion
+
+In this paper, based on the structural characteristics of MoE LLMs and the shortcomings of current weight pruning schemes, we focus on expert-level model sparsification and, for the first time, provide post-training expert pruning together with dynamic (expert) skipping methods to enhance the deployment efficiency of MoE LLMs. Our methods can significantly reduce memory usage and enhance inference speed while maintaining high model performance. Looking ahead, we aim to further refine our pruning/skipping techniques and incorporate them with weight pruning or parameter quantization strategies, achieving more effective deploying approaches for MoE LLMs.
+
+# Limitations
+
+Our method can reduce memory usage and improve inference speed for more efficient deployment of MoE LLMs. Despite its advancements, there are still some limitations. Firstly, our method for expert pruning is based on the enumeration of expert combinations. This is feasible for pruning currently popular MoE LLMs with 4 or 8 experts. However, with the number of experts in each MoE layer increasing (e.g., 32 experts in one MoE layer), it will be cumbersome to perform our pruning algorithm. Secondly, we conduct experiments on the open-sourced Mixtral 8x7B and Mixtral 8x7B Instruct models as they are by far the most popular MoE LLMs. With the development of MoE LLMs, we will carry out experiments on other MoE LLMs in the future to give a more comprehensive analysis of the generalizability and scalability of our method.
+
+# Ethics Statement
+
+Our research focuses on improving the deployment efficiency of Mixture-of-Experts (MoE) large language models (LLMs) through expert-level sparsification techniques, aiming to reduce model sizes and enhance inference speed without compromising performance. While our methods offer potential benefits for deploying advanced LLMs more broadly and efficiently, we acknowledge the importance of considering the ethical implications of deploying such models. These include ensuring the responsible use of LLMs, mitigating biases in model outputs, and addressing privacy concerns. We commit to making our code available for transparency and encourage the community to use our findings responsibly, considering the societal impacts of deploying LLMs.
+
+# Acknowledgement
+
+This project is funded in part by National Key R&D Program of China Project 2022ZD0161100, by the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK, by General Research Fund of Hong Kong RGC Project 14204021. Hongsheng Li is a PI of CPII under the InnoHK.
+
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+Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv e-prints.  
+Omar Sanseviero, Lewis Tunstall, Philipp Schmid, Sourab Mangrulkar, Younes Belkada, and Pedro Cuenca. 2023. Mixture of experts explained.  
+Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538.
+
+Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2023. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695.  
+Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805.  
+Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023a. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.  
+Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023b. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.  
+Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2024. Mathcoder: Seamless code integration in LLMs for enhanced mathematical reasoning. In The Twelfth International Conference on Learning Representations.  
+Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, et al. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38-45.  
+Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284.  
+Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhi-jie Zhang, Kun Yuan, Wenxiu Sun, and Hongsheng Li. 2021. Learning n: m fine-grained structured sparse neural networks from scratch. arXiv preprint arXiv:2102.04010.  
+Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2024. Solving challenging math word problems using GPT-4 code interpreter with code-based self-verification. In The Twelfth International Conference on Learning Representations.
+
+# A Appendix
+
+# A.1 Expert Selection Tendency in MoE Models
+
+To investigate the tendency of expert selection during model inference, we analyze the Mixtral 8x7B model using samples from the C4 dataset (Raffel et al., 2019) (Fig. 5 (a)) and the MATH dataset (Hendrycks et al., 2021) (Fig. 5 (b)), respectively. C4 is a pre-training dataset representing the general relationships between experts, MATH is a dataset designed for a specific downstream task. We visualize the probability of different experts being selected in layer 0, layer 15, and layer 31 of the model during inference. As top-2 experts are chosen by default, each grid in the plot represents the frequency of two experts being selected simultaneously during the forward pass over the sample set (the x-axis and the y-axis of the plot each represent one expert). As can be seen from Fig. 5, the model exhibits a certain tendency in the selection of experts, particularly when tailored to specific downstream tasks.
+
+![](images/5e60804d743c87f5b314979df56a81dd01e6ff11c1f1057288420de52abd1d05.jpg)
+
+![](images/e01f4bc119776057b7ddfbcccda68075b7f2e2a64e749c2ad0f87813b3bb0545.jpg)
+
+![](images/b3510e522ca5015da954dcf99177d7de3af0af1833360696d9951f81200051f1.jpg)
+
+![](images/9cbe5f7a2091eb56a09b54ffc77125e0f18d478c4d65b6e119794dbfe8f21611.jpg)  
+Figure 5: Frequency visualization of expert selection in layer 0, layer 15, and layer 31 for the Mixtral 8x7B model on samples of (a) C4 (Raffel et al., 2019) and (b) MATH (Hendrycks et al., 2021) dataset respectively. The model exhibits certain preferences in the selection of experts.
+
+![](images/84b6988597b3fd0eabc26fcce18d75406626d1594f9ca2754af0a6dadff5eb6a.jpg)
+
+![](images/8f798d2b8b30314d0ffca2a8a7c1419b98d55011763a79300b0140e519aa00ab.jpg)
+
+# A.2 Theoretical Insight and Broader Application of Dynamic Skipping
+
+Consider a single MoE layer. Suppose in a top- $k$  setting with a total of  $n$  experts, for every single token  $\pmb{x}$ , the routing weights of the  $n$  experts are  $w_{1},\ldots ,w_{n},\sum_{i = 1}^{n}w_{i} = 1$ . Without loss of generality, we assume  $w_{1}\geq w_{2}\geq \dots \geq w_{k}\geq \dots \geq$ $w_{n}$ , and the output feature vector of each expert are  $\mathbf{f}_1,\ldots ,\mathbf{f}_k,\ldots ,\mathbf{f}_n$ , where  $\mathbf{f}_i = \mathcal{E}_i(\mathbf{x})$ .
+
+Without dynamic skipping, the original output
+
+of this layer should be:
+
+$$
+\boldsymbol {z} = \frac {1}{\sum_ {m = 1} ^ {k} w _ {m}} \sum_ {m = 1} ^ {k} w _ {m} \mathbf {f} _ {m}.
+$$
+
+Suppose after dynamic skipping, only top-  $i$  experts remain  $(1\leq i\leq k)$ . Then the output is:
+
+$$
+\hat {z} = \frac {1}{\sum_ {m = 1} ^ {i} w _ {m}} \sum_ {m = 1} ^ {i} w _ {m} \mathbf {f} _ {m}.
+$$
+
+As defined above in the main paper, the reconstruction loss can be calculated by:
+
+$$
+\mathcal {L} = | | \hat {z} - z | | _ {2}.
+$$
+
+Through experiment, we observe that the distribution of  $||\mathbf{f}_1 - \mathbf{f}_2||_2$  is fairly concentrated for Mixtral 8x7b on the C4 calibration set. Thus for simplification, we assume  $||\mathbf{f}_m - \mathbf{f}_n||_2 (m \neq n)$  to be a fixed value, say  $D$ . Therefore,
+
+$$
+\begin{array}{l} \mathcal {L} = \left\| \hat {\boldsymbol {z}} - \boldsymbol {z} \right\| _ {2} \\ = \left\| \frac {1}{\sum_ {m = 1} ^ {i} w _ {m}} \sum_ {m = 1} ^ {i} w _ {m} \mathbf {f} _ {m} - \frac {1}{\sum_ {m = 1} ^ {k} w _ {m}} \sum_ {m = 1} ^ {k} w _ {m} \mathbf {f} _ {m} \right\| _ {2} \\ = \frac {\left| \left| \sum_ {n = 1} ^ {k} \sum_ {m = 1} ^ {i} w _ {n} w _ {m} \mathbf {f} _ {m} - \sum_ {n = 1} ^ {i} \sum_ {m = 1} ^ {k} w _ {n} w _ {m} \mathbf {f} _ {m} \right| \right| _ {2}}{\left(\sum_ {m = 1} ^ {i} w _ {m}\right) \left(\sum_ {m = 1} ^ {k} w _ {m}\right)} \\ = \frac {\left| \left| \sum_ {n = 1} ^ {k} \sum_ {m = 1} ^ {i} w _ {n} w _ {m} \mathbf {f} _ {m} - \sum_ {n = 1} ^ {k} \sum_ {m = 1} ^ {i} w _ {n} w _ {m} \mathbf {f} _ {n} \right| \right| _ {2}}{\left(\sum_ {m = 1} ^ {i} w _ {m}\right) \left(\sum_ {m = 1} ^ {k} w _ {m}\right)} \\ = \frac {\left| \left| \sum_ {n = i + 1} ^ {k} \sum_ {m = 1} ^ {i} w _ {n} w _ {m} \left(\mathbf {f} _ {m} - \mathbf {f} _ {n}\right) \right| \right| _ {2}}{\left(\sum_ {m = 1} ^ {i} w _ {m}\right) \left(\sum_ {m = 1} ^ {k} w _ {m}\right)} \\ \leq \frac {\left| \left| \sum_ {n = i + 1} ^ {k} \sum_ {m = 1} ^ {i} w _ {n} w _ {m} \right| \right| _ {2} \cdot D}{\left(\sum_ {m = 1} ^ {i} w _ {m}\right) \left(\sum_ {m = 1} ^ {k} w _ {m}\right)} \\ \end{array}
+$$
+
+$$
+\begin{array}{l} (= \text {h o l d s i f a l l} (\mathbf {f} _ {m} - \mathbf {f} _ {n}) \text {a r e o f t h e s a m e d i r e c t i o n)} \\ = \frac {\sum_ {m = i + 1} ^ {k} w _ {m}}{\sum_ {m = 1} ^ {k} w _ {m}} D. \\ \end{array}
+$$
+
+We set an upper bound  $H$  ( $H \leq D$ ) on  $\mathcal{L}$  (make  $\mathcal{L} \leq H$ ) to trade-off between accuracy and inference speed, then have
+
+$$
+\frac {\sum_ {m = i + 1} ^ {k} w _ {m}}{\sum_ {m = 1} ^ {k} w _ {m}} D \leq H.
+$$
+
+Let  $\beta = \frac{H}{D}$ , we have:
+
+$$
+\sum_ {m = i + 1} ^ {k} w _ {m} \leq \beta \cdot \sum_ {m = 1} ^ {k} w _ {m}.
+$$
+
+Therefore, in this generalized setting, dynamic skipping should reserve the top- $i^{*}$  experts where
+
+$$
+\begin{array}{l} i ^ {*} = \min i \\ \text {s . t .} \sum_ {m = i + 1} ^ {k} w _ {m} \leq \beta \cdot \sum_ {m = 1} ^ {k} w _ {m}. \\ \end{array}
+$$
+
+Specifically, in the top-2 setting, for computational simplicity (reduce the use of additions), let  $\beta = \frac{H}{D - H}$  (a little abuse of notation), then the dynamic skipping criteria for top-2 is
+
+$$
+w _ {2} \leq \beta w _ {1}.
+$$
+
+# A.3 Experiments on the Sizes of Calibration Datasets
+
+We prune the Mixtral 8x7b model with different sizes of calibration datasets. To be specific, we randomly sample 1, 2, 4, 16, 64, and 256 sequences (each composed of 2048 tokens) from the C4 dataset to form calibration datasets. Then the model is pruned to  $r = 6$  and evaluated on various benchmarks. The average LM-eval results are reported in Tab. 7.
+
+<table><tr><td>Number of Sequence</td><td>LM-eval</td></tr><tr><td>1</td><td>62.63</td></tr><tr><td>2</td><td>63.93</td></tr><tr><td>4</td><td>63.53</td></tr><tr><td>16</td><td>63.59</td></tr><tr><td>64</td><td>64.32</td></tr><tr><td>128</td><td>64.22</td></tr><tr><td>256</td><td>63.94</td></tr></table>
+
+As can be seen, using 64 and 128 sequences can result in the highest overall results  $(\geq 64)$ . Using a small set of sequences will possibly lead to performance degradation (especially for using just 1 sequence), but our method is somewhat robust to the size of datasets, as seen from the table.
+
+# A.4 Dynamic Skipping for Domain-specific Tasks
+
+We also perform dynamic expert skipping on domain-specific tasks (mathematical reasoning tasks). We calibrate  $\beta$  for each layer using samples from the training set of MATH and evaluate 5-shot accuracy on the GSM8K dataset. We also test and report the token generation speed of each MoE model. The results are shown in Tab. 8. In this case, dynamic expert skipping leads to more performance drops. But for the Mixtral 8x7B Instruct model, expert pruning with 2 experts and combining dynamic skipping also leads to the same inference speedup with pruning 4 experts, while achieving higher evaluation accuracy.
+
+# A.5 Actual Memory Reduction
+
+A more detailed statistical comparison between our expert pruning method with baseline methods on
+
+Table 7: Performances of expert pruning with different sizes of calibration datasets.  
+
+<table><tr><td>Model</td><td>r</td><td>Pruning</td><td>Skipping</td><td>GSM8K (5-shot)</td><td>Speedup</td></tr><tr><td rowspan="6">Mixtral 8x7B</td><td>8</td><td></td><td></td><td>58.61</td><td>1.00×</td></tr><tr><td>8</td><td></td><td>✓</td><td>54.28</td><td>1.08×</td></tr><tr><td>6</td><td>✓</td><td></td><td>51.25</td><td>1.20×</td></tr><tr><td>6</td><td>✓</td><td>✓</td><td>47.16</td><td>1.21×</td></tr><tr><td>4</td><td>✓</td><td></td><td>37.07</td><td>1.29×</td></tr><tr><td>4</td><td>✓</td><td>✓</td><td>34.80</td><td>1.30×</td></tr><tr><td rowspan="6">Mixtral 8x7BInstruct</td><td>8</td><td></td><td></td><td>63.46</td><td>1.00×</td></tr><tr><td>8</td><td></td><td>✓</td><td>61.94</td><td>1.05×</td></tr><tr><td>6</td><td>✓</td><td></td><td>58.38</td><td>1.20×</td></tr><tr><td>6</td><td>✓</td><td>✓</td><td>53.98</td><td>1.28×</td></tr><tr><td>4</td><td>✓</td><td></td><td>47.01</td><td>1.28×</td></tr><tr><td>4</td><td>✓</td><td>✓</td><td>40.33</td><td>1.33×</td></tr></table>
+
+the Mixtral 8x7B model is shown in Tab. 9.
+
+Table 8: Evaluation results of combining expert pruning with dynamic skipping for domain-specific tasks. Combining two expert-level sparsification methods will lead to more efficient deployment.  
+
+<table><tr><td>Method</td><td>Sparsity</td><td>Memory (MB)</td></tr><tr><td>None</td><td>None (r = 8)</td><td>89,926 (100%)</td></tr><tr><td>Wanda</td><td>2:4</td><td>51,214 (57%)</td></tr><tr><td>Ours</td><td>r = 6</td><td>68,383 (76%)</td></tr><tr><td>Ours</td><td>r = 4</td><td>46,879 (52%)</td></tr></table>
+
+Table 9: Memory reduction comparison of our expert pruning method with baselines on Mixtral 8x7B.
+
+# A.6 Relationships with Other Network Pruning and Parameter Quantization Methods
+
+As plug-and-play techniques, both our proposed expert pruning and dynamic skipping methods are orthogonal to other model light-weighting schemes (e.g., weight pruning (Frantar and Alistarh, 2023; Sun et al., 2023), token pruning (Kim et al., 2022; Ding et al., 2023)) and are compatible with weight quantization approaches (Frantar et al., 2022; Lin et al., 2023).
+
+# A.7 More Experiment Details
+
+In this part, we give more experimental details for a better understanding of our proposed methods.
+
+Calibration Set Construction for Expert Pruning. For task-agnostic models, we use the samples from C4 (Raffel et al., 2019) as the calibration dataset. Following the setting of Wanda, we sample from the first part of the training data<sup>6</sup>. For task-specific (mathematics) models, we use samples from the training set of MATH (Hendrycks et al., 2021). The structure of the MATH dataset is different from C4, so we reconstruct the dataset in the format of C4 and randomly sample from it.
+
+Calibration Set Construction for Dynamic (Expert) Skipping. To calculate  $\beta$  for dynamic expert skipping in each MoE layer, we forward the MoE model over the calibration dataset and set  $\beta$  as the median value of  $\frac{w_{e_1}}{w_{e_0}}$  separately for each layer. We choose to use the median value over the calibration dataset as in this case, the skipping will happen with around  $50\%$  possibility. Here we provide the value of  $\beta$  for the Mixtral 8x7B model with calibration data sampled from C4 and MATH respectively. As can be seen, the parameter in each layer differs significantly.
+
+C4: 0.402,0.494,0.463,0.484,0.478,0.491,0.523, 0.521,0.544,0.570,0.574,0.489,0.503,0.618,0.568, 0.535,0.559,0.519,0.537,0.487,0.469,0.461,0.461, 0.469,0.458,0.418,0.433,0.418,0.406,0.433,0.447, 0.535
+
+MATH: 0.503,0.586,0.505,0.531,0.509,0.422, 0.511,0.461,0.447,0.478,0.529,0.454,0.472,0.531, 0.499,0.486,0.503,0.491,0.430,0.440,0.402,0.423, 0.386,0.407,0.395,0.354,0.340,0.351,0.334,0.368, 0.365,0.346
+
+Model Fine-tuning. In the part of task-specific expert pruning for domain-specific tasks, we fine-tune the Mixtral 8x7B and Mixtral 8x7B Instruct models with 8 experts, 7 experts, and 6 experts on the MetaMathQA (Yu et al., 2023) dataset. The training is conducted on 16 A100-80G GPUs. We train the model for 900 steps, using a learning rate of 2e-5 with the cosine learning rate scheduler.

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+# NounAtlas: Filling the Gap in Nominal Semantic Role Labeling
+
+Roberto Navigli $^{1}$  Dennis Rotondi $^{3}$
+
+Marco Lo Pinto<sup>1</sup>  
+Simone Ciciliano<sup>4</sup>
+
+Pasquale Silvestri<sup>1</sup> Alessandro Scirè<sup>1,2</sup>
+
+$^{1}$ Sapienza NLP Group, Sapienza University of Rome  $^{2}$ Babelscape, Italy
+
+<sup>3</sup>University of Stuttgart <sup>4</sup>Free University of Bozen
+
+{navigli, scire} @diag.uniroma1.it, dennis.rotondi@ki.uni-stuttgart.de
+
+{lopinto.1746911, silvestri.1749274}  $@$  studenti.uniroma1.it, sciciliano@unibz.it
+
+# Abstract
+
+Despite the significant advances made in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus calling for proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach in order to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although it is automatically created, NounAtlas – our nominal frame inventory – is subsequently fully validated. We then put forward a technique for generating silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices, which enable seamless integration of our predicate inventory with its verbal counterpart, i.e., VerbAtlas, we can mix verbal and nominal data and perform robust SRL on both types of predicate.
+
+# 1 Introduction
+
+In the ever-evolving field of Natural Language Processing (NLP), the pursuit of Natural Language Understanding (NLU) has garnered increasing attention over the past two decades. Among the open challenges in this domain, Semantic Role Labeling (SRL) is still far from being solved. Pioneered by Gildea and Jurafsky (2002), SRL is the task of identifying the semantic relations between predicates and their arguments (i.e. "Who did What to Whom, When, Where, and How?"). SRL has already proven to be a valuable asset, contributing to diverse tasks, such as question answering (Shen and Lapata, 2007), textual inference (Bastianelli et al., 2013), neural machine translation (Song et al., 2019), visual semantic role labeling (Chen et al., 2021; Sadhu et al., 2021), text summariza
+
+tion (Trandabat, 2011) and storytelling (Peng et al., 2022).
+
+One common oversimplification regarding SRL is the assumption that most predicates are verbs. Indeed, research has focused heavily on verbs as the driving force of meaning in sentences, also propelled by the development of verb-centric resources (Palmer et al., 2005; Schuler, 2005; Di Fabio et al., 2019). In many contexts, however, nominal predicates are at least as important as their verbal counterparts. This phenomenon is particularly evident in media content, including newspaper headlines, blog titles, short text messages, etc. Consider for instance the sentence "Astounding discovery in the laboratory yesterday night: Dr. Jones unveils groundbreaking research findings!". In this example, the noun "discovery" serves as the Predicate, indicating an action, with its arguments being "Astounding" (ATTRIBUTE of the discovery), "in the laboratory" (LOCATION), "yesterday night" (time), and "Dr. Jones" (AGENT). Such nominal predicates need to be specifically addressed in order for downstream applications to take full advantage of SRL.
+
+While some resources have been provided that include nominal framesets, such as NomBank (Meyers et al., 2004) and FrameNet (Baker et al., 1998), their coverage is limited and they provide either predicate-specific roles (NomBank) or overspecific or incomplete frame scenarios (FrameNet).
+
+Our work aims to fill this gap and equip Semantic Role Labeling with both a comprehensive inventory of nominal predicates (paired with verbal ones) and annotated datasets to perform the task.
+
+In summary our contributions are the following:
+
+- We introduce NounAtlas, the first large-scale inventory of predicates for nominal SRL;  
+- To facilitate the design of SRL approaches leveraging our resource, we create a large-scale dataset annotated with nominal predi
+
+cates and roles from NounAtlas;
+
+- We conduct an extensive evaluation of several baselines trained on our dataset. Our experiments pave the way for the development of a state-of-the-art unified system for both nominal and verbal SRL.  
+- We release our code and resource at https://github.com/SapienzaNLP/nounatlas under a CC license.
+
+# 2 Related Work
+
+# 2.1 Nominal Resources
+
+Existing resources for SRL are predominantly centered on verbs. The earliest examples of such resources include PropBank (Palmer et al., 2005) and VerbNet (Schuler, 2005). More recently, Di Fabio et al. (2019) introduced VerbAtlas, a hand-crafted lexical-semantic resource that provides a widecoverage verbal predicate inventory offering coarse semantically-coherent frames and informative semantic role labels. The frames are created by systematically clustering all verbal synsets in WordNet (Miller, 1995) using FrameNet-style (Baker et al., 1998) scenarios, while adopting VerbNet's human-interpretable and cross-frame role labels.
+
+Based on the frame semantic theory of Fillmore (2006), FrameNet was the first resource to be used for SRL (Gildea and Jurafsky, 2002), and one that includes nominal predicates in its frames. However, FrameNet does not grant full coverage<sup>1</sup>, proving challenging to scale for out-of-domain SRL (Hartmann et al., 2017). Furthermore, although it is explicit and thus human-readable, FrameNet's roles are frame-specific, limiting their generalizability across frames with similar roles.
+
+Concerning nominal predicate resources, the state of the art is currently represented by PropBank (Palmer et al., 2005) and NomBank (Meyers et al., 2004). NomBank is a project that aims to annotate a subset of noun predicates that take arguments. It utilizes a framework similar to that of the first version of PropBank, but for annotating noun predicates rather than verbal ones. Differences arise from variations in noun and verb argument structures, as well as disparities in how nouns and verbs are handled within the Penn Treebank (Marcus et al., 1993). These differences in predicate
+
+treatment pose challenges in developing an SRL system for both verbal and nominal predicates due to the overhead of treating them in distinct ways, as seen in CoNLL-2009 (Hajic et al., 2009).
+
+With NomBank not having been augmented or updated for a long time, nominal predicates began to be included in PropBank, starting from version 2 (Babko-Malaya et al., 2004), where they are referred to as event variables. However, PropBank is verb-oriented: all nominal units have been integrated as aliases of previously-annotated verbal rolesets, which means they are not as thoroughly annotated as their verbal counterparts. Both NomBank and PropBank are primarily limited by their use of enumerated arguments (Arg0, Arg1, etc.), with the absence of an explicit classification of the semantics of predicate roles, making the annotation hard to interpret and overloading the meaning of an argument label. For instance, the first arguments of "drinking" and "feeling" are both labeled as Arg0, despite representing distinct semantic roles (AGENT and EXPERIENCESR, respectively). Also, as of today, NomBank and PropBank suffer from limited coverage of nominal predicates, including 5577 and 4295 nouns, respectively. Moreover, NomBank is built upon PropBank, with 1308 frames and 861 rolesets being shared between the two resources, and a total of 8815 distinct rolesets.
+
+# 2.2 Nominal Semantic Role Labeling
+
+As Orlando et al. (2023) highlighted in their experiments, nominal Semantic Role Labeling is still far from being solved. Here, a clear indication to focus on enriching nominal resources is the fact that the best SRL neural models (Shi and Lin, 2019; Conia et al., 2021a; Conia and Navigli, 2022; Conia et al., 2022), trained exclusively on verbal predicates, struggle to generalize to unseen nominal ones. Research on non-verbal predicates remains significantly underdeveloped, with existing efforts centered mainly around transferring knowledge from verbal predicates to their nominal counterparts in an unsupervised manner (Klein et al., 2020; Zhao and Titov, 2020), rather than creating resources that enable SRL to generalize easily between verbal and nominal predicates. The limited attention given to non-verbal predicates may be attributed to the prevailing design of current SRL datasets, which utilizes predominantly verbal predicates (Daza and Frank, 2020; Tripodi et al., 2021; Jindal et al., 2022) annotated based on the aforementioned predicate inventories.
+
+With our work we aim to fill the gap in nominal SRL. First, we focus on constructing NounAtlas, an inventory of over 10 000 nominal predicates, curated by means of leveraging and inheriting knowledge and semantic frames from VerbAtlas. This addresses the above-mentioned coverage issues and provides integration between nominal and verbal predicates, which can belong to the same frames. Second, we employ the resulting inventory to produce, for the first time, a large dataset for wide-convergence nominal SRL.
+
+# 3 NounAtlas
+
+Before delving into the process of constructing NounAtlas, our large-scale nominal predicate inventory, we highlight a few features in VerbAtlas that enable this process.
+
+VerbAtlas As mentioned in Section 2, VerbAtlas uses verbal synsets from WordNet as verbal predicate proxies, organizing them into semantically-coherent frames. This includes more than 400 synset clusters, each assigned a human-readable label, such as EAT-BITE and SPEAK.
+
+A key advantage of VerbAtlas over its alternatives is its common prototypical argument structure and cross-frame set of semantic roles à la VerbNet: for instance, the frame SPEAK features the roles of AGENT and TOPIC. Importantly, being univocally associated with exactly one frame, each synset is associated with the corresponding frame semantics and predicate argument structure, which can be exploited for SRL. For instance, in Figure 1 the verbal synsets EMBARRASS.v.01 and PRIDE.v.01 are included in the CAUSE-MENTAL-STATE frame.
+
+# 3.1 Creating a Large-Scale Nominal Predicate Inventory
+
+Our first objective is to produce NounAtlas, a largescale inventory of nominal predicates. To achieve this goal, we leverage the VerbAtlas frames to group WordNet nominal synsets that express analogous semantics in nominal form. For instance, we utilize the EAT-BITE verbal frame, which contains synsets such as EAT.V.01, BITE.V.01, and DEVOUR.V.04, to cluster nominal synsets like EATING.N.01, BITE.N.08 and CHOMPING.N.01. We now detail the procedure for creating our nominal predicate inventory.
+
+# 3.1.1 Event-characterizing synset selection
+
+First, we define a nominal predicate as a concept which conveys an event, action or situation, allowing a clear identification of roles such as AGENT, PATIENT, THEORY, etc. In our work we aim to identify nominal synsets with such properties (which we call event-characterizing synsets) as proxies for nominal predicates. As an example, we include WORK.N.01 (activity directed toward making or doing something) as an event-characterizing synset, whereas we exclude non-event synsets, such as WORKER.N.01 (a person who works at a specific occupation) or TABLE.N.02 (a piece of furniture). To do so, we sort the full list of nominal synsets in WordNet 3.0 by their number of hyponyms (i.e., descendants in the nominal taxonomy) and select the top synsets  $s_j$  in the list which are deemed to characterize events. This results in the identification of nine general event-characterizing synsets which are then reduced to four due to the removal of those subsumed by other synsets in the set: EVENT.N.01, EVENT.N.02, PROCESS.N.02 and PROCESS.N.06. We then select as our final set of event-characterizing synsets all the 10 086 nominal synsets that are descendants, i.e., direct or indirect hyponyms, of our set of four general event-characterizing synsets, including the latter.
+
+# 3.1.2 WordNet-based synset-to-frame mapping
+
+With the goal of clustering nominal synsets into frames, we bootstrap the synset-to-frame mapping process by exploiting the WordNet derivationally-related form (DRF) relations, which establish connections between senses of nominal synsets and their corresponding verbal counterparts. For instance, the nominal synset EATING.N.01 (the act of consuming food) is mapped to the verbal synset EAT.V.01 (take in solid food) through a DRF relation. Since the latter synset is contained in the EAT-BITE frame in VerbAtlas, we identify EATING.N.01 as a potential candidate for this frame.
+
+However, multiple cases need to be considered at this stage for a given nominal synset  $s$ :
+
+-  $s$  is linked to a single verbal synset  $v$ . This allows a straightforward inclusion in the corresponding VerbAtlas frame (Unambiguous link), i.e.  $frame(s) \coloneqq \text{frame}(v)$ , where frame returns the VerbAtlas frame of the input synset.
+
+![](images/6e6407273107bdfa4de56601254504eee34cf5d4293ae27ee6bf067be5e66a1d.jpg)  
+Figure 1: Visual representation of the different types of link between nominal and verbal synsets (and the corresponding VerbAtlas frames). Orange arrows represent the derivationally related forms (DRFs) connections in WordNet, whereas blue arrows represent the WordNet hyponymy relations. Best seen in colors.
+
+- There are multiple verbal synsets  $V$  linked to  $s$ . If all the verbal synsets in  $V$  belong to the same frame, the frame mapping is again straightforward, i.e.  $\text{frame}(s) := \text{frame}(v)$  for any  $v \in V$ . Otherwise, we manually map the nominal synset  $s$  to the most suitable frame among those containing verbal synsets linked to  $s$  (Manually-curated links).  
+-  $s$  is not associated with any verbal synset through DRFs (Non-existing links).
+
+We provide a visual representation of each type of link in Figure 1. In this example, since EMBARRASSMENT.N.03 is connected to EMBARRASS.V.01 through an Unambiguous link, we can include it within the CAUSE-MENTAL-STATE frame. Conversely, CONGRATULATION.N.02 requires a manual annotation, since its DRF links lead to synsets belonging to multiple frames (i.e. APPROVE_PRAISE and CAUSE-MENTAL-STATE). Finally, ALIENATION.N.04 is an example of a Non-existing link, since it is not connected via DRF to any verbal synset from VerbAtlas.
+
+Results and Statistics We report the statistics concerning the WordNet-based mapping of nominal synsets to VerbAtlas frames in Table 1. In Figure 2 we show the expectedly skewed distribution of verbal and nominal synsets within each frame, with some frames comprising over 100 synsets (e.g. SPEAK and HIT), and others being limited up to 3 (e.g. RESERVE and FORGET). Interestingly, the figure shows that frames containing more verbal synsets tend to also include a higher number of nominal synsets.
+
+To evaluate the quality of the Unambiguous
+
+![](images/5739e02829dc1283d75a99f481e90bc581eb039e48a441b227fb24f8d2d53bad.jpg)  
+Figure 2: Distribution of verbal (represented in blue) and nominal synsets (orange) across frames. On the x-axis, frames are ordered by their number of verbal synsets. Best seen in colors.
+
+<table><tr><td>Mapping Type</td><td>Count</td></tr><tr><td>Unambiguous links</td><td>3053</td></tr><tr><td>Manually-curated links</td><td>1129</td></tr><tr><td>Non-existing links</td><td>5904</td></tr></table>
+
+Table 1: Statistics about WordNet-based synset-to-frame mapping for the 10 086 nominal synsets.
+
+links, we manually classified a random subset comprising 100 instances without using the DRF information and we found that  $82\%$  of the manual classifications were in agreement with the automatic ones, with another  $17\%$  upon inspection found to be equally valid. These results demonstrate that the WordNet DRF relations can be leveraged effectively to classify a substantial portion, i.e.  $31\%$ , of the overall event-characterizing nominal synsets.
+
+We then moved on to the Manually-curated links. To estimate the complexity of this task, we computed the inter-annotator agreement on a randomly-sampled subset of 100 annotations, with a resulting Cohen's  $\kappa$  of 0.57 (moderate agreement).
+
+The results on both types of link can be attributed to the frequent scenario where the proposed VerbAtlas frames can provide overlapping semantics, leading to multiple valid options for a given nominal synset. For example, the nominal synset EXAMINATION.N.01 was mapped to the ANALYZE frame by one annotator and to VERIFY by another.
+
+# 3.1.3 Ranking frames for unlinked synsets
+
+To address the challenge of Non-existing links, and thereby increase the coverage of our mapping, we design an automatic approach aimed at mapping nominal synsets to the best suited verbal frame.
+
+First, we gather all the nominal synsets involved in Unambiguous and Manually-curated links together with all the verbal synsets from VerbAtlas.
+
+Then, we create a dataset organized into pairs of (nominal synset, verbal synset) definitions as follows: given a nominal synset  $s$  and its mapped frame  $f$ , we pair the definition of  $s$  with the definition of the verbal synset  $v \in f$  which is linked to  $s$  through a DRF relation. We label this pair as positive. Then, we augment our dataset of positive pairs by additionally associating the definition of  $s$  with the ones of  $K$  randomly-selected verbal synsets also belonging to  $f$ . Finally, we obtain  $K + 1$  negative pairs by repeating the same process, but this time selecting as many random frames  $f_i$ , such that  $f_i \neq f \forall i \in \{1, \dots, K + 1\}$ , to sample the verbal synsets from. Each definition is expanded with the corresponding synset's lemmas, which are pretended in a comma-separated format. For instance, we generate a positive pair by associating the nominal synset APPPOINTMENT.N.01 ("the act of putting a person into a non-elective position"), with the verbal synset DELEGATE.V.02 ("give an assignment to a person to a post, or assign a task to a person") both linked to the frame ASSIGN-SMT-TO-SMN. We then obtain a negative pair by associating the same nominal synset with a verbal synset from a different frame e.g. with WASH.V.09 ("remove by the application of water or other liquid and soap or some other cleaning agent"), belonging to the frame WASH CLEAN.
+
+Ranking approach. We use the resulting dataset in a binary classification task, with a positive label
+
+indicating that the two synsets in the pair belong to the same frame and a negative label indicating otherwise. We train a Cross-Encoder model (Reimers and Gurevych, 2019) on our dataset. Then, at inference time, given a nominal synset  $s$ , we link it to a VerbAtlas frame through the following strategy:
+
+1. From each VerbAtlas frame, we randomly select a maximum number of available verbal synset definitions, up to 10, and pair them with the definition of  $s$ ;  
+2. We utilize our model to score each pair;  
+3. We derive the final score for a frame as the average score of its 10 pairs;  
+4. We rank frames according to their scores, arranged from highest to lowest. A higher score indicates a greater likelihood of the nominal synset being associated with the respective frame.
+
+Using this approach, for each nominal synset with Non-existing links we are able to construct a similarity ranking of all the VerbAtlas frames.
+
+Experimental setup. We used mpnet-v2 as our Encoder model, and we finetuned it on our dataset. We added a linear binary classification head on top. Binary cross entropy was employed as loss, while we leveraged Adam as optimizer, with an initial learning rate of  $3 \times 10^{-5}$ . All the experiments in our work were conducted on a computing system equipped with an NVIDIA RTX 3090. We implemented the Cross-Encoder in PyTorch and PyTorch Lightning, using the Transformers library (Wolf et al., 2020). We selected the hyperparameter values according to the best F1 score on the validation split of our dataset.
+
+Training and evaluation datasets. With the aim of assessing the quality of our ranking approach, we created a gold standard evaluation dataset composed of pairs of definitions of (nominal, verbal) synsets. To create the training and validation splits, we selected nominal synsets involved in Unambiguous links. Specifically, for each frame, we partitioned its synsets into  $80\%$  for training and  $20\%$  for validation. To build the test set and ensure its reliability, we used the definitions of all the nominal synsets with a Manually-curated link. We
+
+paired each nominal synset definition in the train, validation and test sets with a maximum of  $2 \cdot K$  definitions of verbal synsets, generating an equal number of  $K$  positive and negative pairs. Recall from the first part of this section that, given a nominal synset  $s$ , half of the definition pairs are positive pairs built using definitions of verbal synsets sampled from the same frame that  $s$  belongs to, i.e.  $frame(s)$ , while the other half are built using definitions of verbal synsets sampled from random frames, each different from  $frame(s)$ .
+
+We set  $K$  to 30, 3, and 3 for our training, validation, and test splits, respectively. As a result, we obtained 132484 pairs for the training set, 3648 for the validation set, and 6546 for the test set.
+
+Results and discussion. We evaluate the performance of our system for the binary classification task with the accuracy and F1 metrics, and for the ranking task using the Top-k accuracy scores. We report the results of our evaluation in Table 2.
+
+As can be seen, our model achieves high accuracy and F1 on binary classification. Moreover, the Top-k accuracy achieved highlights the ability of our system to provide the correct frame among the top-5 ranked. This outcome paves the way for an effective manual annotation process, aimed at selecting one frame among the top-5 ranked by our system, given a nominal synset (cf. next section).
+
+# 3.1.4 Manual mapping of unlinked synsets
+
+We can now proceed to mapping unlinked synsets to frames: given a nominal synset  $s$ , we tasked an expert annotator to select the most suitable frame from among the top-5 provided in the similarity ranking of  $s$ . In special cases where the annotator was not able to choose a suitable candidate from the provided frames, they had the option to consult VerbAtlas, and select a more appropriate frame. The annotator was specifically recruited for this task. We paid them in accordance with the standard salary of their geographical location. They took an average of 3 minutes to annotate one item. The result of our entire semi-automatic mapping process was a large-scale nominal predicate inventory for SRL, which we have called NounAtlas. We note that, by construction, the VerbAtlas and NounAtlas frames share the same labels, making it possible to bring together nominal and verbal synsets into a unified nominal and verbal resource.
+
+Results. The annotator successfully linked 5876 out of the 5904 synsets with Non-existing links
+
+![](images/dc0c01d6b655ed34f8653663774a2a7229f0a441e76d8ac6aeb4ec9939116230.jpg)  
+Figure 3: Example of SRL tagging with PropBank and FrameNet, compared to NounAtlas.
+
+(99.6%). They selected 5207 frames (88.2% of the total) among the top-5 ranked by our system, with 2912 (49.3%) being the top-1. The remaining synsets were classified by directly accessing the full VerbAtlas frame inventory.
+
+# 3.1.5 Qualitative Comparison
+
+To highlight the inherent advantages of using NounAtlas, consider the annotations reported in Figure 3 of a sample sentence annotated with different lexical-semantic nominal inventories. As mentioned in Section 2, PropBank's underspecified role semantics can result in models that struggle to generalize to out-of-domain predicates. FrameNet's roles, instead, are explicit, but frame-specific, resulting in a very large set of different role labels: in the example above, the roles "Ingestor" and "Ingestible" are specific only for the frame "Ingestion". These problems are solved when using NounAtlas, which provides just a few, explicit and cross-frame semantic roles over a very large set of nominal predicates, enabling a more extensive coverage.
+
+# 4 Creating a Nominal SRL Dataset
+
+As a result of the work outlined in Section 3.1, we obtained an inventory of nominal predicates organized into frames. However, we still have to build a large dataset for nominal Semantic Role Labeling, i.e. annotated with our nominal predicates. In what follows, we achieve this goal by (i) collecting WordNet verbal sense occurrences (Section 4.1), (ii) transforming each verbal predicate annotation into a nominal one, adjusting the sen
+
+<table><tr><td colspan="2">Binary classification</td><td colspan="5">Ranking</td></tr><tr><td>Accuracy</td><td>F1-Score</td><td>Acc@1</td><td>Acc@2</td><td>Acc@3</td><td>Acc@4</td><td>Acc@5</td></tr><tr><td>91.9</td><td>91.4</td><td>44.0</td><td>61.3</td><td>70.6</td><td>77.7</td><td>82.1</td></tr></table>
+
+Table 2: Performance of the Cross-Encoder in the binary classification task and the top-k accuracies  $(k = 1,\dots ,5)$
+
+tence accordingly, and obtaining the corresponding frame annotation (Section 4.2), (iii) propagating the argument role annotations (Section 4.3).
+
+# 4.1 Initial Sentence Corpus
+
+The initial step involves collecting sentences featuring sense-annotated verbal predicates. For this purpose, we use SemCor (Miller et al., 1993) – the largest corpus manually annotated with WordNet senses – to extract those sentences which contain at least one verb annotated with a synset  $v$  such that i)  $v$  is directly linked via DRF to a nominal synset  $s$  and, ii)  $v$  and  $s$  are linked through an Unambiguous link.
+
+# 4.2 Predicate Nominalization
+
+The next step consists in transforming each verbal predicate occurrence in our collected sentences into nominal form. To do so, first, we use a Large Language Model (LLM) prompted to nominalize a given verbal predicate annotated with synset  $v$  using any of the synonyms in the corresponding nominal synset  $s = \{n_1, \dots, n_m\}$ , i.e. its deverbal forms. We use the following prompt:
+
+Change the sentence by nominalizing the verb "wi". Use exactly one of these deverbal nouns: "n1,  $\dots ,n_m$  ". Indicate the chosen deverbal noun with \*\*: "w1 w2 ... \*\*wi\*\*... wn"
+
+where  $w_{1} \ldots w_{n}$  is the sentence, and  $w_{i}$  is an inflected form of a lemma in synset  $v$ . The prompt tasks the LLM with indicating the chosen deverbial noun within the sentence (we report some examples of instantiated prompts in Appendix B). To enhance the model's understanding of the task, we provide it with additional inputs as system-level instructions, and a list of manually-crafted nominalization examples for few-shot prompting, to enable in-context learning and coherent output formatting (see Appendix B). The few-shot examples are manually crafted from selected representative verbal samples of SemCor, in order to provide the LLM with a variety of possible nominalization forms and avoid a repetitive structure of the nominalized sentences. Note that, in the case of  $N$  verbal predicates occur
+
+ring in the same sentence, we create  $N$  different prompts, each revolving on a different predicate to nominalize.
+
+Second, each LLM-generated sentence is then automatically validated through the following heuristics to ensure reliability, automatically checking that: (i) a deverbal noun is clearly indicated in the output using the formatting requested in the prompt and outlined in the few-shot examples; (ii) the sentence and the chosen deverbal noun are POS-tagged to make sure that the chosen deverbal noun is indeed a noun; (iii) the lemmatized form  $n_j$  of the chosen deverbal noun is one of the candidates provided, i.e.  $n_j \in s$ . If all checks are successfully passed, the sentence is considered valid and we tag the nominal predicate occurrence in the generated sentence with frame(s). We then proceed to the role propagation step.
+
+During the predicate nominalization, different LLMs and prompt combinations were tested, including ChatGPT models (OpenAI, 2024) and the Gemini-Pro model (Google, 2024) (see Appendix A for a comprehensive list and individual results). In order to finalize the prompt, we evaluated the results quantitatively using the aforementioned validation pipeline, but also qualitatively by manual inspection, over a representative subset of samples, using criteria such as grammatical correctness, adherence to the original sentence, and correct usage of the nominal predicate. These tests highlighted the need to craft a prompt that guides the LLM toward the desired result, and this was the reason for our decision to include the list of admissible deverbals nouns, few-shot examples, and clear, standardized output formatting.
+
+# 4.3 Verbal-to-Nominal Role Propagation
+
+The last step is to label the roles in each sentence. Recall that, by construction,  $\text{frame}(s) = \text{frame}(v)$ , i.e. the frame associated with the nominal predicate  $n_j$  is the same as that of its verbal counterpart (cf. Section 3.1.2).
+
+To label the roles of the target nominal predicate, we carry out the following steps using both the original sentence  $\sigma_v$  used as input for the LLM,
+
+<table><tr><td rowspan="2">Training data</td><td colspan="3">Overall</td><td colspan="3">Predicates</td><td colspan="3">Roles</td></tr><tr><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>V</td><td>62.8</td><td>54.3</td><td>58.2</td><td>13.5</td><td>57.3</td><td>21.8</td><td>66.6</td><td>56.7</td><td>61.3</td></tr><tr><td>N</td><td>71.6</td><td>72.1</td><td>71.9</td><td>98.3</td><td>97.9</td><td>98.1</td><td>75.5</td><td>76.0</td><td>75.7</td></tr><tr><td>N+V</td><td>72.4</td><td>72.4</td><td>72.4</td><td>77.7</td><td>94.0</td><td>85.0</td><td>76.4</td><td>76.4</td><td>76.4</td></tr></table>
+
+Table 3: Precision, Recall and F1 results of the SRL baselines on our nominal dataset. We report the performance of our baselines trained on our nominal dataset (N), on OntoNotes (V), and on the combination of the two  $(\mathrm{V} + \mathrm{N})$
+
+and the corresponding output sentence  $\sigma_{n}$  in which the predicate is nominalized.
+
+First,  $\sigma_v$  is processed using a state-of-the-art SRL system, i.e. InVeRo-XL (Conia et al., 2021b), obtaining automatically-annotated span-based roles for the target verbal predicate  $frame(v)$ .
+
+Second, we use a bi-encoder architecture to encode  $\sigma_v$  and  $\sigma_n$ , and compute the following steps for each role  $r$  in  $\sigma_v$ :
+
+1. We compute the span embedding  $E_r$  of role  $r$  in  $\sigma_v$  by mean pooling and L2 normalization of the embeddings of all the tokens in the span.  
+2. We slide a window of the same size as the role span over  $\sigma_{n}$  by one word until the end of the sentence. For each resulting span  $sp$ , we compute the span embedding  $E_{sp}$  as above.  
+3. We select the span  $sp$  in  $\sigma_{n}$  that maximizes the cosine similarity between  $E_{r}$  and  $E_{sp}$ .  
+4. We propagate the role annotation to the selected span in  $\sigma_{n}$
+
+The result of this process is a large automatically annotated SRL dataset, with nominal predicates identified and tagged using VerbAtlas/NounAtlas frames, and predicate arguments tagged with the corresponding roles.
+
+# 4.4 Results
+
+Statistics. Among its sentences, SemCor contains 36295 verbal predicate occurrences, each of which is used to prompt the LLM to generate a corresponding nominal version. Of these, 29221 passed the validation step, resulting in the same number of valid occurrences of nominal predicates. These latter are then passed into the role propagation step, obtaining 28055 sentences annotated with nominal frames and their roles. The drop in
+
+![](images/f5331e31c54db0fea3f937ad27d134be16d262715c2f998523a5aec5326fdcd4.jpg)  
+Figure 4: Distribution of sentences with nominal predicate across frames (the y-axis indicates the number of sentences whose predicate belongs to the same frame).
+
+the number of sentences in the role propagation step occurs because we consider as good sentences only the ones that have all the roles from the verbal sentence also present in the nominal counterpart. If even a single role is missing, then the sentence is discarded. The distribution of the latter sentences across frames is reported in Figure 4. As expected, a few frames have a considerable number of sentences, with these numbers decreasing exponentially. However, a reasonable number of sentences is also maintained in the long tail (e.g., 32 sentences at the median rank).
+
+Dataset quality assessment. We set aside 500 sentences and tasked a human expert to annotate those sentences according to the following guidelines: for each sentence the expert was asked to (i) correct the sentence if it was found to be ungrammatical or nonsensical, (ii) double-check the frame assigned to the nominal predicate and correct it if needed, and (iii) correct each argument span and (iv) the corresponding role label. The annotator was specifically recruited for this task. We paid them in accordance with the standard salary of their geographical location.
+
+<table><tr><td rowspan="2">Training data</td><td colspan="3">Overall</td><td colspan="3">Predicates</td><td colspan="3">Roles</td></tr><tr><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>V</td><td>89.3</td><td>89.8</td><td>89.6</td><td>93.2</td><td>95.7</td><td>94.4</td><td>88.6</td><td>89.0</td><td>88.8</td></tr><tr><td>N+V</td><td>89.3</td><td>89.6</td><td>89.5</td><td>92.8</td><td>94.4</td><td>93.6</td><td>88.5</td><td>88.9</td><td>88.7</td></tr></table>
+
+Table 4: Precision, Recall and F1 results of the SRL baselines on OntoNotes. We report the performance of our baselines trained on OntoNotes (V), and on the combination of our nominal dataset with OntoNotes  $(\mathrm{V} + \mathrm{N})$
+
+An example of a manual annotation carried out by the expert is:
+
+Original sentence: [The girl]Agent crawled out into the renewing warmth of the sunshine [hugging]FIT [her shoulders]Theme.
+
+Generated sentence with nominal predicate: [The girl]Agent crawled out into the renewing warmth of the sunshine, performing an [embrace]FIT of [her shoulders]Theme.
+
+Corrected sentence: [The girl]Agent crawled out into the renewing warmth of the sunshine, performing an [embrace]FIT [of her shoulders]Theme.
+
+In this example, the automatic pipeline made a mistake in capturing the span of the Theme, neglecting to include the preposition "of".
+
+To determine the quality of the automatically annotated nominal SRL dataset, we computed the percentage of i) grammatical sentences (92.11%), ii) unchanged frame assignments (95.56%), iii) unchanged role spans (77.11%), iv) unchanged role labels (95.27%). These results show the robustness of our approach.
+
+# 4.5 Baselines for Nominal SRL
+
+We used our automatically annotated dataset to train and test a state-of-the-art SRL model in order to provide a baseline for the nominal task. We removed from our dataset the 500 sentences annotated for dataset quality assessment, and used them as our test set. We then split our silver-quality dataset into  $90\%$  training and  $10\%$  validation.
+
+We selected the state-of-the-art SRL model introduced by Conia and Navigli (2020) as the backbone model for our baselines, closely following the training procedure outlined by the authors. We fine-tuned this model on three different datasets: (a) OntoNotes 5.0 (Weischedel et al., 2010),<sup>4</sup> which serves as the standard dataset for verbal SRL, (b)
+
+our silver dataset for nominal SRL, (c) the concatenation of the two datasets. Because OntoNotes sentences are annotated with PropBank predicates, we use the PropBank-to-VerbAtlas mappings made available by Conia and Navigli (2020)<sup>5</sup> to convert the annotations.
+
+Results. In Table 3, we report the performance of the aforementioned baselines on our manually-curated test set. We note that a state-of-the-art SRL approach, trained on verbal data (V), only moderately generalizes to the nominal setting, thereby confirming the findings of Orlando et al. (2023). Conversely, training the same model on our nominal SRL dataset (N) leads to a significant improvement in performance (21% on average). Interestingly, the baseline trained on the union of verbal and nominal data  $(\mathrm{V} + \mathrm{N})$  achieves state-of-the-art performance on both OntoNotes (Table 4) and our gold nominal test set (Table 3).
+
+This outcome paves the way for the development of a competitive unified system performing both nominal and verbal SRL.
+
+# 5 Conclusions
+
+With our work, we fill the gap in nominal SRL by introducing NounAtlas, a large-scale predicate inventory for this task. NounAtlas is constructed by linking nominal predicates from WordNet to VerbAtlas frames through a semi-automatic approach. We additionally create a large dataset for nominal SRL with the help of LLMs, annotated through label propagation with nominal predicates and semantic roles from NounAtlas. Finally, we benchmark multiple baselines consisting of state-of-the-art SRL approaches, trained and evaluated on our dataset. Our experiments demonstrate that a unified SRL approach, trained on both nominal and verbal data, achieves state-of-the-art performance in both settings. We release our code and resource at https://github.com/SapienzaNLP/nounatlas.
+
+# Limitations
+
+While this study provides a valuable starting point for nominal SRL, additional steps could be taken to further enhance its impact. First, our approach utilizes WordNet synsets as predicates, which makes multilingual support feasible through BabelNet (Navigli and Ponzetto, 2010). Indeed each WordNet synset corresponds to a BabelNet synset, which provides translations into tens of other languages. However, lexical coverage varies depending on language (see https://babelnet.org/statistics). Generating a multilingual nominal SRL dataset is more challenging, as one should either use available multilingual corpora annotated with BabelNet, with varying coverage (Pasini et al., 2021), or translate SemCor into other languages and propagate annotations. We leave this to future work.
+
+Additionally, our study focused on nominal arguments, neglecting the exploration of adjectival and adverbial SRL. Furthermore, although the inventory underwent a final step of manual annotation, the training set remains automatically generated. A manual annotation of the entire dataset is expected to bring improved performance on the task. Addressing these limitations in future studies would further advance SRL into a full-fledged NLU task, offering deeper insights into the structured analysis of text.
+
+# Acknowledgements
+
+![](images/dcdd8f8229d0c9c20bfb8302791115e852492aeed2ee577b6d877cba3bada789.jpg)
+
+The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 under the European Union's Horizon 2020 research.
+
+![](images/eb9f74870c7c843844320145fb5938ed8ad151bd89598f1a54762823378a9b99.jpg)
+
+We gratefully acknowledge the support of the PNRR MUR project PE0000013-FAIR. Alessandro Scirè conducted this work during his enrollment in the Italian National Doctorate on Artificial Intelligence at Sapienza University of Rome. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Dennis Rotondi.
+
+# References
+
+Olga Babko-Malaya, Martha Palmer, Nianwen Xue, Aravind Joshi, and Seth Kulick. 2004. Proposition Bank II: Delving deeper. In Proceedings of the Workshop Frontiers in Corpus Annotation at HLT-NAACL 2004,
+
+pages 17-23, Boston, Massachusetts, USA. Association for Computational Linguistics.  
+Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998. The berkeley framenet project. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1, ACL '98/COLING '98, page 86-90, USA. Association for Computational Linguistics.  
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+Long Chen, Zhihong Jiang, Jun Xiao, and Wei Liu. 2021. Human-like controllable image captioning with verb-specific semantic roles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16846-16856.  
+Simone Conia, Andrea Bacciu, and Roberto Navigli. 2021a. Unifying cross-lingual semantic role labeling with heterogeneous linguistic resources. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 338-351, Online. Association for Computational Linguistics.  
+Simone Conia, Edoardo Barba, Alessandro Scirè, and Roberto Navigli. 2022. Semantic role labeling meets definition modeling: Using natural language to describe predicate-argument structures. In *Findings of the Association for Computational Linguistics: EMNLP* 2022, pages 4253–4270, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.  
+Simone Conia and Roberto Navigli. 2020. Bridging the gap in multilingual Semantic Role Labeling: A language-agnostic approach. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, pages 1396-1410, Barcelona, Spain (Online). International Committee on Computational Linguistics.  
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+Simone Conia, Riccardo Orlando, Fabrizio Brignone, Francesco Ceconi, and Roberto Navigli. 2021b. InVeRo-XL: Making cross-lingual Semantic Role Labeling accessible with intelligible verbs and roles. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 319-328, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
+
+Angel Daza and Anette Frank. 2020. X-SRL: A parallel cross-lingual semantic role labeling dataset. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3904–3914, Online. Association for Computational Linguistics.  
+Andrea Di Fabio, Simone Conia, and Roberto Navigli. 2019. VerbAtlas: a novel large-scale verbal semantic resource and its application to semantic role labeling. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 627-637, Hong Kong, China. Association for Computational Linguistics.  
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+Google. 2024. Gemini: A family of highly capable multimodal models.  
+Jan Hajič, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antônia Martí, Lluis Márquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Štepánek, Pavel Stranák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. 2009. The CoNLL-2009 shared task: Syntactic and semantic dependencies in multiple languages. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task, pages 1-18, Boulder, Colorado. Association for Computational Linguistics.  
+Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, and Iryna Gurevych. 2017. Out-of-domain FrameNet semantic role labeling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 471-482, Valencia, Spain. Association for Computational Linguistics.  
+Ishan Jindal, Alexandre Rademaker, Michal Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, and Yunyao Li. 2022. Universal Proposition Bank 2.0. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1700-1711, Marseille, France. European Language Resources Association.  
+Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, and Ido Dagan. 2020. QANom: Question-answer driven SRL for nominalizations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3069-3083, Barcelona, Spain (Online). International Committee on Computational Linguistics.
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+Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330.  
+Adam Meyers, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young, and Ralph Grishman. 2004. The NomBank project: An interim report. In Proceedings of the Workshop Frontiers in Corpus Annotation at HLT-NAACL 2004, pages 24-31, Boston, Massachusetts, USA. Association for Computational Linguistics.  
+George A. Miller. 1995. Wordnet: a lexical database for english. Commun. ACM, 38(11):39-41.  
+George A. Miller, Claudia Leacock, Randee Tengi, and Ross T. Bunker. 1993. A semantic concordance. In Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993.  
+Roberto Navigli and Simone Paolo Ponzetto. 2010. BabelNet: Building a very large multilingual semantic network. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 216-225, Uppsala, Sweden. Association for Computational Linguistics.  
+OpenAI. 2024. Gpt-4 technical report.  
+Riccardo Orlando, Simone Conia, and Roberto Navigli. 2023. Exploring non-verbal predicates in semantic role labeling: Challenges and opportunities. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12378-12388, Toronto, Canada. Association for Computational Linguistics.  
+Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31(1):71-106.  
+Tommaso Pasini, Alessandro Raganato, and Roberto Navigli. 2021. Xl-wsd: An extra-large and cross-lingual evaluation framework for word sense disambiguation. In AAAI Conference on Artificial Intelligence.  
+Xiangyu Peng, Kaige Xie, Amal Alabdulkarim, Harshith Kayam, Samihan Dani, and Mark O. Riedl. 2022. Guiding neural story generation with reader models.  
+Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.  
+Arka Sadhu, Tanmay Gupta, Mark Yatskar, Ram Nevata, and Aniruddha Kembhavi. 2021. Visual semantic role labeling for video understanding. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5585-5596.
+
+K.K. Schuler. 2005. Verbnet: A Broad-coverage, Comprehensive Verb Lexicon. University of Pennsylvania.  
+Dan Shen and Mirella Lapata. 2007. Using semantic roles to improve question answering. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 12-21, Prague, Czech Republic. Association for Computational Linguistics.  
+Peng Shi and Jimmy Lin. 2019. Simple bert models for relation extraction and semantic role labeling.  
+Linfeng Song, Daniel Gildea, Yue Zhang, Zhiguo Wang, and Jinsong Su. 2019. Semantic Neural Machine Translation Using AMR. Transactions of the Association for Computational Linguistics, 7:19-31.  
+Diana Trandabat. 2011. Using semantic roles to improve summaries. In Proceedings of the 13th European Workshop on Natural Language Generation, pages 164-169, Nancy, France. Association for Computational Linguistics.  
+Rocco Tripodi, Simone Conia, and Roberto Navigli. 2021. UniteD-SRL: A unified dataset for span- and dependency-based multilingual and cross-lingual Semantic Role Labeling. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2293-2305, Punta Cana, Dominican Republic. Association for Computational Linguistics.  
+Ralph Weischedel, Mitch Marcus, Martha Palmer, Edward Hovy, Robert Belvin, Sameer Pradhan, and Lance Ramshaw. 2010. Ontonotes: A large training corpus for enhanced processing. In Handbook of Natural Language Processing and Machine Translation. Springer, New York, NY.  
+Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Huggingface's transformers: State-of-the-art natural language processing.  
+Yanpeng Zhao and Ivan Titov. 2020. Unsupervised transfer of semantic role models from verbal to nominal domain.  
+A LLM Tests for Predicate Nominalization  
+We tested several LLM and prompt combinations for the task of nominalizing a verbal predicate. The tested LLMs are: OpenAI's ChatGPT models (gpt-3.5-turbo-1106, gpt-3.5-turbo-instruct, text-davinci-003) and Google's Gemini-Pro model. We
+
+evaluated them both quantitatively, using the validation pipeline described in Section 4.2, and qualitatively by manual inspection.  
+Our initial prompt strategy left ample freedom for the LLM to choose the deverbal noun, since it had only the verbal predicate to convert as input. This approach provided sentences in which the nominal predicate was not easily identifiable, preventing its linkage to an existing frame. Also, the model struggled in adhering to the desired format. Adding explicit instructions to keep the formatting consistent helped, but did not completely solve, this issue. The strategy of adding examples was more effective for the purpose of keeping the format consistent. To facilitate the identification of the resulting deverbal noun, a request to mark it in the output was added to the prompt. This solved any issue concerning the parsing of the output. At this stage, one problem still persisted: in some cases, the deverbal nouns produced by the LLM were impossible to link to an existing frame. To solve this issue, we included in the prompt a list of candidate deverbal which were directly linkable to a frame to choose from. Following this process, the resulting sentences were: i) adherent to the required format, ii) indicating and using a correct nominal predicate, i.e. a predicate linked via DRF to the verbal one, and iii) as similar as possible to the original one while preserving grammar and logic adequacy. As far as the selection of the LLM was concerned, we observed that gpt-3.5-instruct followed the instructions provided but it would often generate grammatically incorrect sentences when forced to use a specific deverbal noun. Instead, gpt-3.5-turbo and Gemini-pro produced the most natural sounding sentences. Gemini-pro was finally chosen based on a manual inspection of the results.  
+The validation results on a subset of 150 SemCor sentences converted using the selected prompt are shown in Table 5.
+
+# B Prompts and Examples for LLMs
+
+Some examples of instantiated prompts for the LLM are reported below:
+
+Change the sentence by nominalizing the verb "produced" indicated by \*\*. Use exactly one of these deverbal nouns: "production". Indicate the chosen deverbal noun with \*\*: "The Fulton County Grand Jury said Friday an investigation of
+
+Atlanta's recent primary election \*\*produced\*\* " no evidence " that any irregularities took place .
+
+Change the sentence by nominalizing the verb "charged" indicated by \*\*. Use exactly one of these deverbal nouns: "charge", "mission", "commission", "direction". Indicate the chosen deverbal noun with \*\*: "The September October term jury had been \*\*charged\*\* by Fulton Superior Court Judge Durwood Pye to investigate reports of possible "irregularities" in the hard-fought primary which was won by Mayor-nominate Ivan Allen Jr. .
+
+The system directive used for the LLM is:
+
+You are a linguist that can nominalize sentences. The sentences must be grammatically correct.
+
+Finally, we report two of the 10 examples used for few-shot prompting. The examples are shuffled at each prompt request and mimic the structure of a realistic User-LLM chat-like conversation:
+
+User: Change the sentence by nominalizing the verb "absorb" indicated by \*\*. Use exactly one of these deverbual nouns: "soaking up", "absorption". Indicate the chosen deverbual noun with \*\*: "The kidneys \*\*absorb\*\* sodium as a part of renal physiology .
+
+LLM: The  $^{**}$ absorption\*\* of sodium by the kidneys is a part of renal physiology.
+
+User: Change the sentence by nominalizing the verb "mention" indicated by **. Use exactly one of these deverbal nouns: "citation". Indicate the chosen deverbal noun with **: "Vandiver likely will **mention** the $ 100 million highway bond issue approved earlier in the session as his first priority item."
+
+LLM: Vandiver's  $^{**}$ citation\*\* of the $100 million highway bond issue approved earlier in the session will likely be his first priority item.
+
+# C Guidelines for the human evaluation
+
+In this Section, we report the instructions provided to the annotator about the task of classifying the set of nominal synsets with non-existing links (cf. Section 3.1.2) into frames.
+
+<table><tr><td>LLM model</td><td>valid samples</td></tr><tr><td>GPT-3.5-turbo-1106</td><td>132/150 (88%)</td></tr><tr><td>GPT-3.5-turbo-instruct</td><td>130/150 (86.6%)</td></tr><tr><td>text-davinci-003</td><td>102/150 (68%)</td></tr><tr><td>Gemini-Pro</td><td>136/150 (90.6%)</td></tr></table>
+
+Table 5: Number of samples passing validation across LLMs
+
+We provided two Excel files.
+
+# The first file:
+
+- Contains the nominal synsets requiring classification.  
+- For each synset, presents five frames automatically selected by a machine learning model.  
+- Annotators must choose the single most fitting frame from these five options for each synset.
+
+# The second file:
+
+- Lists all 425 available frames within VerbAtlas along with their respective definitions.  
+- For each frame, lists all the included verbal synsets and their respective definitions.  
+- Serves as a reference for exploring frames beyond the five presented in the first file for specific synsets.
+
+The classification process has to follow these steps for each synset:
+
+1. Review the synset itself (column A), its lemmas (column B), and its definition (column C).  
+2. Examine the five proposed frames (column B) with their semantic definitions (column C). Additionally, consider the definitions explaining how synsets within the frame relate to its specific roles. For example, the frame "LOSE" might have the definition "An agent LOSES a theme to a recipient".
+
+While selecting the appropriate frame, possible cases arise:
+
+1. Suitable frame in the 5 proposed: Place the "x" in the corresponding row.  
+2. No suitable frame among the 5 proposed:
+
+- Consult the second file to find an appropriate frame among the 425 options.
+
+- Replace the name of a proposed frame with your chosen frame from the second file (keep the original definition).  
+- Place an "x" in the corresponding row.
+
+3. Synset belongs to multiple frames: Place an "x" in each relevant frame row (Minimize this occurrence; use it only for true ambiguity where a single choice is impossible. Add a note in column D explaining the ambiguity).  
+4. No existing frame seems appropriate: Don't mark any frames.  
+5. Synset is not a nominal predicate: Don't mark any frames. This is uncommon; attempt classification even for broader event concepts.

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+# OceanGPT: A Large Language Model for Ocean Science Tasks
+
+Zhen Bi $^{1,2,5,6}$ , Ningyu Zhang $^{1,2,5*}$ , Yida Xue $^{1}$ , Yixin Ou $^{1}$ , Daxiong Ji $^{2,3}$ , Guozhou Zheng $^{2,4}$ , Huajun Chen $^{1,2*}$
+
+$^{1}$ College Computer Science and Technology, Zhejiang University  $^{2}$ Donghai Laboratory  $^{3}$ Ocean College, Zhejiang University  $^{4}$ Zhoushan-Zhejiang University Ocean Research Center  $^{5}$ School of Software Technology, Zhejiang University,  $^{6}$ Huzhou University {bizhen_zju, zhangningyu, ouyixin, jidaxiong, guozhou, huajunsir}@zju.edu.cn Project Website: https://oceangpt.zjukg.cn/
+
+# Abstract
+
+Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over  $70\%$  of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reasons are the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OCEANGPT, the first-ever large language model in the ocean domain, which is expert in various ocean science tasks. We also propose DOINSTRUCT, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OCEANBENCH, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OCEANGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.
+
+# 1 Introduction
+
+Ocean science, which delves into the intricacies of oceans that cover over  $70\%$  of our planet's surface, is essential not only for understanding the rich reservoirs of life and biodiversity but also for recognizing their pivotal role in regulating the global climate and supporting economies (Esaias et al., 1998; Falkowski, 2012; Visbeck, 2018; Jin et al., 2023). Recently, advances in Large Language Models (LLMs) (OpenAI, 2023; Jiang et al., 2023; Zha et al., 2023; Yin et al., 2023; Zhao et al., 2023) have
+
+![](images/b1aeb9e60d32052dbfece234f4d0f9f905d9dc57e11901cdc76b9c1169f6fe72.jpg)  
+Figure 1: Capabilities of OCEANGPT. Our proposed model not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.
+
+transformed the paradigm in science domains such as medical science (Moor et al., 2023), molecular science (Fang et al., 2023), protein science (Lin et al., 2023) and geoscience (Deng et al., 2023). However, the potential for the large language model in ocean science is under-explored.
+
+Despite remarkable success in general domain, current LLMs still do not fully meet the specific demand of oceanographers. This inadequacy is primarily due to: (1) The immense volume and intricate nature of ocean data. As ocean science research progresses, acquiring data becomes increasingly challenging, which makes enhancing the oceanic understanding both a golden opportunity and a significant hurdle. (2) The necessity for higher granularity and richness in knowledge. Note that the data requirements faced by researchers are becoming increasingly intricate and diverse. Ocean science encompasses various domains and subjects, each with its distinct data attributes and patterns.
+
+To alleviate these issues, we introduce
+
+OCEANGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. Specifically, we propose DOINSTRUCT, an efficient ocean science instruction generation framework that capitalizes on multi-agent collaboration. Each agent in our designed framework is considered as an expert in a specific domain (science and research, resources and development, ecology and environment etc.) and is responsible for generating the corresponding data. For the advancement of ocean science research using LLMs, we also create a benchmark called OCEANBENCH to evaluate the capabilities in ocean science tasks.
+
+Through extensive experiments, OCEANGPT shows superiority for diverse ocean science tasks. Note that our benchmark data is based on criteria manually evaluated by ocean experts, and can accurately reflect the capabilities that LLMs possess in the field of ocean science. As depicted in Figure 1, our model can comprehensively answer questions according to the instructions of oceanographers, which demonstrates its expertise in oceanography. We further explore the potential of OCEANGPT from the perspectives of ocean engineering. Specifically, we integrate ocean robotics instructions into the training data and evaluate its ability via code or console commands. OCEANGPT not only demonstrates a higher level of knowledge expertise but also gains preliminary embodied intelligence capabilities in ocean technology.
+
+Our contributions can be summarized as follows:
+
+- We introduce OCEANGPT, the first ocean LLM, which shows superiority for various ocean science tasks. It can answer oceanographic questions according to the instructions of oceanographers, demonstrating expertise in oceanography.  
+- We propose DOINSTRUCT, an automated domain instruction evolving framework that constructs the ocean instruction dataset by multiagent collaboration. Our framework effectively alleviates the difficulty of obtaining ocean domain data.  
+- Extensive experiments demonstrate the superiority of OCEANGPT in the OCEANBENCH. OCEANGPT not only demonstrates a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities.
+
+# 2 Related Work
+
+Large Language Models. The landscape of LLM (Brown et al., 2020; Chowdhery et al., 2022; Touvron et al., 2023a,b) has rapidly evolved and achieved a series breakthroughs. Rae et al. (2021); Zhang et al. (2022); Thoppilan et al. (2022); Scao et al. (2022); Zeng et al. (2023) have explored the performance across a wide range of model scales and broadened the application scope. (Qiao et al., 2023a; Zhang et al., 2023a; Qiao et al., 2023b; Wang et al., 2023a; Xi et al., 2023). Retrieval-Augmented Generation (RAG) is a useful solution by incorporating knowledge from external databases (Gao et al., 2023; Lewis et al., 2020; Schick et al., 2023; Khandelwal et al., 2020). To align LLMs, instruction tuning (Wei et al., 2022; Zhang et al., 2023b; Ouyang et al., 2022; Taori et al., 2023; Wang et al., 2023d; Chiang et al., 2023; Xu et al., 2023) is a crucial technique to alignment with user preferences and desired outputs. Different from those, we train a totally new ocean science large language model and introduce an effective domain instruction generation framework via multi-agent collaboration.
+
+Science Large Language Models. LLMs have emerged as cornerstone models in addressing challenges within scientific research. Singhal et al. (2022) explores the potential of clinical LLMs and introduces a human evaluation framework and instruction prompt tuning. Moor et al. (2023) proposes generalist medical AI that is capable of handling diverse medical tasks using self-supervised learning on large datasets. Kraljevic et al. (2021) introduces MedGPT, a model using EHR data and Named Entity Recognition tools for predicting future medical events. BioGPT (Luo et al., 2022) is a language model pre-trained on biomedical literature for improved text generation and mining. Theodoris et al. (2023) describes Geneformer, a model pre-trained on single-cell transcriptomes for making predictions with limited data in network biology. Lin et al. (2023) demonstrates the prediction of atomic-level protein structure from primary sequences using scaled-up language models. Deng et al. (2023) introduces the first LLM specifically designed for geoscience, including its training and benchmarking protocols. Chen et al. (2023) presents tele-knowledge pre-training for fault analysis. Different from previous works, we design the first large language model for ocean science tasks and explore its potential for ocean research.
+
+# 3 OCEANGPT
+
+To obtain OCEANGPT, we firstly construct the training corpus for ocean science and pre-train an ocean LLM based on LLaMA-2 (Touvron et al., 2023b) in Section 3.1. Then we propose DOINSTRUCT, an automated framework for domain instruction generation to build an ocean domain-specific instruction dataset. Our framework leverages multi-agent collaboration and utilizes ocean literature to automatically generate a large volume of domain-specific instructions for ocean science tasks (Section 3.2). The overview training procedure of our OCEANGPT is shown in Figure 2.
+
+# 3.1 Pre-training Stage
+
+![](images/0c3bf81c42b58a8d2f52f938cb9f982cf3d73dd59fa3262d44c74812bac5fabd.jpg)  
+Figure 2: Overall framework of OCEANGPT.
+
+To pre-train the foundation model for ocean science tasks, it is essential to construct the pretraining corpus specific to ocean science. Therefore, we firstly collect a raw corpus of 67,633 documents from open-access literature. For the specific volumes we choose, we prefer to consider publications from recent years to ensure the inclusion of the latest research and developments. At the same time, we select some historically significant literature to help the LLM understand the developmental history of the field. For diversity, we choose articles from different sources to ensure coverage of various research perspectives and methods. Specifically, we utilize the Python package pdfminer to convert the content of literature files into plain text. To ensure the quality and consistency of the data, further processing of the collected dataset is necessary. We apply regular expressions to filter out figures, tables, headers, footers, page numbers, URLs and references. Additionally, any extra spaces, line breaks, and other non-text characters are removed. The processed documents cover various aspects of ocean science such as ocean physics, ocean chemistry, ocean biology, geology, hydrology, etc. It is important to note that special characters, emoticons, and garbled characters are also replaced or eliminated during this process. We also employ hash-based methods to de-duplicate the data, which helps reduce the risk of over-fitting
+
+during pre-training and enhances its generalization capability.
+
+# 3.2 Domain Instruction Data Generation
+
+As ocean science research deepens, researchers are facing increasingly complex and diversified data demands. Ocean science corpus contains multiple fields and topics, and each topic has its unique data characteristics and patterns. To effectively simulate and obtain those data, we propose a domain instruction generation framework DOINSTRUCT to obtain ocean instructions  $H$  by multi-agent collaboration. Each agent is considered as an expert in a specific domain (topic) and is responsible for generating the corresponding data. It not only ensures the professionalism and accuracy of the data but also allows for the parallel and efficient generation of a large amount of data. Note that the proposed framework also has greater flexibility, allowing us to independently optimize and adapt to different science domains (e.g., astronomy).
+
+Ocean Topic Definition. To provide researchers with a clear and organized resources, we manually categorize the data in ocean science into five major ocean topics, which are based on the expertise of experts in oceanography. The definitions of these five topics comprehensively cover all the main areas of ocean science and are relatively independent. The detailed explanation for the five major topics is described as follows:
+
+- Science and research focuses on the fundamental scientific theories and research related to the ocean, such as ocean currents, sea temperatures and ocean biodiversity. This portion of data separately helps drive the advancement of pure scientific research and theories.  
+- Resources and development includes fisheries, minerals, oil and gas, as well as other sustainable development resources. It is set for a better examination and planning of the rational development of ocean resources.  
+- Ecology and environment. Environmental protection and ecological sustainability are currently global hot topics. It helps to address issues such as ocean pollution, ecological degradation, and the impact of climate change on the oceans in a more focused manner.  
+- Technology and engineering encompasses aspects ranging from ocean measurements, ob
+
+![](images/dfe042a50f8bd11ecd4acd8c7bf0ee32fb542a8cc46d8d89eba1c3b8462c86ed.jpg)  
+Figure 3: Procedure of our proposed DOINSTRUCT. We use agents (gpt-3.5-turbo) as experts for each ocean topic and make them rapidly expand the instructions by collaboration. In this framework, we design three agent roles: evolving generator, fine-tuned literature extractor and inspector with rule constraints.
+
+servational equipment, and ship engineering to ocean energy development. Such categorization aids in a more focused exploration of ocean engineering and technological needs, while also facilitating interdisciplinary research with other engineering disciplines.
+
+- Life, culture and others. The ocean is not only a natural resource or a subject of scientific research; it is also an integral part of culture and lifestyle. This category consists of aspects ranging from history and culture to the mutual influences between the ocean and human societal activities, such as tourism, leisure.
+
+While these five topics are distinct, there might be some overlap as well. For instance, some issues related to ocean environmental protection might also be associated with the technology of ocean engineering. For the sake of convenience in data analysis, in the actual construction of the dataset, we map each sample to the most relevant category.
+
+Agents as Domain (Ocean) Experts. In Figure 3, we use agents as domain experts for each ocean topic and make them rapidly expand the instructions by collaboration. We collect the seed instruction data and propose three strategies by using multiple agents acting as experts.
+
+To construct the seed dataset, we employ dozens of annotators with rich backgrounds in marine science. Each annotator is responsible for several
+
+topics and they first manually write some representative example for each marine topic. Then we use LLMs to mimic the existing data to generate a large number of similar samples. All samples are ultimately manually checked by the annotators.
+
+The entire process is very time-consuming, with all the experts spending a total of four days to validate the seed data. The final seed instruction dataset includes 5 major categories, over 500 sub-categories and a total of more than 10,000 data samples.
+
+- Evolving Agent as the Generator. We design an evolving approach that selects samples from the seed dataset and simultaneously calls upon two agents (gpt-3.5-turbo) to evolve the selected samples. The evolution procedure includes two aspects: (1) we enrich the content of the sample by having the agent automatically add relevant background knowledge to it; (2) we guide the agent to refine the sample by conducting a more in-depth analysis of specific concepts or entities. Through multiple rounds of iterative execution, our method can rapidly expand the existing seed dataset, which allows for the rapid expansion of both the breadth and depth of information.  
+- Fine-Tuned Agent as the Literature Extractor. As shown in Figure 3, we collect a smaller expert-annotated corpus and use the BM25 to retrieve high quality sentences in a larger ocean corpus. We regard the retrieved texts
+
+Algorithm 1 Domain Instruction Data Generation  
+Require: Seed dataset  $S$  with format (inst, output), Ocean literature corpus  $O$ , Pre-defined rules  $R$  for filtering  
+Ensure: High-quality instruction dataset  $H$   
+1: Initialize empty datasets.  
+2: Step1Data =  $\emptyset$ , Step2Data =  $\emptyset$ ,  $H = \emptyset$   
+3:  
+    ▷ Agent Collaboration as Domain Experts.  
+4: for each sample in  $S$  do  
+5: inst, output ← sample  
+6: enriched_sample ← Enrich.inst, output)  
+7: refined_sample ← Refine.inst, output)  
+8: Step1Data ← Step1Data ∪ enriched_sample ∪ refined_sample  
+9: end for  
+10:  
+    ▷ Fine-Tuned Agent as Literature Extractor.  
+11: RetrievedTexts ← BM25_Retrieve(O)  
+12: Model M ← FineTune(S_reverse)  
+13: for each document in RetrievedTexts do  
+14: output ← document(content)  
+15: inst ← M(output)  
+16: Step2Data ← Step2Data ∪ (inst, output)  
+17: end for  
+18:  
+    ▷ Agent as Inspector with Rule Constraints.  
+19: MergedData ← Inspector(Step1Data, Step2Data, R)  
+20:  
+    ▷ Quality Control by Debating.  
+21: for each sample in MergedData do  
+22: inst, output ← sample  
+23: debate_result ← Debate.inst, output)  
+24: if debate_result is high-quality then  
+25: H ← H ∪ sample  
+26: end if  
+27: end for  
+28: return H
+
+as high-quality candidate samples. Meanwhile, we fine-tune gpt-3.5-turbo with the seed instruction dataset, regarding the finetuned agent as the literature extractor. In other words, it can automatically extract instructions (inst) from the unannotated ocean science corpus (output). Therefore, we utilize the agent to automatically build pairs of (inst, output) on external ocean science literature.
+
+- Agent as the Inspector with Rule Constraints. For the massively generated instructions, we use the pre-defined rules as constraints and perform filtering on the data. These rules include syntactic and semantic constraints as well as basic definitions in the ocean domain. We describe these rules using natural language because many constraints and norms related to ocean science cannot be directly represented with expressions. Therefore, we provide prompts to the gpt-3.5-turbo API as demonstrations, letting it play the role of an
+
+inspector. Our method ensures that the generated ocean instruction data is of higher quality. Detailed prompt is shown in Table 5.
+
+Finally, we assign two extra gpt-3.5-turbo agents as roles to debate the quality of data and obtain high-quality instruction dataset. Our designed framework can rapidly constructing a ocean science dataset using multi-agents, and by incorporating external knowledge from marine literature, it overcomes the limitations inherent to the agents themselves. Our framework can also be effectively applied to the instruction data construction in other scientific domains. It should be noted that we separately synthesize robot instructions to equip OCEANGPT with the capability to interact with the environment. The procedure is in Algorithm 1 and the statistics of dataset is in Figure 4.
+
+![](images/52e7bc3f3fe419a97e99c4c0ff3089933f9fcddd5232deab604706d96f4f0c2a.jpg)  
+Figure 4: Statistics of our final instruction dataset. We use DOINSTRUCT to expand more than 150,000 instructions (data-evolving, data-extracting).
+
+Quality Control for the Datasets. We ask domain experts to carefully review and check data to ensure quality. Specifically, the human volunteers are first trained to make sure they have a comprehensive understanding of the task. Then, we develop a platform that can help experts to randomly sample  $10\%$  instances from the generated instruction dataset. Next, the trained domain experts are asked to validate if there are potential errors in the sampled instances. The final IAA (inter-annotator agreement) score for our dataset is 0.82, which satisfies the research purpose.
+
+# 4 Benchmarking Ocean Science Tasks
+
+We provide detailed explanations of the experimental setup and the baseline models in Section 4.1. In Section 4.1, we construct an ocean-specific benchmark OCEANBENCH to evaluate the capabilities of our OCEANGPT. We describe the automatic and human evaluation in Section 4.1.
+
+# 4.1 Implementation Details and Baselines
+
+For the pre-training stage, we pre-train our OCEANGPT $^2$  based on the LLaMA-2 (Touvron et al., 2023b) for seven days with six A800 Nvidia GPUs. For the instruction-tuning stage, we employ the LoRA method (Hu et al., 2021) to fine-tune the pre-trained model and choose three baseline models for comparison. We use the chat version of LLaMA-2 (Llama-2-7b-chat-hf), which is a generative language model optimized for dialogue use cases. We also use Vicuna-1.5 (Chiang et al., 2023), a chat model which fine-tunes LLaMA-2 on dataset collected from ShareGPT. We further use ChatGLM2-6B, the optimized version of GLM (Zeng et al., 2023). The detailed experimental settings are shown in Table 2 (Appendix A).
+
+OCEANBENCH. To evaluate the capabilities of LLMs for oceanography tasks, we design a benchmark called OCEANBENCH. Our benchmark includes a total of 15 ocean-related tasks such as question-answering, extraction, and description. Our evaluation samples are automatically generated from the seed dataset and have undergone dedduplication  $^{3}$  and manual verification by experts.
+
+For the quality control, we further sample part of data and ask domain experts to evaluate the quality (those disagreed cases or bad cases will be manually fixed by domain experts.). The distribution of our designed OCEANBENCH and the detailed statistics can be found in Table 1 and Figure 11.
+
+<table><tr><td>Task</td><td>Num</td><td>Task</td><td>Num</td></tr><tr><td>Analysis</td><td>674</td><td>Classification</td><td>895</td></tr><tr><td>Judgment</td><td>655</td><td>Letter Writing</td><td>359</td></tr><tr><td>Open-ended Generation</td><td>930</td><td>Extraction</td><td>1,078</td></tr><tr><td>Recommendation</td><td>1,089</td><td>Description</td><td>1,246</td></tr><tr><td>Summary</td><td>149</td><td>Editing</td><td>1,075</td></tr><tr><td>Identification</td><td>464</td><td>Transformation</td><td>401</td></tr><tr><td>Question Answering</td><td>1,230</td><td>Others</td><td>157</td></tr><tr><td>Commonsense Reasoning</td><td>1,024</td><td></td><td></td></tr></table>
+
+Table 1: The detailed statistics of OCEANBENCH.
+
+Metrics. For the task-level calculation, we compare the effectiveness of two models for each task. When one model performs better on the majority
+
+2We release the 7B version at https://huggingface.co/zjunlp/OceanGPT-7B-v0.1 for evaluation, and further release the 2B (MiniCPM-2B-sft-bf16) and 8B (Meta-Llama-3-8B-Instruct) version at https://huggingface.co/collections/zjunlp/ oceangpt-664cc106358fdd9f09aa5157.
+
+3We also perform dedduplication between the benchmark and our training dataset to avoid the data leakage in the training stage of OceanGPT. The detailed explanation about the similarity calculating dedduplication method is in Appendix A.
+
+of test samples in a single task, it is considered to 'win' that task. For the instance-level computation process, we do not differentiate between specific tasks and instead calculate overall metrics.
+
+Automatic Evaluation. To evaluate the performance and reduce reliance on manual evaluation, we leverage GPT-4 as the evaluator. Inspired by Wang et al. (2023c,b), we utilize an effective calibration method to evaluate the performance of two LLMs. For each testing question, we query the GPT4 to obtain the comparison result when given two outputs from two LLMs. We note that LLMs are sensitive to the position of responses, so alleviating the positional bias is very important. To balance the position bias, we exchange the order of the responses to form the new prompt. The final evaluating result is the sum of the test results for the two prompts with their order swapped.
+
+Human Evaluation. To validate our proposed framework, we also collect the output data in different settings and evaluate it by human evaluation. We employ 5 students with an ocean science background as human annotators. For each evaluation setting, we sample a set of 200 examples and human annotators will rank the outputs they prefer. The total expense is about 500 US dollars.
+
+# 5 Results
+
+# 5.1 Insights from Performance Results
+
+OCEANGPT can obtain better performance than previous open-sourced LLMs. In Figure 5, we compare the performance of OCEANGPT with the three baseline models across 15 sub-tasks at the task-level in the ocean domain. We utilize both automatic and human evaluators, then compute the win rate  $(\%)$  with baseline models. Compared to the baselines (llama2-chat-7b, vicuna-1.5-7b, chatglm2-6b), OCEANGPT outperforms in the majority of tasks, which demonstrates the effectiveness of the proposed approach.
+
+OCEANGPT excels in a range of ocean science tasks. As shown in Figure 6, we present detailed automatic evaluation experimental results in the OCEANBENCH. It can be clearly seen that our model is superior to baseline language models in the vast majority of tasks. Note that previous open-sourced LLMs even fail to handle several expertise ocean tasks (e.g., Editing). While our designed multi-agent data generation framework can effectively act as experts in various subfields of the
+
+![](images/fd9a9c4440e4a26d88f9b929ba54dceaa519adbf52c24ec3cf93b5c523d2087c.jpg)  
+Figure 5: Ocean task-level results. Left: Automatic evaluation. Right: Human evaluation. Compared to baselines, OCEANGPT performs better than llama2-chat-7b, vicuna-1.5-7b and chatglm2-6b in both two settings. The instance-level result is in Figure 10 (Appendix A).
+
+![](images/92a5de9755441c3b3d87de8e09dce2f5d57745d79342ad6621be3f2bc33f0908.jpg)
+
+![](images/e49fe83e96c17a51bd3447334a8dfc4e5fa09024e0e94e9230c8dd370a65ec9b.jpg)
+
+![](images/b5484b587522b972537a2ef78fc3b6e8b7d662a7f074976f6bc1f0901e44e855.jpg)
+
+![](images/b4d3af6f225d5b18c31a65324fe7fc6db9efbf27f946c0b5f6c06633554f3b87.jpg)
+
+![](images/16391e0503b28c535a0b301ad153963cadee91c0ef8e37bffdf47d9fdfce8c69.jpg)
+
+![](images/1216d2fb67acf16737cbc3466ab46e71af2829d238313310e826ecd20d637060.jpg)  
+Figure 6: Evaluation results of OCEANGPT in the ocean science tasks in OCEANBENCH. The complete experimental results are shown in Figure 12 (Appendix A).
+
+![](images/bc37b399bfb5b9b54b26cad3721ddfbda55a58db1671c8c39889c3237e41c136.jpg)
+
+![](images/f407c4e39af6961fdc985b2f3fcc3922de6fb02fed507d178afd6526782f55db.jpg)
+
+![](images/fb5a6a3bd3de43df2ad00e7ae4d0fa69c16126164a54630fcc2760a243f12924.jpg)
+
+![](images/28fa6eeb2389a2fff0eaba516462bccb7a8ccbd66ba8d8f2cdab69c06ed8fea6.jpg)  
+Figure 7: Performance analysis for different agents. We design three indicators to measure the generation effect.
+
+ocean domain, which indicates that OCEANGPT is a better expert in various ocean domains.
+
+DOINSTRUCT are the effective ocean data generators by multi-agent collaboration. As shown in Figure 7, we design three indicators to measure the data generation effect of our proposed method from the perspectives of knowledge quality, expertise and diversity. We use manual evaluation to calculate the scores of the three indicators from 1 to 5. The higher the score, the better the effect of the testing model. It can be seen that the evolving generator agent can effectively enhance the richness of ocean data. When the extraction agent is at work, the expertise of the content is greatly improved. At the same time, the inspector agent plays a significant role in enhancing the quality of the
+
+generated data. It shows that multi-agent collaboration is effective for ocean instruction generation.
+
+# 5.2 Exploring the Potential of OceanGPT
+
+In this section, we explore the potential of OCEANGPT from the perspectives of ocean science and ocean engineering. For ocean science (Section 5.2.1), we focus on the key scientific issues of nuclear pollution in the ocean environment. For ocean engineering (Section 5.2.2), we explore the potential in robotics applications (Li et al., 2023). Specifically, we use Gazebo as the simulator (Manhães et al., 2016) to test OCEANGPT's ability to control underwater robots.
+
+# 5.2.1 OceanGPT for Ocean Science
+
+In Figure 8, we compare the outputs of OCEANGPT and vicuna-1.5-7b. It shows that OCEANGPT shows a higher level of knowledge expertise when describing the content of radioactive nuclide research. Its textual content is not only clear in structure and well-organized, but also covers various aspects of radioactive nuclide research, from experimental design to data analysis, and then to risk assessment and disposal guidelines.
+
+# OceanGPT
+
+The study of interfacial chemistry and toxicological effects of key radioactive nuclides is a complex research field that requires a multidisciplinary approach. It involves multiple techniques such as experimental research, theoretical research, and numerical simulations. The following is a detailed outline of the steps to conduct research on the interface chemistry and toxicological effects of key radioactive nuclides:
+
+1. Determine the specific radioactive nuclides of interest ...2. Conduct laboratory experiments ... 3. Develop computational models: Computational models can be used to simulate the chemical and toxicological effects of radioactive nuclides. ... 4. Analyze experimental data...5. Develop toxicological risk assessment methods: Toxicological risk assessment methods are used to evaluate the potential hazards of radioactive nuclides. This involves developing models that can predict the toxicological effects of radioactive nuclides and assessing their risks to human health. 6. Conduct field experiments: ...7. Develop guidelines for safe disposal: Safe disposal of radioactive nuclides is critical for preventing environmental contamination and protecting human health. This involves ..
+
+# Vicuna-7b-1.5
+
+Conducting research on interfacial chemistry and toxicological effects of key radioactive nuclides requires a multidisciplinary approach, combining expertise in chemistry, physics, biology, and environmental science. Here are some general steps you can follow to conduct research in this area:
+
+1. Identify the key radioactive nuclides of interest... 2. Develop a research question... 3. Design experiments: Develop a set of experiments that will help you answer your research question. This may involve conducting laboratory experiments, field studies, or both. Be sure to choose experiments that are appropriate for your research question and that use methods that are reliable and valid. 4. Collect and analyze data... 5. Interpret results and draw conclusions: Use your data to draw conclusions about the interfacial chemistry and toxicological effects of the key radioactive nuclides. Be sure to consider any limitations of your study and to consider how your findings may be generalized to other contexts. 6. Communicate your results: Finally, share your findings with others through publication in academic journals, presentations at conferences, and other means. Be sure to clearly communicate the importance of your work and its implications...
+
+![](images/dcc7b3b05deb5c268abc16ced42973ae65ebe683442e68252a3685e7ae8d2c50.jpg)  
+Figure 8: Case analysis on ocean science task. We use blue font to represent the difference and the instruction is: How to conduct research on interfacial chemistry and toxicological effects of key radioactive nuclides?
+
+![](images/d90fd0e2aa37d09cda38ed2969a40bf6ba2e8b2d1e310082298b81bf7c11dca5.jpg)  
+Figure 9: Our model can be instructed for underwater robot control in the simulation platform of Gazebo which shows OCEANGPT gains preliminary embodied intelligence capabilities.
+
+![](images/77e221bcbce38a63acc36c2b9774cc0ce216f9513336e3c5b8c09d5f705dde2f.jpg)
+
+In contrast, although vicuna-1.5-7b has clear expression and logicality, it lacks depth and specific content related to radioactive nuclides. Overall, OCEANGPT has advantages in terms of knowledge expertise, quality, and richness. The complete outputs are shown in the Table 6.
+
+# 5.2.2 OceanGPT for Ocean Engineering
+
+Ocean engineering focuses on the design, development, and management of structures and systems within the ocean environment. It plays an indispensable role in harnessing the vast potential of the oceans while ensuring sustainable and secure maritime operations. To facilitate interaction between OCEANGPT and the external world, we synthesize robotic code data and integrate those machine code instructions into the training data.
+
+As depicted in Figure 9, OCEANGPT can instruct underwater robots via code or console commands, allowing them to execute basic path-finding operations. In this example, by using programming language as a prompt, our OCEANGPT can automatically generate code (the robot generate a double helix path) for underwater robot to operate complex tasks (based on human instructions). In fact, the experimental result suggests that OCEANGPT has the potential to acquire embodied intelligence.
+
+Though we make preliminary attempts for ocean robot interaction, it paves the way for advanced oceanic models to undertake intricate robotic control and complex planning tasks.
+
+# 6 Conclusion
+
+In this paper, we introduce OCEANGPT, the first-ever oceanographic pre-trained language model, which is expert in various ocean science tasks. To alleviate the difficulties for obtaining ocean data, we propose an domain construction framework called DOINSTRUCT, which constructs the ocean instruction dataset by multi-agent collaboration. Each agent in our designed framework is considered as an expert in a specific topic and is responsible for generating the corresponding data. Our generated dataset consists of diverse instructions to align the desired behaviors in ocean science issues. Though comprehensive analysis, we observe that OCEANGPT not only demonstrates a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean engineering. We will continue to improve OCEANGPT by training on larger corpus with larger models (e.g., 30B, 70B) and maintain OCEANBENCH by adding new data and tasks.
+
+# Limitations
+
+Bias in Data Distribution In the realm of LLMs, the distribution of pre-training data and instruction data can be subject to substantial biases, which can shape the outputs of these models. Pre-training data for LLMs often comes from the internet, a vast and potentially biased source of information. The Internet content is inherently skewed, reflecting the biases of its contributors, and hence may not represent a balanced global perspective. Similarly, instruction data can also carry the biases of the humans who create these instructions. For instance, instruction developed by individuals with a particular cultural, socioeconomic, or educational background may inadvertently favor specific perspectives, languages, or communication styles and marginalize others. This bias in data distribution can result in models that reinforce existing prejudices, lack cultural sensitivity, or fail to accurately understand and generate content in underrepresented languages or dialects.
+
+Hallucination in LLMs Although LLMs have shown tremendous success in general domains of NLP, there is a notable issue regarding their tendency to produce hallucinations. Hallucinations refer to instances where LLMs occasionally generate content that deviates from the user's input, contradicts previously generated context, or conflicts with established world knowledge. By developing strategies to address the issue of hallucination, LLMs can better align their outputs with user intent, preserve coherence within generated content, and enhance their overall utility in real-world applications.
+
+# Acknowledgments
+
+We would like to express gratitude to the anonymous reviewers for constructive comments. This work was supported by the National Natural Science Foundation of China (No. 62206246, No. NSFCU23B2055, No. NSFCU19B2027), the Fundamental Research Funds for the Central Universities (226-2023-00138), Zhejiang Provincial Natural Science Foundation of China (No. LGG22F030011), Yongjiang Talent Introduction Programme (2021A-156-G), CCF-Baidu Open Fund, the "Pioneer and Leader + X" Plan Project of Zhejiang Province under Grant (2024C01162), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.
+
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+
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+
+# A Appendix
+
+<table><tr><td>Hyperparameter</td><td>Setting</td></tr><tr><td>Fine-tuning method</td><td>LoRA</td></tr><tr><td>Batch Size</td><td>512</td></tr><tr><td>Device†</td><td>NVIDIA A800</td></tr><tr><td>GPU number</td><td>6</td></tr><tr><td>Learning Rate (LR)</td><td>1e-4</td></tr><tr><td>LoRA r</td><td>8</td></tr><tr><td>LoRA α</td><td>16</td></tr><tr><td>LoRA Dropout</td><td>0.05</td></tr><tr><td>Epoch</td><td>10</td></tr></table>
+
+Table 2: Detailed experimental settings.
+
+![](images/3d7fadbf243f20b0c78b090a0e276bd3703e9265c33f58177c5ae9f9a0f96acc.jpg)  
+Figure 10: Instance-level results (automatic evaluation)
+
+Figure 11: Distribution of our OCEANBENCH.  
+![](images/d389162e20c2dc6d5d99c695512b0c0dae8891d0875a4928977ee54116701ad5.jpg)  
+Description  $\equiv$  Question-Answering  $\equiv$  Recommendation  $\equiv$  Extraction  $\equiv$  Editing  
+Commonsense Reasoning Open-ended Generation Classification  
+Analysis Judgment Recognition Transformation Letter Writing  
+Summary Others
+
+# The Cost for Fine-tuning GPT-3.5-Turbo
+
+For fine-tuning GPT-3.5-turbo, we use the reference code provided by OpenAI to fine-tune their language model. Overall, during the actual debugging process, we train and test the model multiple times, spending a total of nearly 500 US dollars (with the number of high-quality training samples being around 2000). Each time we run the script to train the model, it takes several hours.
+
+The training cost is 0.008 USD per 1K tokens, the input cost during use is 0.012 USD per 1K tokens, and the output cost is 0.016 USD per 1K tokens. Assuming our prompt's input and output for one conversation is 1000 tokens, and if we have 2000 training samples with actual testing on 10000 samples, our training cost would be approximately 16.8 USD. The usage cost of the model after finetuning is about 138.0 USD, making the total cost around 154.8 USD. Since we debugged multiple times in the actual process, the real expenditure is greater. Overall, the overall training cost is not high and is affordable.
+
+# Comparison between Our Fine-tuning Method and the Prefix Prompts
+
+In the paper, we define 5 marine science topics, but this is a very broad categorization. In reality, each major topic contains many subtopics. For example, the topic 'Ecology and Environment' includes subtopics like marine meteorology, marine pollution, and over a dozen others. Altogether, these subtopics amount to over 500. Each of these subtopics is relatively independent and very important. Concatenating them as a prefix to GPT-3.5-turbo would exceed its maximum length limit and the actual usage cost would also be significant. Therefore, we believe that fine-tuning GPT-3.5-turbo is a better choice. The prompt examples are shown in Table 3 and Table 4.
+
+# The Similarity Calculating Method in the Dedduplication Procedure
+
+Because comparing pairs for similarity involves a significant number of calculations, we choose a simple and effective method to address this challenge. We primarily use hash detection to compare two samples. First, we pre-extract keywords from the question part of each sample and then combine them into a new string. For example, the keywords for a data sample might be 'advice', 'ocean', and 'nuclear leakage'. We then employ hash detection
+
+Instruction: You are a helpful ocean assistant. You are to extract the question from the provided content.
+
+Input: Raw sentences in the marine literature (The instruction prompt will be concatenated with raw sentences about seawater resources).
+
+# Output:
+
+Answer: Existing methods of seawater resource exploitation have many problems, such as causing soil erosion and environmental pollution. Therefore, we need to seek more sustainable development methods, including water conservation, wastewater recycling, and the development of new water resources.
+
+Question: Please discuss your views on the current methods of developing seawater resources.
+
+Table 3: The prompt for fine-tuning GPT-3.5-turbo.
+
+# Instruction:
+
+You are a helpful ocean assistant. You are to extract the question from the provided content.
+
+# Input:
+
+Raw sentences in the marine literature (The instruction prompt will be concatenated with raw sentences about seawater resources).
+
+# The demonstration and answer pairs:
+
+I will first give you some typical examples to help you become a marine expert.
+
+Demonstration 1: ... Answer 1: ...
+
+Demonstration 2: ... Answer 2: ...
+
+Demonstration 3: ... Answer 3: ...
+
+Demonstration 4: ... Answer 4: ...
+
+(The demonstration and answer pairs for each marine subtopics. over 500 sub-categories. Each sub-categories has different task types)
+
+# Output:
+
+Answer: ... Question: ...
+
+(Concatenating them as a prefix to GPT-3.5-turbo would exceed its maximum length limit and the actual usage cost is significant)
+
+Table 4: The prefix prompt to GPT-3.5-turbo.
+
+to compare the keywords of the two samples. This method can relatively accurately prevent data leakage during the training process. It's important to note that sometimes the extraction of keywords can lead to redundancy or repetition, so we sometimes process them multiple times. Additionally, we also randomly select some samples and use the GPT-3.5-turbo API for detection to check for any cases of incomplete processing.
+
+Additionally, regarding the dedduplication process between the benchmark and our training dataset, we remove only a hundred or two hundred samples from the training set in the actual experiment, which is not a large number.
+
+![](images/9a2d977a0e54c01dc21fbe43bca2d37ad636bba346e40e825fe30f496403ec83.jpg)  
+Figure 12: Automatic evaluation results of OCEANGPT in all tasks in OCEANBENCH.
+
+# Prompt for "Fine-Tuned Agent as the Literature Extractor":
+
+You are a helpful ocean assistant. You are to extract the question from each of the answer provided.
+
+Answer: This is a seahorse, belonging to the family Sygnathidae. Seahorses are vertebrates commonly found in tropical and subtropical waters. They have unique morphology and biological characteristics and are important organisms in marine ecosystems.
+
+# Prompt for "Evolving Agent as the Generator":
+
+Assuming you are an expert in marine engineering and resources, please keep the meaning of the following sentences unchanged and provide as much professional knowledge as possible.
+
+Sentences:Please recommend some mineral resources found in the East China Sea.
+
+# Prompt for "Agent as the Inspector with Rule Constraints":
+
+Assuming you are an inspector in marine science, please filter and judge the sentences in 'Sentences' based on the constraints provided below:
+
+Constraints: Keyword Filter: Focus on literature that prominently mentions the terms 'coral reefs', 'ocean acidification', or 'deep-sea exploration'. Date Range: Only consider articles published between 2010 and 2022. Author Filter: Prioritize works by
+
+the Oceanic Research Institute. Type of Literature: Specifically look for 'experimental studies' and 'review articles'. Exclude 'conference papers'. Geographical Focus:
+
+Highlight research that pertains to the Pacific Ocean region. Language Constraint: Only select literature written in English. Abstract Inclusion: Ensure the abstract contains the phrase 'climate impact'. Abstract Exclusion: Exclude any literature whose abstract mentions 'laboratory simulation'.
+
+# Prompt for automatic evaluation using GPT4:
+
+Please check if following sentences contain rich ocean related information. If so, output "related". Otherwise, output "unrelated".
+
+Sentences: Dissolved organic carbon (DOC) represents the largest pool of reduced carbon in oceans and plays important roles in the ocean carbon cycle and food webs. DOC comprises nearly half of the riverine organic carbon flux into oceans. Riverine DOC is involved in numerous ecosystem functions, including key roles in chemical and biological processes. Refractory and labile DOC are, respectively, important for carbon sequestration in the ocean and a vital food source for marine bacteria.
+
+Table 5: The prompt example that we use in this work.
+
+<table><tr><td colspan="2">Instruction: How to conduct research on interfacial chemistry and toxicological effects of key radioactive nuclides?</td></tr><tr><td>OCEANGPT</td><td>The study of interfacial chemistry and toxicological effects of key radioactive nuclides is a complex research field that requires a multidisciplinary approach. It involves multiple techniques such as experimental research, theoretical research, and numerical simulations. The following is a detailed outline of the steps to conduct research on the interface chemistry and toxicological effects of key radioactive nuclides: 1. Determine the specific radioactive nuclides of interest: Different radioactive nuclides have different chemical properties and toxicological effects. Therefore, it is necessary to select specific radioactive nuclides of interest and establish a research plan. 2. Conduct laboratory experiments: Laboratory experiments are crucial for studying the chemical properties and toxicological effects of radioactive nuclides. The experiments involve measuring the chemical properties of radioactive nuclides, such as their reactivity, stability, and chemical reactivity. 3. Develop computational models: Computational models can be used to simulate the chemical and toxicological effects of radioactive nuclides. This involves developing mathematical models and algorithms to predict the behavior of radioactive nuclides under different conditions. 4. Analyze experimental data: Experimental data obtained from laboratory experiments need to be analyzed and interpreted. This involves studying the experimental results and comparing them with theoretical predictions. 5. Develop toxicological risk assessment methods: Toxicological risk assessment methods are used to evaluate the potential hazards of radioactive nuclides. This involves developing models that can predict the toxicological effects of radioactive nuclides and assessing their risks to human health. 6. Conduct field experiments: Field experiments are necessary to study the behavior of radioactive nuclides in real-world environments. This involves measuring the chemical and toxicological effects of radioactive nuclides in different ecosystems and studying their distribution and transport. 7. Develop guidelines for safe disposal: Safe disposal of radioactive nuclides is critical for preventing environmental contamination and protecting human health. This involves developing guidelines for the safe disposal of radioactive nuclides and their storage and transport.</td></tr><tr><td>vicuna-1.5-7b</td><td>Conducting research on interfacial chemistry and toxicological effects of key radioactive nuclides requires a multidisciplinary approach, combining expertise in chemistry, physics, biology, and environmental science. Here are some general steps you can follow to conduct research in this area: 1. Identify the key radioactive nuclides of interest: Start by identifying the specific radioactive nuclides that you want to study. This may include elements such as uranium, plutonium, and cesium, among others. 2. Develop a research question: Once you have identified the key radioactive nuclides, develop a research question that addresses an important aspect of their interfacial chemistry and toxicological effects. This question should be specific and focused, and should guide your research efforts. 3. Design experiments: Develop a set of experiments that will help you answer your research question. This may involve conducting laboratory experiments, field studies, or both. Be sure to choose experiments that are appropriate for your research question and that use methods that are reliable and valid. 4. Collect and analyze data: Once you have conducted your experiments, collect and analyze the data you have generated. Be sure to carefully document your methods and results, and to interpret your data in a clear and concise manner. 5. Interpret results and draw conclusions: Use your data to draw conclusions about the interfacial chemistry and toxicological effects of the key radioactive nuclides. Be sure to consider any limitations of your study and to consider how your findings may be generalized to other contexts. 6. Communicate your results: Finally, share your findings with others through publication in academic journals, presentations at conferences, and other means. Be sure to clearly communicate the importance of your work and its implications for interfacial chemistry and toxicology.</td></tr></table>
+
+Table 6: Detailed case analysis on ocean science task. The input prompt is How to conduct research on interfacial chemistry and toxicological effects of key radioactive nuclides?
+
+<table><tr><td>Task Type</td><td>Example</td></tr><tr><td>Analysis</td><td>Input: Analyze the impact of marine environmental dynamics processes on the wave environment. Output: The dynamic processes of the oceanic environment have a significant influence on the wave conditions, such as the temperature of seawater, salinity, and ocean current affecting the height and frequency of waves. Meanwhile, the wave conditions can further impact the dynamics of ocean current and tides.</td></tr><tr><td>Classification</td><td>Input: Classify the ships in the East China Sea according to their type. Output: Ships in the East China Sea can be classified into categories such as merchant ships, fishing vessels, passenger ships, and warships, etc. Merchant ships are primarily used for cargo transport, fishing vessels for fishery resources, passenger ships for tourism and personnel transport, and warships for maritime security and defense. These diverse types of vessels are frequently active in the waters of the East China Sea.</td></tr><tr><td>Commonsense Reasoning</td><td>Input: Why are fish resources in the East China Sea gradually decreasing? Output: Fish resources in the East China Sea are gradually decreasing, mainly due to the impact of factors such as overfishing and environmental pollution. With the increase in population and economic development, fishing activities have been continuously strengthened, resulting in over-exploitation of fish resources. In addition, marine environmental pollution also has adverse effects on the survival and reproduction of fish resources. Therefore, protecting the marine environment and rationally developing fishing resources are key to ensuring the sustainable development of fish resources in the East China Sea.</td></tr><tr><td>Description</td><td>Input: Describe the classification and distribution areas of coral reefs. Output: Coral reefs are calcareous submarine structures formed by coral entities and their attachments. According to their crescent characteristics, they can be categorized into large ring coral reefs, small ring coral reefs, crisscross serrated coral reefs, irregular shaped coral reefs, etc. Approximately 70% of the world&#x27;s coral reefs are distributed in tropical and subtropical sea areas, with the most important distribution areas being the western Indian Ocean, western Pacific Ocean, and eastern Pacific Ocean.</td></tr><tr><td>Editing</td><td>Input: Edit a big data report used for analyzing marine ecosystems. Output: Important parameters that can be tracked in marine ecosystems include water temperature, salinity, direction of waves, quantity and species of plankton, rate of ocean acidification, etc. In marine big data analysis, we use these data to train machine learning algorithms to understand the changes in the marine ecosystem, forecast potential environmental changes, etc. By utilizing these critical parameters, we can perceive the panorama and pattern of the marine ecosystem and extract valuable information from it to support adaptive and sustainable management practices in the ocean.</td></tr><tr><td>Extraction</td><td>Input: Extract the definition of the greenhouse effect and its causes. Output: The greenhouse effect refers to a thermal blanketformed by the absorption and reflection of solar heat radiation by greenhouse gases (such as carbon dioxide, methane, fluorocarbons, etc.) in the atmosphere, thereby raising the surface temperature of the earth. The sources of greenhouse gases include human activities, natural gases, deforestation, etc., which leads to an intensification of the greenhouse effect and global warming.</td></tr><tr><td>Judgment</td><td>Input: Is active intervention suitable for maintaining the coral reef ecosystem? Output: Active intervention can protect coral reef ecosystems, such as through artificial restoration, controlling sea water pollution, controlling carbon emissions, and reasonable development of coral reefs. Therefore, the coral reef ecosystem is suitable for active intervention and maintenance.</td></tr><tr><td>Letter Writing</td><td>Input: Write a letter to the local environmental protection agency about the waste discharge and pollution recently discovered in the sea area, proposing solutions. Output: Dear xx Environment Bureau: Recently, we have discovered a large amount of waste emission and pollution in the sea area, which has caused serious damage to our marine environment. We suggest that you take a series of measures to strengthen the monitoring and management of pollution sources in the sea area, punish illegal emissions in accordance with the law, and educate illegal polluting enterprises to enhance their environmental protection awareness. We hope that you can take action to effectively protect our marine environment.</td></tr><tr><td>Open-ended Generation</td><td>Input: Briefly introduce the principle and method of wave energy generation. Output: The principle of wave power generation is to convert wave energy into mechanical energy, and then turn the mechanical energy into electrical energy through a generator. The methods of wave power generation include: floating system, wave influx system, wave reflux system, and wave pressure system.</td></tr><tr><td>Question Answering</td><td>Input: What is three-dimensional digital ocean modeling technology? Output: Three-dimensional marine digital modeling technology involves the use of advanced remote sensing, lidar, sonar, and other detection technologies to acquire surface and subsurface oceanographic topographic data. Through digital modeling methods, a three-dimensional mathematical model of the ocean can be constructed. This type of model can provide information on topography, ocean currents, hydrology and acoustics, which is beneficial for applications in ocean resource development, environmental protection, and management, among others.</td></tr><tr><td>Recognition</td><td>Input: Identify the following sea fish species: sea bream, codfish, saury, and shark. Output: Dorada is a common type of sea fish, laterally compressed in form with a big mouth, and is known for its delicious meat. Cod is also a common sea fish, slender in shape with a soft hanging lower jaw, and is also a delicacy. Saury is a type of fish that enters ports from summer to autumn and can be made into a variety of dishes. Sharks are carnivorous marine animals with many species, some of which are primarily used as food, while others may attack humans.</td></tr><tr><td>Recommendation</td><td>Input: Recommend a desalination method suitable for deep-sea water resource development Output: The development of deep-sea water resources is suitable for the adoption of low-temperature seawater desalination technology, which uses cooling water to lower the seawater temperature below the ambient temperature, condensing out fresh water. Compared to traditional seawater desalination technology, low-temperature seawater desalination can conserve energy and reduce costs, while the resulting brine is also more concentrated.</td></tr><tr><td>Summary</td><td>Input: Provide a summary of the current status and future development prospects of offshore wind power generation technology. Output: Ocean wind power generation technology is one of the hot technologies in the field of renewable energy, its power generation efficiency is becoming increasingly high, equipment is becoming increasingly advanced, and the costs are gradually decreasing. It has a broad development prospect and can inject new development momentum into China&#x27;s marine economy.</td></tr><tr><td>Transformation</td><td>Input: How to transform marine plants into green products? Output: The conversion process of marine plants can be divided into initial collection and extraction of effective parts, followed by processing to obtain the final product. Among them, extraction and processing are critical steps, and they vary for different marine plants. Common processing methods include alkali extraction, acid extraction, and enzyme extraction. The resulting green products also vary, such as chlorophyll, peptide substances, polysaccharide substances, etc.</td></tr></table>
+
+Table 7: Examples for tasks in OCEANBENCH.

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+# OLIVE: Object Level In-Context Visual Embeddings
+
+Timothy Ossowski<sup>1</sup>, Junjie Hu<sup>1,2</sup>
+
+$^{1}$ Department of Computer Science,  $^{2}$ Department of Biostatistics and Medical Informatics
+
+University of Wisconsin, Madison, WI, USA
+
+ossowski@wisc.edu, junjie.hu@wisc.edu
+
+# Abstract
+
+Recent generalist vision-language models (VLMs) have demonstrated impressive reasoning capabilities across diverse multimodal tasks. However, these models still struggle with fine-grained object level understanding and grounding. In terms of modeling, existing VLMs implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and inevitably introduces noisy spurious background features. Additionally, these models struggle when generalizing to unseen visual concepts and may not be reliable for domain-specific tasks without further fine-tuning. To address these limitations, we propose a novel method to prompt large language models with in-context visual object vectors, thereby enabling controllable object level reasoning. This eliminates the necessity of fusing a lengthy array of image patch features and significantly speeds up training. Furthermore, we propose region-level retrieval using our object representations, facilitating rapid adaptation to new objects without additional training. Our experiments reveal that our method achieves competitive referring object classification and captioning performance, while also offering zero-shot generalization and robustness to visually challenging contexts. $^{1}$
+
+# 1 Introduction
+
+Despite the popularity, many existing VLMs such as LLaVA (Liu et al., 2023), MiniGPT4 (Zhu et al., 2023a), and mPLUG-OWL (Ye et al., 2023) handle the entire image for visual understanding, leading to two major shortcomings. First, these VLMs use a visual transformer to split an image into a grid of image patches and embed them into a lengthy array of image patch embeddings that have object level features scattered around different positions of the array. This leads to the different granularity
+
+between the image patch tokens and text tokens, further creating difficulty in aligning and grounding visual objects to text concepts. Second, feeding all image patch embeddings to the large language model (LLM) decoder is problematic due to the resulting long context and inefficiency of including in-context examples from multiple images.
+
+To improve fine-grained visual alignment, recent region-based VLMs are pre-trained to integrate object level information into the LLM decoder. GPT4ROI (Zhang et al., 2023b) pre-trains LLMs to understand ROIAlign features (He et al., 2017) extracted from bounding boxes. Other similar methods such as Shikra (Chen et al., 2023) or Kosmos-2 (Peng et al., 2024) ground and refer to objects using text in multimodal referential dialogues. FERRET (You et al., 2023) and ViP-LLaVA (Cai et al., 2023) further support free-form shapes as referring input by summarizing visual features sampled within the region of interest. Although these methods provide improvement to object level reasoning, they still fail at recognizing unseen/rare objects and are sensitive to spurious background features, as shown in §5. Even powerful closed-source multimodal models such as GPT4V are unreliable to deploy in high-stakes domain-specific situations such as the medical domain (Senkaiahliyan et al., 2023).
+
+A straightforward way to handle generalization to unseen visual content is to integrate a retrieval component. Methods such as REVEAL (Hu et al., 2023) and MuRAG (Chen et al., 2022) provide retrieved multimodal facts as supplementary context to help VLMs generalize to new concepts without further training. However, these models do not consider object level retrieval and in-context prediction. Models such as Flamingo (Alayrac et al., 2022) and Qwen-VL (Bai et al., 2023) allow for in-context examples from multiple images, yet do not support object level retrieval and reasoning.
+
+To address the above issues, we propose to encode object level in-context visual embeddings
+
+(OLIVE) to enhance LLMs with region-level reasoning capabilities. Critically, we omit lengthy image patch features and encode visual object embeddings by a lightweight encoder of 20 million parameters, allowing for faster training and direct connection to existing LLMs. This preserves the full functionality of the original LLMs, while also introducing novel multimodal reasoning abilities. Furthermore, our object level retrieval module allows for more precise queries and retrieved information to help the model adapt to domain-specific tasks with limited training data. Our contributions are summarized below and in Table 1:
+
+- We propose a lightweight object encoder that can be connected to existing LLMs to enable controllable object level multimodal reasoning with free-form input annotations.  
+- Our model omits image patch features and summarizes object features into a single vector, significantly reducing context length for more efficient training and inference, and allowing for in-context examples from multiple images.  
+- We conduct extensive experiments with region-retrieval of object level features and showcase rapid adaptation to unseen visual concepts.
+
+# 2 Preliminaries
+
+Generative VLM Architecture Recent generative VLMs (e.g., LLaVA, BLIP-2) adopt a similar architecture that connects a pre-trained visual encoder  $\phi_v$  and a pre-trained language model decoder  $\phi_t$  through a lightweight fusion neural network, denoted as  $\phi_c$ . Specifically, the fusion module first uses a projection function to map a visual feature  $\mathbf{v} \in \mathcal{V}$  to the text embedding space  $\mathcal{X}$  of the language model decoder, and then fuse the visual and text embeddings as input to language model decoder. Formally, given an image  $v$  and a text prompt  $x$ , the decoder takes in the combined feature  $\mathbf{x}$  to autoregressively predict the output  $y$ .
+
+$$
+\begin{array}{l} \mathbf {x} _ {t} = \operatorname {T x t E m b e d} (x; \phi_ {t}) \in \mathcal {X} (1) \\ \mathbf {v} = \operatorname {I m g E n c o d e r} \left(v; \phi_ {v}\right) \in \mathcal {V} (2) \\ \end{array}
+$$
+
+$$
+\mathbf {x} _ {v} = \operatorname {P r o j e c t} (\mathbf {v}; \phi_ {c}) \in \mathcal {X} \tag {3}
+$$
+
+$$
+\mathbf {x} = \text {F u s e} \left(\mathbf {x} _ {v}, \mathbf {x} _ {t}; \phi_ {c}\right) \tag {4}
+$$
+
+$$
+p _ {\mathrm {v l m}} (y | v, x) = \prod_ {j = 0} ^ {| y |} p _ {\phi_ {t}} \left(y _ {j} \mid \mathbf {x}, y _ {<   j}\right) \tag {5}
+$$
+
+Different from prior fusion modules (e.g., linear projection in LLaVA, gated cross-attention in
+
+Flamingo, and Q-former in BLIP-2) that project the whole image features, we propose an object level encoder (§3.1) that captures fine-grained region features and speeds up training and inference.
+
+Visual Instruction Tuning We adopt a similar visual instruction-tuning approach as Liu et al. (2023) by fine-tuning parts of the VLM parameters (e.g.,  $\phi_c$  and/or  $\phi_t$ ) on instruction-following data. The training objective is based on maximum likelihood estimation for next-token predictions given the input image and the text prompt. Different from prior work using pure text prompts, our object encoder and retrieval module ( $\S 3.1$ ,  $\S 3.2$ ) enables the usage of code-switched prompt sequence mixing text tokens and image object tokens, and the rapid adaptation to unseen domains via in-context prediction.
+
+# 3 Method
+
+This section as well as Figure 1 highlights the main components of our method. We first design an object encoder (§3.1) to learn visual object embeddings in a shared vision-text space, then apply a similarity search over object embeddings to retrieve relevant visual objects (§3.2), and finally construct a code-switch multimodal prompt to integrate the retrieved object information for generation (§3.3).
+
+# 3.1 Object Encoder
+
+Following popular region-grounded models such as FERRET (You et al., 2023), we allow for free-form annotation of objects using the object segmentation mask  $\mathbf{o}_{\mathrm{mask}}$  as input. Specifically, we first encode an image  $v$  with a vision transformer (Dosovitskiy et al., 2020) to obtain patch-level features  $\mathbf{v}$ :
+
+$$
+\mathbf {v} = \operatorname {I m g E n c o d e r} \left(v; \phi_ {v}\right) \in \mathbb {R} ^ {\left(n ^ {2} + 1\right) \times d}, \tag {6}
+$$
+
+where  $n$  is the grid size and  $d$  is the dimension of hidden states. To further obtain an object level feature  $\mathbf{v}_{\mathrm{obj}}$  from the image, we first extract a subset of the image features  $\mathbf{V}_{\mathrm{masked}}$  corresponding to the binary object segmentation mask  $\mathbf{o}_{\mathrm{mask}}$ :
+
+$$
+\mathbf {v} _ {\text {m a s k e d}} = \mathbf {v} [ \text {F l a t t e n} (\mathbf {o} _ {\text {m a s k}}) ] \in \mathbb {R} ^ {l \times d} \tag {7}
+$$
+
+where  $\mathbf{o}_{\mathrm{mask}}$  is a  $n\times n$  binary matrix, indicating the corresponding image patches occupied by an object in the image, and  $l$  denotes the number of the occupied patches. These segmentation masks can be created by automatic segmentation tools such as SAM (Kirillov et al., 2023) or provided by human selection on the image. The segmentation
+
+<table><tr><td>Model</td><td>Free-form Visual Prompts</td><td>Free-form Text prompts</td><td>Visual Generalization</td><td>Generative Approach</td><td>Multi-Image</td></tr><tr><td>Ferret</td><td>✓</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Flamingo</td><td>✗</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>GPT4ROI</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>GLAMM</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>RegionCLIP</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Llama-Adapter v2</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>ViP-LLAVA</td><td>✓</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>OLIVE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></table>
+
+Table 1: Comparison of OLIVE to recent VLMs. To the best of our knowledge, we are the first method to offer visual generalization with in-context prompting, while also allowing for free form annotation. A more comprehensive summary of related studies is in §6.
+
+![](images/faf2272f678d51bfad48747f34ad9b41e8cc4719775fbd6953d5d3b04205a0e7.jpg)  
+Figure 1: An overview of our method which consists of three main components, described in detail in Section 3. The object encoder (green) is the only module with required trainable parameters. Note: the prompt in the LLM decoder is modified slightly for visual clarity. The exact prompt can be found in Appendix A.
+
+mask is first flattened and used to select object patches  $\mathbf{v}_{\mathrm{masked}}$  from  $\mathbf{v}$ . Finally, we obtain the object embedding by compressing  $\mathbf{v}_{\mathrm{masked}}$  into a single vector  $\mathbf{v}_{\mathrm{obj}}$ .
+
+$$
+\mathbf {v} _ {\mathrm {o b j}} = \operatorname {O b j e c t E n c o d e r} \left(\mathbf {v} _ {\text {m a s k e d}}; \phi_ {c}\right) \in \mathbb {R} ^ {d}, \tag {8}
+$$
+
+where the object encoder uses a lightweight 2-layer transformer that acts similar to a visual resampler (Zou et al., 2023a; Li et al., 2023), followed by a learnable linear layer to further project the visual representation to the text space (Liu et al., 2023).
+
+# 3.2 Visual Object Retrieval
+
+In many cases, the object of interest does not resemble anything seen during training. With our visual object embeddings, we can easily perform object level retrieval to match an open class of visual objects and integrate the retrieved information into the language decoder for predicting unseen or rare objects from specific domains (e.g., biomedicine). To this end, we assume access to a retrieval set  $\mathcal{R} = \{(o_i,d_i,v_i)\}_{i = 1}^m$ , where each triple consists
+
+of an object's segmentation mask  $\mathbf{o}_i$ , the object's text description  $d_i$  and the image  $v_i$  containing this object. To retrieve relevant objects from  $\mathcal{R}$ , we use a similar object encoding as §3.1 except that we use the mean pooling of  $\mathbf{v}_{\mathrm{masked}}$  as the object encoder in Eq. (9), since this simple strategy does not require any learnable parameters for projection to the text embedding space and visual object embeddings can be pre-computed before any fine-tuning. However, we use a learnable object encoder in Eq. (8) to connect object embeddings to the LM decoder during instruction-tuning for text generation (§3.3).
+
+$$
+\mathbf {v} _ {\mathrm {o b j}} = \operatorname {M e a n P o o l} \left(\mathbf {v} _ {\text {m a s k e d}}\right) \in \mathbb {R} ^ {d}, \tag {9}
+$$
+
+During retrieval, we compute a query vector  $\mathbf{v}_{\mathrm{query}}$  for a given object, and compute the cosine similarity scores between  $\mathbf{v}_{\mathrm{query}}$  and all the visual object embeddings from  $\mathcal{R}$  to obtain the top  $k$  closest triples, denoted as  $\mathcal{K} = \{(o_i,d_i,v_i)\}_{i = 1}^k$ .
+
+# 3.3 In-context Prompt Construction
+
+As the visual object embeddings are projected into the text embedding space of the LM decoder, this
+
+allows us to construct a code-switched prompt that mixes visual objects with text tokens for the LM decoder (e.g., Llama 2 (Touvron et al., 2023)). In addition, as our object encoder compresses a visual object into a single vector  $\mathbf{v}_{\mathrm{obj}}$ , this significantly shortens the length of the visual tokens that the LM decoder needs to fuse with text tokens. Therefore, we can easily integrate multiple retrieved object embeddings into the prompt to augment the LM decoder for in-context text generation. Specifically, we define a special vocabulary token [obj] which can be inserted flexibly in the user prompt  $x$ . For example, the user can ask "[obj] Describe this part of the image" to perform region-level description. The embedding of this token is directly replaced with its corresponding visual object embedding. Formally, given a text prompt  $x$  that contains indexed [obj] tokens referring to an object  $\mathbf{v}_{\mathrm{obj}}$  of interest in an image  $v$  and its relevant objects in  $\mathcal{K}$ , we define a prompting function that replaces the text embedding of [obj] with its corresponding visual object embedding, and integrates the top  $k$  most similar objects  $\mathcal{K}$  as in-context examples. For example, a prompt with retrieved in-context examples can be "The top [k] related objects are: [obj_1] is a [label],...[obj_k] is a [label]. [obj_query] What is this?". We provide more details about in-context prompt templates and construction in Appendix A.
+
+$$
+\mathbf {x} = \operatorname {P r o m p t} (x, \mathbf {v} _ {\mathrm {o b j}}, \mathcal {K}) \tag {10}
+$$
+
+Finally, we feed the multimodal prompt  $\mathbf{x}$  into the LM decoder for text generation following Eq. (5). Note that compared to prior VLMs (e.g., LLaVA) that directly fuse the patch-level features  $\mathbf{v}$  of the whole image (Eq. 6) with object information scattering around different positions in  $\mathbf{v}$ , our object encoding is computationally more efficient and speeds up the training that involves multiple in-context objects in the multimodal prompt.
+
+# 4 Experimental Settings
+
+In this section, we first describe two main object-level tasks for evaluation (§4.1) together with the datasets used (§4.2). Finally, we describe three variants of our model (§4.3), the training details (§4.4), and the other baselines in comparison (§4.5).
+
+# 4.1 Object-level Tasks
+
+Referring Object Classification Given an object referred by its image location (e.g. segmentation mask/bounding box), the model is instructed to
+
+generate a text that predicts the object's class label in a predefined label set,  $\mathcal{C} \in \{c_1, c_2, \ldots, c_n\}$ . We provide the ground truth segmentation mask to eliminate localization errors and focus on evaluating the models' understanding of image objects.
+
+Referring Expression Generation Given an input image object referred by a segmentation mask, the model is instructed to generate a natural language expression which semantically matches multiple ground-truth references  $\mathcal{R} \in \{r_1, r_2, \ldots, r_m\}$ . We use METEOR (Banerjee and Lavie, 2005) and CIDEr (Vedantam et al., 2015) score for evaluating generated description quality.
+
+# 4.2 Datasets
+
+This section describes the different datasets used in our experiments, with more details in Appendix C.
+
+Common Objects in Context (COCO) (Lin et al., 2014) is a popular visual reasoning dataset with over 800,000 object-level annotations for 80 categories of objects. We use it to train our model to understand region input since it contains high-quality segmentation annotations. We use the standardized train and validation 2017 splits for the detection task, and discard a few  $(< 1\%)$  small segmentation annotations that fail to be converted into a binary mask. Following (Zhong et al., 2022), we evaluate in the setting where ground-truth segmentations are provided as input to eliminate localization errors. We use the standard metric of mean average precision (mAP) for object detection using the COCO API, as well as overall accuracy.
+
+refCOCOg (Kazemzadeh et al., 2014) is a variant of the COCO dataset with about 50,000 annotations for objects and their description. We use the data to train our model to describe image regions and use their standardized train/validation split.
+
+ChestX-Ray8 (CXR8) (Wang et al., 2017) is a medical dataset consisting of 108,948 frontal-view X-ray images. The image annotations for the 8 possible pathologies are text-mined from the radiology reports using NLP tools. A small subset of 984 images contains bounding box annotation of the pathology. We use this subset for our zero-shot domain adaptation experiments, splitting the data into  $16\%$  retrieval set and  $84\%$  test data. The retrieval set consists of 20 examples of each pathology, and we use overall accuracy as the evaluation metric.
+
+# 4.3 OLIVE Variants
+
+OLIVE-R (Retrieval-only) This retrieval-only method predicts the answer to the user question by taking a majority vote of the top  $k$  retrieved examples. For simplicity, we fix  $k = 5$  for this setting unless otherwise specified and analyze the effect of  $k$  in Figure 6. Although simple, this baseline proves to be effective and provides salient additional context as described in §4.3. However, this discriminative model does not allow for free-form text generation for tasks such as region captioning.
+
+OLIVE-G (Generative-only) This model is trained to generate free-form text based solely on the user question and corresponding object features. We omit the retrieved information to observe the capability of the standalone object representations. We find that even without retrieval, the model can learn to perform more challenging object-level tasks such as region description. The final decoder input can be expressed as a variant of Eq. (10):
+
+$$
+\mathbf {x} = \operatorname {P r o m p t} (x, \mathbf {v} _ {\mathrm {o b j}}). \tag {11}
+$$
+
+OLIVE-RG (Full) Our full model generates text outputs based on in-context object examples from retrieval. The multimodal in-context prompt is constructed using Eq. (10). This prompt includes the retrieved object features, their labels, and their similarity scores. The exact construction can be found in Appendix A. The top  $k$  retrieved multimodal documents in  $\mathcal{K}$  are obtained using the same retrieval described in §3.2 and ordered in increasing relevance score. Both OLIVE-G and OLIVE-RG use greedy decoding for text generation.
+
+# 4.4 Training Details
+
+Our model uses a frozen ViT-L/14 vision transformer from a CLIP model to obtain patch-level features. For our LLM backbone, we use either Llama 2-7B or GPT-2 (124M) (Radford et al., 2019). The LLM is finetuned with LoRA (Hu et al., 2021) as we find this improves model performance. We use the train splits of two different region-level datasets (i.e., COCO, refCOCOg) as our training data for their respective tasks, and evaluate models on their corresponding validation splits because their test data does not have object-level annotation. More details are in Table 7 and we leave the other hyperparameter search to future exploration. We additionally find that we can train a multi-task model by combining the datasets for all object-level tasks (Details in Appendix E).
+
+# 4.5 Other Baselines in Comparison
+
+CLIP (Radford et al., 2021) Contrastive Language Image Pretraining learns a joint vision-language space between images and their matching captions. We use this method for zero shot object classification by predicting the target with the highest cosine similarity to the cropped region.
+
+BioMedCLIP (Zhang et al., 2023a) The authors train a CLIP model aligned to biomedical image-text pairs, achieving state of the art on a variety of medical tasks. We use this model as a baseline for object classification in the medical domain.
+
+RegionCLIP (Zhong et al., 2022) This model learns region-text level alignment through soft-labels obtained from CLIP. We use it for referring object detection based on ROAlign features.
+
+Kosmos 2 (Peng et al., 2024) This generative VLM trains a LLM decoder to perform a variety of visual grounding tasks from their newly introduced grounded image-text (GRIT) dataset. We compare with their results on referring expression generation on the refCOCOg dataset.
+
+Flamingo (Alayrac et al., 2022) This generative model learns to connect frozen visual features and LLMs by training on interleaved image-text data. We evaluate Flamingo's few-shot performance on referring expression generation on cropped image regions. We use an open-source implementation trained on the multimodal C4 (Zhu et al., 2023b) and LAION-2b (Schuhmann et al., 2022) datasets.
+
+5 Results and Analysis  
+
+<table><tr><td>Method Type</td><td>Pre-Training Data</td><td>Method</td><td>Accuracy</td></tr><tr><td rowspan="6">Classification</td><td>None</td><td>OLIVE-R</td><td>33.5</td></tr><tr><td>PMC-15</td><td>BioMedCLIP</td><td>32.5</td></tr><tr><td>PMC-15</td><td>BioMedCLIPcrop</td><td>23.3</td></tr><tr><td>CLIP400M</td><td>CLIP</td><td>14.0</td></tr><tr><td>None</td><td>Random Guess</td><td>12.5</td></tr><tr><td>CLIP400M</td><td>CLIPcrop</td><td>11.2</td></tr><tr><td rowspan="3">Generative</td><td>COCO</td><td>OLIVE-RG</td><td>31.2</td></tr><tr><td>C4 + LAION-2b</td><td>Flamingo-9B</td><td>12.5</td></tr><tr><td>COCO</td><td>OLIVE-G</td><td>0.0</td></tr></table>
+
+Table 2: Zero shot transfer results of our Llama 2 backbone referring object classification model to new objects. crop indicates cropping the image based on the bounding box annotation and using it as the input image. Flamingo is evaluated 8-shot (one example from each pathology).
+
+# 5.1 Referring Object Classification
+
+Unseen Object Classification One of the benefits of our retrieval augmented system is its rapid
+
+![](images/e82ba7ef4e71e443d10c3fd850a9ebcd4d4b70f30f6047810963f075ba94a321.jpg)  
+Figure 2: Examples showcasing the benefit of using retrieval for out of distribution objects. Despite not being trained with any images of sharks or turtles, OLIVE-RG can describe them zero shot by adding a few pictures of them in the retrieval set.
+
+![](images/dc0befa45937b427cf7387d6437417055734a816148687e3c0402853999d9244.jpg)
+
+generalization to unseen visual concepts. We estimate this capability by training on the COCO dataset and evaluating object classification on an unseen medical dataset which has drastically different types of images and limited training data. Table 2 illustrates the performance of our method on the CXR8 dataset in either a classification or generative setting. Even with as little as 20 examples per class in the medical retrieval set, OLIVE-R achieves competitive performance compared to domain-adapted models (i.e., BioMedCLIP), which we hypothesize is because of our region-level retrieval and in-distribution retrieval set. We also note that our generative approach OLIVE-RG can utilize the retrieved in-context examples and achieve similar performance to BioMedCLIP, despite only being trained on COCO images. Without retrieval, the generative model fails catastrophically with  $0\%$  accuracy, and zero-shot CLIP achieves about the same performance as random guessing.
+
+Rare Object Classification We also investigate our model's performance on rare, but seen objects. Figure 3 shows our method's performance on the top 5 rarest classes in the COCO dataset. For OLIVE-G and OLIVE-RG, we use a 224 pixel resolution visual encoder to match the CLIP visual encoder. OLIVE-G tends to have lower performances on the rare classes. However, when combining retrieval with parameterized methods in OLIVE-RG and OLIVE-RG-336px, the performance on rare classes improves significantly, with OLIVE-RG-336px performing better than CLIP on all rare classes. OLIVE-RG also achieves better performance on three out of five classes despite being trained on less data. Our model's overall performance can be found in Appendix D.
+
+![](images/76c0102344586fd08577965664983f9e3dfc5488335b5874bbe88c185d67a992.jpg)  
+Figure 3: Classification accuracy on the most uncommon objects in the COCO dataset. We use a Llama 2 decoder backbone and numbers below the object classes indicate their proportion of the retrieval set. Combining retrieval with generative methods improves performance and increasing vision encoder resolution provides further gains.
+
+# 5.2 Referring Expression Generation
+
+Captioning Unseen Objects In addition to referring object classification, we investigate our model's ability to caption out-of-distribution objects. Figure 2 illustrates an example of asking our model to describe animals not seen during training. Without retrieval, OLIVE-G fails to describe the shark and turtle. However, after manually adding just 5 labeled objects of turtles and sharks to the existing retrieval set, OLIVE-RG accurately describes the object and provides supporting examples for its prediction. The label description for each object in the retrieval set is only the name of the animal, but the model generates additional characteristics in its description. Appendix B shows more examples of zero-shot adaptation to unseen visual concepts in the object classification setting.
+
+![](images/61d174f0d744bd8b515c4029b27f03d5271e7c157dd17274aa4e158312635a77.jpg)  
+Prompt: What is the object in the red rectangle?  
+Kosmos 2: It is a boogie board  
+ViP-LLaVA: The object is a skateboard  
+OLIVE-G (ours): The snowboard of the man
+
+Challenging Visual Context To test the quality of the representations generated from our object encoder, we qualitatively evaluate our model prediction in adversarial visual contexts. Figure 7 shows a white dog and a black cat in a "yin-yang" shape. We observe that free-form annotation allows for more precise user queries and object descriptions, and illustrates other properties such as scene content awareness and patch-level details as shown in Appendix B. While many VLMs can accurately understand normal scenes, Figure 4 illustrates an example in which an object-level representation may be necessary, with recent works struggling to caption the snowboarder on the beach. The detailed performance of our model on the refCOCOg captioning task can be found in Appendix F.
+
+# 5.3 In-context Example Size
+
+Since our method omits image patch features and compresses object information into a single vector, it can process many objects from different images at once. In Figure 5, we highlight the difference in context length for various methods when prompted with multimedia examples. We assume an average prompt length of 30 accompanying each in-context image example for all models. Even approaches designed for interleaved image-text data such as Flamingo insert multiple latent vectors for each image, incurring a higher cost than our approach.
+
+![](images/7c49ebf01bd9606a55f188d16b310794704a45e33483fedb3be7c30336515710.jpg)  
+Figure 5: Context length of different VLMs when prompted with multimodal input. Models that represent images with many patch tokens or learned latents incur higher costs with more in-context examples.
+
+![](images/cb322035964d3bb077f8ea50438aa74aa337ffd5ca6f0e06b714069c4f355e04.jpg)  
+Figure 4: A challenging visual example in which the background of the image does not correspond with the query object. Methods which cross-attend to the whole image struggle to identify the snowboard, while our object representation enables more accurate description.  
+Figure 6: An illustration of how retrieval set size and choice of  $k$  affects referring object classification performance. Numbers in the legend indicate the size of the retrieval set, and the stars are the highest accuracies achieved on their curves.
+
+# 5.4 Sensitivity on Retrieval: Coverage and  $k$
+
+In Figure 6 we analyze the effect of changing the size of the object retrieval set as well as the number of retrieved examples,  $k$ . To thoroughly test various settings, we evaluate the retrieval-only based approach (OLIVE-R) on the validation split of the COCO dataset using different sized subsets of the training data for retrieval. We ensure the retrieval set contains an equal amount of each object class when possible. Our results indicate that the optimal value of  $k$  depends on the size of the retrieval set. With a small retrieval dataset (red), performance is lower and the optimal  $k$  tends to be smaller. Larger retrieval sets (blue, green) benefit from retrieving more examples and have greater performance.
+
+# 5.5 Object Vector Visualization
+
+Having a single vector representation for each object allows for visualization using dimensionality reduction. In Figure 8, we perform principal com
+
+![](images/760c56af6a0ca27720c2bf29f9bba08f2422bf098911effb66928afb65dd7b5f.jpg)  
+Figure 7: An illustration of the benefit of free-form visual input. Models that use the entire image or bounding box to refer to regions fail to describe the black cat, while OLIVE-G can use free-form annotation to identify the white dog and black cat.
+
+![](images/5193d49e9868d40e04bcec92265b4f597b83532d405c83e299f7cb3d03791092.jpg)
+
+![](images/4b21aeb1efafccfd019bd3a108ca0d28a45e8937cde471b86d5d0373cebc28d0.jpg)
+
+![](images/6e485ee3cc7bcc55af65c337255376a05062a118a15560d211196eb5a6896d44.jpg)
+
+ponent analysis (PCA) on the hidden states of object vectors at different layers in the LLM decoder. We plot 200 examples from each of 10 object categories and note several patterns. First, objects from the same class tend to appear together, even though they appear in different visual contexts. This suggests that the object encoder has semantic understanding of the visual concepts. Second, the object vectors naturally form hierarchical clusters where objects from the same super class such as vehicle, animal, or fruit have overlapping clusters. Lastly, the clustering appears similar across all layers, with only minor variations.
+
+# 6 Related Work
+
+Grounding in Language and Vision A popular approach for aligning vision and language embeddings is contrastive learning methods such as CLIP and ALIGN (Li et al., 2021). However, these methods align the entire image representation, leading to poor reasoning on image details for downstream vision language tasks. Region-CLIP (Zhong et al., 2022) and GLIP (Li et al., 2022) address this issue by proposing fine-grained alignment with region-text pairs during pretraining. GLIPv2 (Zhang et al., 2022) further improves the pretraining and alignment by introducing localization, detection, and other tasks. Another recent popular approach involves training models on automatically curated region-level data from image-caption pairs (Peng et al., 2024). Many other works focus on region-level alignment during pretraining for greater vision-language understanding (You et al., 2023; Chen et al., 2023; Zeng et al., 2022b,a). More generally, a recent study (Bugliarello et al., 2023) shows that VLMs with fine-grained object-level
+
+pretraining such as X-VLM (Zeng et al., 2022a) have better reasoning ability. Other works align vision and language using regularization or loss to create relation aware cross attention between modalities (Pandey et al., 2023; Ren et al., 2021).
+
+Visual Resampling Visual resampling is a popular technique to compress long sequences of image features into a few rich vector representations. This is achieved by constructing a fixed amount of learnable vectors that attend to the visual features through cross-attention layers. Models such as BLIP (Li et al., 2023) first explore this idea to connect frozen vision features to LLMs efficiently by summarizing the content of the image. Other methods including X-Decoder (Zou et al., 2023a) or SEEM (Zou et al., 2023b) use resampling to encode various types of prompts or intents which improve the LLM decoding ability. Additionally, works such as Flamingo (Alayrac et al., 2022) and QwenVL (Bai et al., 2023) show that multiple images can be inserted in-context to the prompt by compressing image features with resamplers, enabling few shot capabilities. Our work visually resamples object representations for object-conditioned text generation, and only uses a single vector for the representation. This allows for more fine-grained reasoning and longer in-context prompting.
+
+Retrieval Augmented VLMs In the text domain, learning to retrieve relevant documents to enhance the LLM query (Guu et al., 2020) has been explored extensively (Wang et al., 2023). Recent VLM works follow a similar approach to retrieve multimodal documents to improve performance on knowledge-intensive tasks and improve generalization to rare situations. Gao et al. (2022) summarizes visual content into natural language to use as
+
+![](images/32684cc661e8d2f7f16ae70883ccc929a5e02e616f3f2f9fde20af0e56e3fd67.jpg)  
+Figure 8: Visualization of the top 2 components when performing PCA decomposition on object vectors in different layers of the LLM decoder (Llama 2). We display 200 examples from each class. Across different layers, objects with similar semantics appear together in the plot, even though they appear in different visual contexts. The clusters are similar across all layers.
+
+![](images/0871eaeec1b3a2cefed374b4afe74f4472dcfa012b96b66ba788941922ad6130.jpg)
+
+![](images/634b8626bd86b8a751eb0a1a13aede7e31b6b2493a3002af59c1e545ae2c8908.jpg)
+
+a query for dense passage retrieval. MuRAG (Chen et al., 2022) proposes a multimodal image-text memory bank to help models answer challenging knowledge-based visual questions such as "What shape is the pediment on top of the white house?" REVEAL (Hu et al., 2023) and RA-VQA (Lin and Byrne, 2022) learns a trainable multimodal retriever similar to REALM (Guu et al., 2020) during pretraining to fetch relevant documents to answer questions, achieving state of the art performance on datasets such as VQAv2 (Antol et al., 2015) and OKVQA (Schwenk et al., 2022). To the best of our knowledge, we are the first to integrate region-level retrieval with LLMs, in which the multimodal documents are indexed by object-level visual features.
+
+# 7 Conclusion
+
+We present a simple approach to insert object level visual embeddings into large language model decoders, enabling object level reasoning with flexible prompt structure. Our object encoder compresses fine-grained region level information into a single vector, enabling in-context prompting with objects from multiple images and more efficient training and inference. In addition, we introduce the idea of region retrieval, which allows for precise queries free of image background noise and rapid generalization to rare and unseen objects with no parameter updates. We hope our method may help researchers design vision language models which can adapt to their needs by simply updating the retrieval set or object encoder, while also being responsive to varying user intents using LLM prompting techniques.
+
+# 8 Limitations
+
+While our approach provides a flexible way for users to supply object-level prompts, it does not output bounding boxes or other region-level grounding. This may be addressed in future research by further finetuning on region-level instruction tuning data as done in FERRET (You et al., 2023), GLAMM (Rasheed et al., 2023), and other region-level VLM pretraining. At the moment, we also do not explore generic image tasks such as VQA or image captioning. However, a potential solution is to use our object encoder to connect to existing VLMs (e.g. LLaVA) which excel at these tasks. Lastly, our results in the retrieval setting depend on the quality of the retrieved examples. Curating a high-quality retrieval set at the object-level can be challenging. However, existing tools such as GLIPv2 (Zhang et al., 2022) allows for semi-automatic generation of region-level data as used in KOSMOS-2 (Peng et al., 2024) in developing the GRIT dataset.
+
+# 9 Ethical Considerations
+
+Biases From Pretrained LLMs Since our model uses existing pretrained LLMs such as Llama 2 or GPT2, it may inherent some of the social biases or toxicity acquired during their pretraining stages. While Llama 2 undergoes extensive alignment to ideal human values through reinforcement learning from human feedback (RLHF) (Griffith et al., 2013), some of these toxic behaviors may still be present in the morally aligned model. We make sure to only use images of common objects in the COCO dataset, which do not contain any of these biases or violent scenes to the best of our knowledge. Nevertheless, further testing to ensure the
+
+impartiality of the model may be necessary before deploying in widespread technologies.
+
+Domain Adaptation Some of our experiments involve evaluating our model in a data-scarce domain in a zero shot manner with in-context prompting. While this is a promising direction for efficient domain adaptation, users should take caution in directly using model prediction, as this is a challenging task due to distribution shift. We encourage human-in-the-loop interaction to sanity check the outputs. Different from other ICL prompting methods, we provide retrieved examples and similarity scores which can help determine the trustworthiness of the model prediction, which may be valuable for high-risk domains such as medicine.
+
+# Acknowledgement
+
+Ossowski and Hu are supported by the Wisconsin Alumni Research Foundation and the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB033782. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
+
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+
+# A Appendix
+
+Table 3 contains all the prompts we use to instruct the LLM decoder.
+
+# B Qualitative Examples
+
+Here we include several selected examples showcasing the strengths and weaknesses of our approach.
+
+Visual Concept Generalization In Figure 9 we demonstrate more examples of rapid generalization to new visual concepts. Many existing methods confidently predict concepts from their pretraining, while ours can predict new concepts on the fly.
+
+Scene Content Awareness Even though our object representation involves masking out image patch features from other parts of the image, we have observed that the object vector still contains information about its surroundings. Figure 10 illustrates this phenomenon, where OLIVE can include the cow in its description, despite not including any image patches corresponding to the cow in the user selection.
+
+Patch level Detail Our method also can identify and describe small objects at the patch level. Figure 11 shows an example of object classification on smaller objects.
+
+Describing Partially Visible Objects We notice that our model can make mistakes when describing occluded or partially visible objects as seen in Figure 12. We hypothesize that the training data of refCOCOg does not include these kinds of image regions, which also limits its availability in retrieval data. This may be addressed with larger-scale pretraining on data such as GRIT which likely includes more occluded objects.
+
+Errors in Detailed Description While our model can identify the object most of the time, it sometimes gets minor details incorrect. For example, the colors of a shirt or other piece of clothing are seen in Figure 12. This may be due to the extreme compression we learn into a single vector. Future work may consider visually resampling the object features into more than just 1 latent vector for detailed captioning, but still use the single vector representation for retrieval.
+
+# C Dataset Information
+
+Table 4 provides more details on the dataset splits used in our training and evaluation. Our COCO train and validation splits are slightly smaller than normal because of our approach of using segmentation masks. We decide to omit some excessively small segmentations which account for less than  $1\%$  of the data. For tasks that require training (COCO and refCOCOg), we use the train split of the COCO object detection dataset as our retrieval data. We make sure to omit the closest match when training object detection on COCO with retrieval to avoid label leakage. We also confirm that no images are repeated in the validation split from the training split for both datasets.
+
+# D Referring Object Classification
+
+This task requires the LLM to predict the object class label given a ground truth input annotation (e.g. bounding box, segmentation, etc). We follow a similar evaluation protocol used in (Zhong et al., 2022) and (Zareian et al., 2021), in which the ground truth annotation is supplied to avoid localization error. Table 5 shows the overall referring object classification accuracy and  $\mathrm{mAP}^3$  for our methods. We observe several findings. First, although retrieved examples help with domain adaptation and rare objects, it does not improve the overall in-domain performance. Second, both the LLama 2 and GPT2 baseline have similar performances on the task, suggesting that even smaller models can learn vision-language grounding. Lastly, even our retrieval-only baseline, which requires no training, has better accuracy than some parameterized methods such as CLIP.
+
+# E Multi-Task Model
+
+We also explore the possibility of training a multi-task model using a similar curriculum learning strategy to LLaVA (Liu et al., 2023). We first train the model on the referring object classification task to perform the object-word level alignment. The model is then trained on the referring expression generation task, and finally on an object instruction following dataset (Cai et al., 2023) with many different tasks. For each stage of training, we formulate the task in an instruction-following manner through the prompts in Table 3. This allows the
+
+<table><tr><td>Task</td><td>ICL Prompt for Retrieved Examples</td><td>Vanilla Prompts</td></tr><tr><td rowspan="2">Object Classification</td><td>You are a helpful vision assistant trained to help people analyze images. The top [k] related objects are: [obj] is a [label] with confidence [score] [obj] is a [label] with confidence [score]</td><td rowspan="2">[obj] What is this? Answer in 1-2 words [obj] What is this object? Answer with a short word or phrase. [obj] Identify this object. Here is an object [obj]. What is this? Answer with a short word or phrase.</td></tr><tr><td>[vanilla prompt]</td></tr><tr><td rowspan="2">Region Description</td><td>You are a helpful vision assistant trained to help people analyze images. The top [k] related objects are: [obj] is a [label] with confidence [score] [obj] is a [label] with confidence [score]</td><td rowspan="2">[obj] Briefly describe this image region. [obj] Describe this part of the image. [obj] Share some details about about what&#x27;s happening here in the image. [obj] Break down what you see in this particular part of the picture. [obj] Describe what you notice in this area of the picture.</td></tr><tr><td>[vanilla prompt]</td></tr></table>
+
+Table 3: Collection of the prompts we use to guide the LLM decoder generation. For a given task, we sample one of the vanilla prompts uniformly at random. Text in brackets indicates variables whose value is dynamically filled in.
+
+![](images/4d524f2f234114ed0531a3420bd2e190b92dfa798794f3e5f553e93a47b56c37.jpg)  
+Figure 9: Examples of rapid adaptation to unseen visual concepts during training. Non-retrieval-based methods such as LLaVA often fail to generalize, instead predicting animals seen during pre-training.
+
+![](images/ac61c4808fe2af1e870897cdf95d82606d88f34a539d2e0f0c66e97f83eb1af4.jpg)
+
+![](images/a27b128d2b0ac8584e69950ff09dc92e1ada5d816fa029fe57d31d9845d63344.jpg)  
+Figure 10: Example of scene content awareness of the object embeddings. Although neither selection includes any part of the cow, the model can still mention the cow in its description.
+
+![](images/27b0e061d23e1a307695e9c163036da404fff3c89cdca2e0bacc34d878f1da2b.jpg)
+
+![](images/b6f0e81dc123d4d3789a066b85b08de2d1562c55be4e6c94fe8b0c8cca71ed15.jpg)  
+Figure 11: Object classification on small objects.
+
+![](images/04644cc407978228d344813010473a6fab6e290922fb4eadb14d1cdd217ee06e.jpg)  
+Figure 12: Failure cases of our model. The model sometimes struggles with occluded/partially missing objects and may mix up some fine-grained details about objects.
+
+<table><tr><td>Dataset</td><td>Train Split</td><td>Validation Split</td><td>Retrieval Set Train Split</td><td>Retrieval Set Test Split</td><td>Number of Classes</td></tr><tr><td>COCO</td><td>849586</td><td>36320</td><td>849586</td><td>849586</td><td>80</td></tr><tr><td>refCOCOg</td><td>44822</td><td>5000</td><td>849586</td><td>849586</td><td>-</td></tr><tr><td>CXR8</td><td>-</td><td>824</td><td>-</td><td>160</td><td>8</td></tr></table>
+
+Table 4: More details about the datasets and splits we use in our experiments. For COCO and refCOCOg, we use the train split of the COCO dataset as the retrieval data. We select the first 20 examples of each pathology as the retrieval set for the zero shot evaluation on CXR8.
+
+![](images/d7963ff651c8e3ca82d29caf7a5b03f4c9b2f429899a6d3b8e845a80da848e2c.jpg)  
+Figure 13: When trained on multiple tasks and object instruction following data, OLIVE is able to respond to varying user intents.
+
+<table><tr><td>Method Type</td><td>Method</td><td>Accuracy</td><td>mAP</td></tr><tr><td rowspan="4">Classification</td><td>OLIVE-R</td><td>64.1</td><td>40.5</td></tr><tr><td>CLIP ViT-L/14</td><td>40.9</td><td>45.1</td></tr><tr><td>RegionCLIP RN50</td><td>-</td><td>61.4</td></tr><tr><td>OVR</td><td>-</td><td>44.5</td></tr><tr><td rowspan="4">Generative</td><td>OLIVE-G (GPT2)</td><td>76.6</td><td>60.4</td></tr><tr><td>OLIVE-G (Llama 2)</td><td>76.8</td><td>60.3</td></tr><tr><td>OLIVE-RG (GPT2)</td><td>74.8</td><td>57.5</td></tr><tr><td>OLIVE-RG (Llama 2)</td><td>74.1</td><td>56.2</td></tr></table>
+
+model to be responsive to many different user intents (Figure 13)
+
+# F Referring Expression Generation
+
+Table 5: Performances on the referring object classification task with different levels of context on the COCO dataset. For each method type, the highest values for each metric are bolded.  
+
+<table><tr><td>Method</td><td>METEOR</td><td>CIDEr</td></tr><tr><td>OLIVE-G (Llama 2)</td><td>16.5</td><td>64.0</td></tr><tr><td>OLIVE-RG (Llama 2)</td><td>16.6</td><td>67.7</td></tr><tr><td>OLIVE-G (GPT2)</td><td>16.4</td><td>70.9</td></tr><tr><td>OLIVE-RG (GPT2)</td><td>17.0</td><td>75.0</td></tr><tr><td>SLR (Yu et al., 2017)</td><td>15.4</td><td>59.2</td></tr><tr><td>SLR+Rerank (Yu et al., 2017)</td><td>15.9</td><td>66.2</td></tr><tr><td>GLAMM (Rasheed et al., 2023)</td><td>16.2</td><td>105.0</td></tr><tr><td>GRIT (Wu et al., 2022)</td><td>15.2</td><td>71.6</td></tr><tr><td>Kosmos 2 (zero shot)</td><td>12.2</td><td>60.3</td></tr><tr><td>Kosmos 2 (fewshot k = 2)</td><td>13.8</td><td>62.2</td></tr><tr><td>Kosmos 2 (fewshot k = 4)</td><td>14.1</td><td>62.2</td></tr><tr><td>Flamingo-9B (zero shot)</td><td>9.2</td><td>34.3</td></tr><tr><td>Flamingo-9B (fewshot k = 2)</td><td>10.2</td><td>36.2</td></tr><tr><td>Flamingo-9B (fewshot k = 4)</td><td>12.3</td><td>39.6</td></tr></table>
+
+We study our model's overall performance on referring expression generation by quantitatively evaluating our model on the RefCOCOg validation
+
+set shown in Table 6. Several findings can be observed. First, including retrieved multimodal documents results in slightly better performance. Second, the size of the LLM can be modified without much performance change, with GPT2 performing slightly better than Llama 2. Third, having global image context contained in the object representation is important, as methods that crop the image region (e.g. Flamingo) perform worse.
+
+# G Training Hyperparameters
+
+We provide the detailed training hyperparameters in Table 7.
+
+Table 6: Referring expression generation on the refCOCOg validation split. Our approach has competitive performance compared to other notable methods which also offer multimodal in-context prompting.  
+
+<table><tr><td>Hyperparameter</td><td>Classification</td><td>Generation</td></tr><tr><td>Epochs</td><td>1</td><td>5</td></tr><tr><td>Batch Size</td><td>4</td><td>4</td></tr><tr><td>Training Steps</td><td>~200,000</td><td>~56,030</td></tr><tr><td>Learning Rate</td><td>2e-5</td><td>2e-5</td></tr><tr><td>Optimizer</td><td>Adam</td><td>Adam</td></tr><tr><td>GPU Used</td><td>GTX 3090</td><td>GTX 3090</td></tr><tr><td>Train Time (hours)</td><td>24</td><td>7.5</td></tr></table>
+
+Table 7: Details about the hyperparameters we use for (1) Referring Object Classification (Classification) and (2) Referring Expression Generation (Generation) in our experiments.

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+# 0 : Accelerating the Science of Language Models
+
+Dirk Groeneveld $^{\alpha}$  Iz Beltagy $^{\alpha}$
+
+Pete Walsh $^{\alpha}$  Akshita Bhagia $^{\alpha}$  Rodney Kinney $^{\alpha}$  Oyvind Tafjord $^{\alpha}$
+
+Ananya Harsh Jha $^{\alpha}$  Hamish Ivison $^{\alpha\beta}$  Ian Magnusson $^{\alpha}$  Yizhong Wang $^{\alpha\beta}$
+
+Shane Arora $^{\alpha}$  David Atkinson $^{\alpha}$  Russell Authur $^{\alpha}$  Khyathi Raghavi Chandu $^{\alpha}$
+
+Arman Cohan $^{\gamma \alpha}$  Jennifer Dumas $^{\alpha}$  Yanai Elazar $^{\alpha \beta}$  Yuling Gu $^{\alpha}$
+
+Jack Hessel $^{\alpha}$  Tushar Khot $^{\alpha}$  William Merrill $^{\delta}$  Jacob Morrison $^{\alpha}$
+
+Niklas Muennighoff Aakanksha Naik $^{\alpha}$  Crystal Nam $^{\alpha}$  Matthew E. Peters $^{\alpha}$
+
+Valentina Pyatkin $^{\alpha \beta}$  Abhilasha Ravichander $^{\alpha}$  Dustin Schwenk $^{\alpha}$  Saurabh Shah $^{\alpha}$
+
+Will Smith $^{\alpha}$  Emma Strubell $^{\alpha \mu}$  Nishant Subramani $^{\alpha}$  Mitchell Wortsman $^{\beta}$
+
+Pradeep Dasigi $^{\alpha}$  Nathan Lambert $^{\alpha}$  Kyle Richardson $^{\alpha}$
+
+Luke Zettlemoyer $^{\beta}$  Jesse Dodge $^{\alpha}$  Kyle Lo $^{\alpha}$  Luca Soldaini $^{\alpha}$
+
+Noah A. Smith $^{\alpha \beta}$  Hannaneh Hajishirzi $^{\alpha \beta}$
+
+$^{\alpha}$ Allen Institute for Artificial Intelligence
+
+$^{\beta}$ University of Washington  $^{\gamma}$ Yale University
+
+$^{\delta}$ New York University  $\mu$ Carnegie Mellon University
+
+olmo@allenai.org
+
+# Abstract
+
+Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.
+
+# 1 Introduction
+
+Language models have been at the center of NLP technologies for many years (Rosenfeld, 2000; Ben
+
+gio et al., 2003; Mikolov et al., 2013; Peters et al., 2018; Brown et al., 2020). Recently, due to large-scale pretraining and human annotation for alignment, they have become commercially valuable (OpenAI, 2023). However, as their commercial value has increased, the largest models have become gated behind proprietary interfaces, with important details left undisclosed.
+
+We believe that full access to open language models for the research community is critical to the scientific study of these models, their strengths and weaknesses, and their biases and risks. Accordingly, we introduce OLMo, a powerful, truly open language model alongside open training data, training and evaluation code, intermediate model checkpoints, and training logs.
+
+Recent LM releases have varied in their degree of openness. For example, Mixtral 8x7B provided model weights and a brief report (Jiang et al., 2024), while LLaMA came with in-depth adaptation training instructions (Touvron et al., 2023b), and Mosaic Pretrained Transformer came with many details, including the dataset distribution, though not the data itself (MosaicML NLP Team,
+
+2023). Falcon's pretraining data was partially released (Almazrouei et al., 2023), and the most open models—the Pythia suite (Biderman et al., 2023) and BLOOM (BigScience et al., 2022)—released training code, model checkpoints, data, and more.
+
+With OLMo, we release the whole framework from data to training to evaluation tools: multiple training checkpoints across multiple hardware types, training logs, and exact datasets used, with a permissive license. We are not the only team to do this; recent work from LLM360 targets similar goals (Liu et al., 2023). OLMo narrows the gap from their models to state-of-the-art capabilities of models like Llama 2. This project has benefited from lessons learned from all of these previous efforts with their varying degrees of openness, and we believe that a large, diverse population of open models is the best hope for scientific progress on understanding language models and engineering progress on improving their utility.
+
+The OLMo framework encompasses the tools and resources required for building and researching language models. For training and modeling, it includes full model weights, training code, training logs, and inference code. The released model includes four variants of our language model at the 7B scale corresponding to different architectures, optimizers, and training hardware, and one model at the 1B scale, all trained on at least 2T tokens. We also release hundreds of intermediate checkpoints available as revisions on HuggingFace. For dataset building and analysis, the full training data used for these models is openly available (Dolma; Soldaini et al., 2024), including code that produces the training data, and tools for analyzing pretraining data (Elazar et al., 2024). For evaluation, we build on Catwalk (Groeneveld et al., 2023) for downstream evaluation and Paloma (Magnusson et al., 2023) for perplexity-based evaluation. For adaptation, we use Open Instruct (Ivison et al., 2023; Wang et al., 2023) to train with instruction and feedback data. Finally, all code and weights are released under the Apache 2.0 License.
+
+With this release, we hope to catalyze research into as-yet poorly understood aspects of these models, for example, the relationship between pretraining data and model capabilities, the impact of design and hyperparameter choices, and various optimization methods and their impact on model training. In addition, we report on the lessons learned
+
+and important details necessary to successfully train language models at this scale.
+
+# 2 OLMo Framework
+
+This section describes the OLMo framework, consisting of the OLMo models (Section 2.1), our pretraining dataset, Dolma (Section 2.2), and our evaluation framework (Section 2.4).
+
+# 2.1 OLMo Model and Architecture
+
+We adopt a decoder-only transformer architecture based on (Vaswani et al., 2017), and deliver 1B and 7B variants as described in Table 1. Our specific architecture includes several improvements over the vanilla transformer from (Vaswani et al., 2017) following other recent large language models like PaLM (Chowdhery et al., 2022), the LLaMA family (Touvron et al., 2023a,b), OpenLM (Gururangan et al., 2023), and Falcon (Almazrouei et al., 2023). See Table 5 in Appendix A for a comprehensive comparison of our 7B architecture to the similarly-sized models from these other families.
+
+We generally select hyperparameters by optimizing for training throughput on our hardware while minimizing the risk of loss spikes and slow divergence. We ablate choices through our in-loop evaluation setting, given available computational sources (Section 2.4). Our main changes over the vanilla transformer architecture can be summarized as follows:
+
+1. No biases. Following LLaMA, PaLM, and others, we exclude all bias terms from our architecture in order to improve training stability.  
+2. Non-parametric layer norm. We use the non-parametric formulation of layer norm (Ba et al., 2016) in which there is no affine transformation within the norm, i.e., no "adaptive gain" (or bias). We believe this was the safest option and it was also the fastest compared to the other variants we considered: parametric layer norm and RMSNorm (Zhang and Senrich, 2019).  
+3. SwiGLU activation function. Like LLaMA, PaLM, and others we use the SwiGLU activation function (Shazeer, 2020) instead of ReLU, and following LLaMA the activation hidden size is approximately  $\frac{8}{3}d$ , but increased to the closest multiple of 128 (e.g. 11,008 for our 7B model) to improve throughput. $^2$
+
+<table><tr><td>Size</td><td>L</td><td>D</td><td>H</td><td>Tokens</td><td>Peak LR</td><td>Warmup</td><td>Weight Tying</td><td>Batch size</td></tr><tr><td>1B</td><td>16</td><td>2048</td><td>16</td><td>2T</td><td>4.0E-4</td><td>2000 steps</td><td>yes</td><td>~4M</td></tr><tr><td>7B</td><td>32</td><td>4086</td><td>32</td><td>2.46T</td><td>3.0E-4</td><td>5000 steps</td><td>no</td><td>~4M</td></tr></table>
+
+4. Rotary positional embeddings (RoPE). Like LLaMA, PaLM, and others we replace absolute positional embeddings with rotary positional embeddings (RoPE; Su et al., 2021).  
+5. Vocabulary. We use a modified version of the BPE-based tokenizer from GPT-NeoX-20B (Black et al., 2022) with additional tokens for masking personal identifiable information (PII). The final vocabulary size is 50,280. However, to maximize training throughput we increase the size of the corresponding embedding matrix in our model to 50,304 to be a multiple of 128.
+
+# 2.2 Pretraining Data: Dolma
+
+Despite progress in access to model parameters, pretraining datasets are still not as open. Pretraining data are often not released alongside open models (let alone closed models) and documentation about such data is often lacking in detail that would be needed to reproduce or fully understand the work. This has made it difficult to support certain threads of language model research, such as understanding how training data impacts model capabilities and limitations. To facilitate open research on language model pretraining, we built and released our pretraining dataset, Dolma—a diverse, multisource corpus containing trillions of tokens across billions of documents acquired from different data sources that are (1) commonly seen in large-scale language model pretraining and (2) accessible to the general public (Soldaini et al., 2024). Table 2 provides a high-level overview of the amount of data from each source.
+
+Dolma is built using a pipeline of (1) language filtering, (2) quality filtering, (3) content filtering, (4) dedduplication, (5) multi-source mixing, and (6) tokenization. We refer the reader to the Dolma report (Soldaini et al., 2024) for more details about its design principles, details about its construction, and a more detailed summary of its contents. The
+
+is half the size of the input. So technically our inputs to SwiGLU have a dimensionality of  $2 \times 11,008 = 22,016$  for our 7B model.
+
+Table 1: OLMo model sizes, number of training tokens, and optimizer settings. In all runs, the optimizer was AdamW, with betas of 0.9 and 0.95, and an epsilon of 1.0E-5. L is number of layers, D is hidden dimension, H is number of attention heads, WD is weight decay.  
+
+<table><tr><td>Source</td><td>Type</td><td>UTF-8 bytes (GB)</td><td>Docs (millions)</td><td>Tokens (billions)</td></tr><tr><td>Common crawl</td><td>web pages</td><td>9,812</td><td>3,734</td><td>2,180</td></tr><tr><td>GitHub</td><td>code</td><td>1,043</td><td>210</td><td>342</td></tr><tr><td>Reddit</td><td>social media</td><td>339</td><td>377</td><td>80</td></tr><tr><td>Semantic Scholar</td><td>papers</td><td>268</td><td>38.8</td><td>57</td></tr><tr><td>Project Gutenberg</td><td>books</td><td>20.4</td><td>0.056</td><td>5.2</td></tr><tr><td>Wikipedia</td><td>encyclopedia</td><td>16.2</td><td>6.2</td><td>3.7</td></tr><tr><td colspan="2">Total</td><td>11,519</td><td>4,367</td><td>2,668</td></tr></table>
+
+Table 2: Composition of Dolma. Tokens counts are based on the GPT-NeoX tokenizer.
+
+report provides additional analyses and experimental results from training language models on intermediate states of Dolma to share what we learned about important data curation practices, including the role of content or quality filters, dedduplication, and mixing data from multiple sources. We keep documents from each source separate, both during curation as well as in the final release. We open-sourced our high-performance data curation tools; this toolkit can be used to further experiment on Dolma, reproduce our work, and enable fast and easy curation of pretraining corpora. Finally, we also open-sourced our WIMBD tool (Elazar et al., 2024) to help with dataset analysis.
+
+# 2.3 Adaptation
+
+Pretrained models are not always used as-is, but rather further finetuned to improve their performance, safety, and usability. Often models are first trained to follow instructions (Mishra et al., 2022; Wei et al., 2022; Sanh et al., 2022), and then further trained on human preferences (Ouyang et al., 2022) to improve the quality of their generations. We showcase the efficacy of using OLMo as a base model for further fine-tuning by training OLMo to be a general chat assistant following the TULU data and training setup (Ivison et al., 2023). This involves first performing instruction finetuning with a mixture of distilled and human-written instruction data and then further aligning the model with
+
+distilled preference data using Direct Preference Optimization (DPO) (Rafailov et al., 2023).
+
+# 2.4 Evaluation
+
+We perform base model evaluation at two stages: online evaluation to make decisions for model design and offline evaluation to evaluate model checkpoints. For the offline stage, we use the Catwalk framework (Groeneveld et al., 2023), a publicly available evaluation tool with access to a wide range of datasets and task formats, to perform downstream evaluation as well as intrinsic language modeling evaluation on the perplexity benchmark Paloma (Magnusson et al., 2023).
+
+For both downstream and perplexity evaluation, we use our fixed evaluation pipeline to compare results against publicly available models. We also report a separate evaluation of our adapted model.
+
+In-Loop Training Ablations Throughout model training, we perform downstream evaluations to make decisions around model architecture, initialization, optimizers, learning rate schedule, and data mixtures. We call this our online evaluation as it runs in-loop every 1000 training steps (or  $\sim 4\mathrm{B}$  training tokens) and provides an early and continuous signal on the quality of the model being trained. These evaluations rely on many of the core tasks and experiment settings used for our offline evaluation detailed in Section 4.1, which also mirrors the task and evaluation structure of the EleutherAI eval harness (Gao et al., 2023).
+
+Downstream Evaluation Following much previous work (Brown et al., 2020; Black et al., 2022; Touvron et al., 2023a,b, inter alia), we report zero-shot performance on a set of downstream tasks. Our evaluation suite consists of 8 core tasks corresponding closely to the commonsense reasoning task set reported by Touvron et al. (2023a) and Touvron et al. (2023b) (see Table 3 for a list of tasks). Given the scale of the models being evaluated, such tasks were selected at the beginning of model development due to their naturalness (e.g., all can formulated as text completion scoring tasks) and ability to provide meaningful signals throughout training (see Figure 1).
+
+Intrinsic Language Modeling Evaluation To measure how OLMo fits distributions of language beyond held-out training data, we use Paloma (Magnusson et al., 2023), a new perplexity benchmark that includes 585 different domains of text.
+
+Domains range from nytimes.com to r/depression on Reddit and are drawn from 18 separate data sources, such as C4 (Raffel et al., 2020), in stratified samples. This allows for more equal inclusion of text domains that are under-represented in their source corpora.
+
+We aim not just to compare OLMo against other models for best performance, but also to demonstrate how it enables fuller and more controlled scientific evaluations. OLMo-7B is the largest LM with explicit decontamination for perplexity evaluation. Following the approach described in Paloma, we remove any pretraining document with paragraphs leaked from Paloma evaluation data. Without decontamination, other models risk underestimating perplexity (i.e., overestimating the model's out-of-sample fit). We also release intermediate checkpoints, allowing richer comparisons with two other models that release checkpoints, Pythia-6.9B (Biderman et al., 2023) and RPJ-INCITE-7B (Together Computer, 2023) (see Figure 2).
+
+Adaptation Evaluation We also evaluate OLMo after instruction fine-tuning and DPO training using the TÜLU evaluation suite proposed in Wang et al. (2023); Ivison et al. (2023). We focus on evaluations around model chat capabilities and safety in order to showcase the efficacy of using OLMo as a base for further fine-tuning.
+
+# 3 Training OLMo
+
+This section describes our pretraining setup, including our distributed training framework (Section 3.1), optimizer (Section 3.2), data preparation (Section 3.3), and hardware (Section 3.4).
+
+# 3.1 Distributed Training Framework
+
+We train our models using the ZeRO optimizer strategy (Rajbhandari et al., 2019) via PyTorch's FSDP framework (Zhao et al., 2023), which reduces memory consumption by sharding the model weights and their corresponding optimizer state across GPUs. At the 7B scale, this enables training with a micro-batch size of 4096 tokens per GPU on our hardware (see Section 3.4). For OLMo-1B and -7B models, we use a constant global batch size of approximately 4M tokens (2048 instances, each with a sequence length of 2048 tokens).
+
+To improve throughput, we employ mixed-precision training (Micikevicius et al., 2017) through FSDP's built-in settings and PyTorch's amp module. The latter ensures that certain operations
+
+like the softmax always run in full precision to improve stability, while all other operations run in half-precision with the bffloat16 format. Under our specific settings, the sharded model weights and optimizer state local to each GPU are kept in full precision. The weights within each transformer block are only cast to bffloat16 when the full-sized parameters are materialized on each GPU during the forward and backward passes. Gradients are reduced across GPUs in full precision.
+
+# 3.2 Optimizer
+
+We use the AdamW optimizer (Loshchilov and Hutter, 2019) with the hyperparameters shown in Table 1. For all model sizes, we warm up the learning rate over 5000 steps (~21B tokens) and then decay it linearly from there down to a tenth of the peak learning rate over the remainder of training. After the warm-up period, we clip gradients such that the total  $l^2$ -norm of the parameter gradients<sup>3</sup> does not exceed 1.0. Table 5 gives a comparison of our optimizer settings at the 7B scale to those of other recent LMs that also used AdamW.
+
+# 3.3 Data
+
+We built our training dataset out of a 2T-token sample from our open dataset, Dolma (Soldaini et al., 2024), which we describe in Section 2.2. The tokens from every document are concatenated together after appending a special EOS token to the end of each document, and then we group consecutive chunks of 2048 tokens to form training instances. The training instances are shuffled in the exact same way for each training run. The data order and exact composition of each training batch can be reconstructed from the artifacts we release.
+
+All of our released models have been trained to at least 2T tokens (a single epoch over our training data), and some have been trained beyond that by starting a second epoch over the data with a different shuffling order. The impact of repeating this small amount of data should be negligible according to prior work (Muennighoff et al., 2023).
+
+# 3.4 Hardware
+
+In order to verify that our codebase could be used on both NVIDIA and AMD GPUs without any loss
+
+in performance, we trained models on two different clusters:
+
+- LUMI: Provided by the LUMI supercomputer, we used up to 256 nodes on this cluster, where each node consists of  $4 \times$  AMD MI250X GPUs with 128GB of memory and 800Gbps of interconnect.  
+- MosaicML: Provided by MosaicML (Databricks), we used 27 nodes on this cluster, where each node consists of 8x NVIDIA A100 GPUs with 40GB of memory and 800Gbps interconnect.
+
+Despite minor differences in batch size to optimize for training throughput, both runs resulted in nearly identical performance on our evaluation suite by 2T tokens.
+
+# 4 Results
+
+The checkpoint used for evaluating OLMo-7B is trained until 2.46T tokens on the Dolma (Soldaini et al., 2024) dataset with a linear learning rate decay schedule mentioned in Section 3.2. In our experiments, we find that tuning this checkpoint further on the Dolma dataset for 1000 steps with the learning rate linearly decayed to 0 boosts model performance on perplexity and end-task evaluation suites described in Section 2.4. We compare OLMo with other publicly available models including LLaMA7B (Touvron et al., 2023a), Llama-2-7B (Touvron et al., 2023b), MPT-7B (MosaicML NLP Team, 2023), Pythia-6.9B (Biderman et al., 2023), Falcon7B (Almazrouei et al., 2023) and RPJ-INCITE-7B (Together Computer, 2023).
+
+# 4.1 Downstream evaluation
+
+Setup Our core downstream evaluation suite (see Table 3) consists of: arc (both arc_easy and arc_challenge) (Clark et al., 2018), boolq (Clark et al., 2019), openbookqa (Mihaylov et al., 2018), sciq (Welbl et al., 2017), hellaswag (Zellers et al., 2019), piqa (Bisk et al., 2020), and winogrande (Sakaguchi et al., 2021). In Appendix C, we also report results on an additional set of auxiliary tasks outside of our core evaluation set that we found to have less stable performance trends (see Figure 4).
+
+<table><tr><td>Models</td><td>arc challenge</td><td>arc easy</td><td>boolq</td><td>hella-swag</td><td>open bookqa</td><td>piqa</td><td>sciq</td><td>wino-grande</td><td>avg.</td></tr><tr><td>StableLM 1.6B</td><td>43.8</td><td>63.7</td><td>76.6</td><td>68.2</td><td>45.8</td><td>74.0</td><td>94.7</td><td>64.9</td><td>66.5</td></tr><tr><td>Pythia 1B</td><td>33.1</td><td>50.2</td><td>61.8</td><td>44.7</td><td>37.8</td><td>69.1</td><td>86.0</td><td>53.3</td><td>54.5</td></tr><tr><td>TinyLlama 1.1B</td><td>34.8</td><td>53.2</td><td>64.6</td><td>58.7</td><td>43.6</td><td>71.1</td><td>90.5</td><td>58.9</td><td>59.4</td></tr><tr><td>OLMo-1B</td><td>34.5</td><td>58.1</td><td>60.7</td><td>62.5</td><td>46.4</td><td>73.7</td><td>88.1</td><td>58.9</td><td>60.4</td></tr><tr><td>Falcon-7B</td><td>47.5</td><td>70.4</td><td>74.6</td><td>75.9</td><td>53.0</td><td>78.5</td><td>93.9</td><td>68.9</td><td>70.3</td></tr><tr><td>LLaMA 7B</td><td>44.5</td><td>67.9</td><td>75.4</td><td>76.2</td><td>51.2</td><td>77.2</td><td>93.9</td><td>70.5</td><td>69.6</td></tr><tr><td>Llama 2 7B</td><td>48.5</td><td>69.5</td><td>80.2</td><td>76.8</td><td>48.4</td><td>76.7</td><td>94.5</td><td>69.4</td><td>70.5</td></tr><tr><td>MPT-7B</td><td>46.5</td><td>70.5</td><td>74.2</td><td>77.6</td><td>48.6</td><td>77.3</td><td>93.7</td><td>69.9</td><td>69.8</td></tr><tr><td>Pythia 6.9B</td><td>44.1</td><td>61.9</td><td>61.1</td><td>63.8</td><td>45.0</td><td>75.1</td><td>91.1</td><td>62.0</td><td>63.0</td></tr><tr><td>RPJ-INCITE-7B</td><td>42.8</td><td>68.4</td><td>68.6</td><td>70.3</td><td>49.4</td><td>76.0</td><td>92.9</td><td>64.7</td><td>66.6</td></tr><tr><td>OLMo-7B</td><td>48.5</td><td>65.4</td><td>73.4</td><td>76.4</td><td>50.4</td><td>78.4</td><td>93.8</td><td>67.9</td><td>69.3</td></tr></table>
+
+Table 3: Zero-shot evaluation of OLMo-1B and OLMo-7B, with other publicly available comparable model checkpoints on 8 core tasks from the downstream evaluation suite described in Section 2.4. For OLMo-7B, we report results for the 2.46T token checkpoint.
+
+In all cases, we perform zero-shot evaluation using the rank classification approach popularized by Brown et al. (2020). Under this approach, candidate text completions (e.g., different multiple-choice options) are ranked by likelihood (usually normalized by some normalization factor), and prediction accuracy is reported. While Catwalk implements several common likelihood normalization strategies, including normalizing by number of tokens (per-token normalization; Brown et al., 2020; Liang et al., 2022), by number of characters (per-character normalization; Gao et al., 2023), as well as incorporating an answer's unconditional likelihood (Brown et al., 2020), we selected the normalization strategies for each dataset separately. Specifically, we used unconditional normalization for arc and openbookqa, per-token normalization for hellaswag, piqa, and winogrande and no normalization for boolq, and sciq (i.e., tasks formulated as single token prediction tasks).
+
+Results Table 3 summarizes the result of zero-shot evaluation of OLMo and compares against other publicly available models of comparable size. We report results on 8 core tasks from our evaluation suite described in Section 2.4. On aggregate, OLMo-7B is competitive against all the comparable models. We include the comparison to StableLM 1.6B, but note that it is significantly larger, and was trained on unknown data.
+
+In Figure 1 we plot the accuracy score progression of 8 core end-tasks. All tasks, except OBQA, show an upward trend in accuracy numbers as
+
+OLMo-7B is trained on more tokens. A sharp upward tick in accuracy of many tasks between the last and the second to last step shows us the benefit of linearly reducing the LR to 0 over the final 1000 training steps. See Table 7 in Appendix C for additional evaluation results and discussion.
+
+# 4.2 Intrinsic language modeling evaluation
+
+Setup For intrinsic evaluations, Paloma proposes a range of analyses, from inspection of performance in each domain separately to more summarized results over combinations of domains. We report results at two levels of granularity: the aggregate performance over 11 of the 18 sources in Paloma as in (Magnusson et al., 2023), as well as more fine-grained results over each of these sources individually. This particular subset of 11 sources from Paloma excludes sources that are not publicly available, involve fringe or toxic text, or consist of code data not supported by Paloma's decontamination approach. This leaves C4 (Raffel et al., 2020), mC4-en (Chung et al., 2023), Wikitext 103 (Merit et al., 2016), Penn Treebank (Marcus et al., 1999; Nunes, 2020), RedPajama (Together Computer, 2023), Falcon-RefinedWeb (Penedo et al., 2023), Dolma (Soldaini et al., 2024), M2D2 S2ORC (Reid et al., 2022), M2D2 Wikipedia (Reid et al., 2022), C4 100 domains (Chronopoulou et al., 2022), and Dolma 100 Subreddits (Soldaini et al., 2024). To allow for a fair comparison between models with different vocabularies, we report bits per byte as defined by Gao et al. (2020) over the test sets of these sources.
+
+![](images/7acc27e0000edc495db87239e1d1695b39497b0dd2747f50410177087b0a8048.jpg)
+
+![](images/223f10e7ba2863887f66cd557eb4e9bb6d7e968b75d615cc332e070db51a7d65.jpg)
+
+![](images/ab6639958bc420e3c31b6aa9f52d27e5ae01145c2e265e086664407aac873ffe.jpg)
+
+![](images/83a06d44e191557adb6ba93e0ff02daba23b5c79f0ce86bc6083762f2c8a5f83.jpg)
+
+![](images/faeae18d6c99b1925856e895cedc090904645c8b84f0309ed545bbc01c6480fa.jpg)  
+Figure 1: Accuracy score progression of OLMo-7B on 8 core end-tasks score from Catwalk evaluation suite described in Section 2.4. We can see the benefit of decaying LR to 0 in the final 1000 steps of training on most tasks.
+
+![](images/ae877ffe4eb533871539466ad6fb9bcf1e98d0deb1322c9afa682c8b812e1f88.jpg)  
+Tokens Seen (billions)
+
+![](images/c5a7dc839fb72b2e2acbe915fd2c72e86b26826aeccfecb0669a51fe09358258.jpg)
+
+![](images/053b629d6623d70557cc94df49114dbe9d1db74000af66bb049484bc607a9e3f.jpg)
+
+Results In the Sources Combined subplot of Figure 2, we show the performance of OLMo-7B against 6 comparably-sized language models on the combination of 11 data sources from Paloma. Overall we find OLMo to have a competitive fit, especially given its training data was explicitly decontaminated against Paloma. As seen through the comparison of final models (see shapes) as well intermediate checkpoints (see dashed lines), the OLMo results follow similar scaling trends of other models. Note that the performance of intermediate checkpoints is influenced by where that checkpoint occurs in the learning rate schedule. So models trained for fewer steps will tend to have steeper training curves without necessarily being more sample efficient if training duration were fixed across all models. MPT-7B, nevertheless, stands out as improving ahead of the other models in this subplot. This could be due to a number of factors, including pretraining data composition and its match to the domains in Paloma (e.g., MPT trains on  $27\%$  non-Common Crawl data rather than  $18\%$  for LLaMA,  $12.2\%$  for RedPajama, and  $11.2\%$  for OLMo) as well as various data preprocessing decisions (e.g., MPT's use of semantic dedduplication by Abbas et al., 2023, on C4).
+
+The remaining subplots in Figure 2 provide more fine-grained analysis by reporting bits per byte separately for each of the 11 data sources that are combined in the aggregated Paloma metric. From this we see greater variation in sample efficiency,
+
+largely driven by the similarity of training and evaluation distributions. Notably, OLMo-7B fares well on evaluations predominated by Common Crawl, such as C4, though different ways of postprocessing Common Crawl are best fit by models trained with that specific data, such as Falcon-7B on Falcon RefinedWeb. Meanwhile, OLMo-7B is less sample efficient compared to other models on sources less related to scraped web text, such as WikiText-103, M2D2 S2ORC, and M2D2 Wikipedia. The RedPajama evaluation shows a similar pattern, perhaps as only 2 of its 7 domains are from Common Crawl, and Paloma weights domains within each source equally. Since heterogeneous data from curated sources like Wikipedia and ArXiv papers is scarcer than scraped web text, maintaining sample efficiency for fit to these distributions of language will be challenging as pretraining corpora are scaled.
+
+# 4.3 Adaptation Evaluation
+
+Setup We evaluate OLMo-7B before adaptation, and after both the supervised fine-tuning and DPO training stage, focusing on the safety and chat evaluations used by Wang et al. (2023). We additionally compare to officially released instruction-tuned variants of the models from Table 3. We finally also compare to TULU 2 models to compare against models trained using the same post-training data mixes and procedures.
+
+![](images/d1eaf2f3aed811bbd7393ab4e6c32d07e4daf7da217b8fa6e7eef4901fb1e270.jpg)  
+Figure 2: Bits per byte on 11 evaluation data sources from Paloma and their combination (Magnusson et al., 2023), decontaminated from OLMo's pretraining data. While models follow a general data scaling trend, sample efficiency is most favorable on in-distribution data. For example, OLMo-7B overtakes all other models on C4, perhaps from having  $88.8\%$  Common Crawl pretraining data.
+
+<table><tr><td>Model</td><td>MMLU 0-shot ↑</td><td>AlpacaEval %win ↑</td><td>ToxiGen % Toxic ↓</td><td>TruthfulQA %Info+True ↑</td></tr><tr><td>OLMo (base)</td><td>28.3</td><td>-</td><td>81.4</td><td>31.6</td></tr><tr><td>MPT Chat</td><td>33.8</td><td>46.8</td><td>0.1</td><td>42.7</td></tr><tr><td>Falcon Instruct</td><td>25.2</td><td>14.0</td><td>70.7</td><td>27.2</td></tr><tr><td>RPJ-INCITE Chat</td><td>27.0</td><td>38.0</td><td>46.4</td><td>53.0</td></tr><tr><td>Llama-2-Chat</td><td>46.8</td><td>87.3</td><td>0.0</td><td>26.3</td></tr><tr><td>TÜLU 2</td><td>50.4</td><td>73.9</td><td>7.0</td><td>51.7</td></tr><tr><td>TÜLU 2+DPO</td><td>50.7</td><td>85.1</td><td>0.5</td><td>-7</td></tr><tr><td>OLMo+SFT</td><td>47.3</td><td>57.0</td><td>14.4</td><td>41.2</td></tr><tr><td>OLMo+SFT+DPO</td><td>46.2</td><td>69.3</td><td>1.7</td><td>52.0</td></tr></table>
+
+Table 4: Evaluation of various instruction-tuned 7B models, including OLMo-7B and before and after adaptation training. Lower is better for ToxiGen and higher is better for other metrics. We provide a detailed description of models and metrics in Appendix. E.
+
+Results We find that instruction tuning considerably improves the performance and safety of OLMo-7B, increasing MMLU performance by a wide margin and improving ToxiGen and TruthfulQA scores - especially after DPO training. Additionally, we find that OLMo-7B outperforms most other chat variants after both initial instruction tuning (OLMo+SFT) and additional preference alignment (OLMo+SFT+DPO), highlighting both the strength of OLMo-7B as a base model and the
+
+strength of the TÜLU mix used to perform adaptation training. However, we find there is still a gap with TÜLU 2, which is trained by applying the TÜLU mix on Llama 2. This gap may be due to test set contamination in Llama  $2^{8}$  and because the TÜLU mix was primarily designed for Llama models. Overall, we see that OLMo-7B greatly benefits from additional tuning and serves as a strong base model for downstream applications.
+
+# 5 Artifacts Released
+
+By sharing artifacts from all pipeline stages, we aim to encourage open research and reduce duplicated, often costly efforts, by academics and practitioners. We release the following:
+
+# - Pretraining (§2.1)
+
+1. The training and modeling code.  
+2. The trained model weights for the 7B model, 7B-twin-2T, and the 1B model. For all the models, we release not only the final model weights but also  $500+$  intermediate checkpoints at intervals of 1000 steps.
+
+3. The complete set of metrics logged to Weights & Biases during training.
+
+# Data (§2.2)
+
+1. Our full pretraining corpus Dolma (Soldaini et al., 2024).  
+2. Tools to support reproduction of full training data order as well as inspection of which training data was seen at each step during training.  
+3. Tools for recreating our training data (Soldaini et al., 2024) and performing dataset analysis (Elazar et al., 2024).
+
+# Adaptation (§2.3)
+
+1. The training code and data for adaptation.  
+2. The model weights for OLMo+SFT and OLMo+SFT+DPO.
+
+# - Evaluation (§2.4)
+
+1. The code and data in our evaluation framework Catwalk (Groeneveld et al., 2023) for offline evaluation on both downstream tasks and intrinsic language modeling (Magnusson et al., 2023).  
+2. The evaluation suite (Wang et al., 2023; Ivison et al., 2023) for adapted models.
+
+# 6 Conclusion and Future Work
+
+This paper presents our first release of OLMo, a state-of-the-art, truly open language model and its framework to build and study the science of language modeling. Unlike most prior efforts that have only released model weights and inference code, we release OLMo and the whole framework, including training data, training and evaluation code, and detailed metrics collected during the training runs. Additionally, we released adapted models, as well as all of our model adaptation code and data.
+
+We intend to continuously support and extend OLMo and its framework, and continue to push the boundaries of open LMs to empower the open research community. Since the original release of OLMo described here, we improved our data and training setup to significantly improve results. For example, MMLU scores have improved by 24 points to  $52\%$ . We look forward to bringing different model sizes, modalities, datasets, safety measures, and evaluations into the OLMo family. We hope this and future releases will empower and strengthen the open research community and inspire a new wave of innovation.
+
+# Limitations
+
+We recognize building a large language model has many limitations. In fact, each step of the process of creating a language model, from the data to training to adaptation to evaluation each have their own limitations, and so we've added sections for each below. Of course we recognize that AI systems today can have broad societal reach, and therefore there are significant limitations beyond what we are able to fit into this section.
+
+Data Our work focuses on pretraining data in English. We hope that our open framework enables the development of future models in more languages as well as multilingual models. The data that models are trained on is what gives models their capabilities, and at the scale of training a large language model we recognize that the data likely contains problematic content like toxic language, personal information, and copyrighted text. We mitigated this to the best of our ability but recognize there are no perfect approaches today that can completely remove such content.
+
+Training Training a large language model is currently a challenging endeavor which is missing significant support from the open source community. With our limited page count we did not provide extensive training logs documenting, for example, training runs that diverged or failed to learn.
+
+Adaptation Our pretrained models face the same issues as existing pretrained LLMs, such as bias, toxicity and, hallucinations. Our adapted models are better at avoiding these generations, but they are not perfect. Additionally, we note that we largely adopt an existing data mixture designed for a different model family (TÜLU, designed for Llama models), and OLMo may require different data mixing to adjust for its unique strengths and weaknesses. TheTÜLU mix itself also relies on data distilled from a variety of models, and we hope to reduce our reliance on such data in the future.
+
+Evaluation While we've included comparisons on a variety of datasets to other current language models, many of the downstream tasks are not actually representative of how users interact with language models (i.e., as a chatbot). In addition, language model evaluations are currently very noisy; we aimed to include only evaluations on datasets that provided some signal as to which model performs best, but recognize that there is no perfect
+
+automatic evaluation, and thus comparisons should be taken with a grain of salt.
+
+# Ethics Statement
+
+Through this work, we take the position that increased openness of language models is essential for scientific understanding of their abilities and limitations and for broad participation in the continued development of such models. Training on open data further enhances these benefits. In addition, our open release enables practitioners to take our models and build on them instead of having to train their own from scratch, in which case they would be repeating our work while consuming more resources and leading to an increased environmental impact. Of course, openness is not without risk; the possibility remains that these models will be used in unintended ways that cause harm. We believe that research and development efforts to understand and mitigate those potential harms will also be accelerated by the openness of the models, allowing a diversity of approaches and analyses. Over the past year there have been a number of comparable models released with very permissive licenses, so using a more strict license for our work would not remove the overall risk in the field. We believe this trade-off on the side of being more open is the best option.
+
+# Acknowledgments
+
+OLMo would not have been possible without the support of many individuals and institutions. The experimental components of this work were made possible through a partnership with AMD and CSC, enabling use of the LUMI supercomputer, and Kempner Institute at Harvard University. We thank Jonathan Frankle and the team at MosaicML (now Databricks) for sharing their experiences with FSDP, and building the code base that OLMo is based on. We thank our teammates Taira Anderson, Michelle Benedict, Jon Borchardt, Evie Cheng, Arnavi Chheda, Johann Dahm, Matt Latzke, Kelsey MacMillan, Aaron Sarnat, Carissa Schoenick, Sam Skjonsberg, Michael Schmitz, Michael Wilson, Caitlin Wittlif, and the entire IT team, for their help with the website, design, internal and external communications, budgeting, and other activities that supported smooth progress on this project. Finally, we also express gratitude for the helpful discussions and feedback from our teammates at AI2 and close collaborators, including Prithviraj (Raj)
+
+Ammanabrolu, Peter Clark, Nicole DeCario, Doug Downey, Ali Farhadi, Ian Ferreira, Vainö Hatanpää, Sham M. Kakade, Julien Launay, Sydney Levine, Pekka Manninen, Franzi Roessner, Maarten Sap, Ludwig Schmidt, Yulia Tsvetkov, and Daniel S. Weld.
+
+# References
+
+Amro Abbas, Kushal Tirumala, Daniel Simig, Surya Ganguli, and Ari S Morcos. 2023. Semdedup: Data-efficient learning at web-scale through semantic dedduplication. arXiv preprint arXiv:2303.09540.  
+Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra-Aimée Cojocaru, Daniel Hesslow, Julien Launay, Quentin Malartic, Daniele Mazzotta, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. The falcon series of open language models. ArXiv, abs/2311.16867.  
+Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all.  
+Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. ArXiv, abs/1607.06450.  
+Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback.  
+Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res., 3:1137-1155.  
+Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, Usvsn Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar Van Der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 2397-2430. PMLR.  
+BigScience, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilic, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François
+
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+
+# A Training Settings
+
+Table 5 summarizes the model architecture and the optimizer parameters of OLMo-7B as well as recent similar-sized models.
+
+# B Power Consumption and Carbon Footprint
+
+Following previous literature (Strubell et al., 2019; Patterson et al., 2021; Wu et al., 2022; Dodge et al., 2022), we estimate the total energy consumed and carbon released while pretraining our models by calculating the total power consumption required for training, and then multiplying it by the carbon emission intensity of the power grid where the model was trained. While reporting these operational emissions is standard practice, it does not account for other sources of emissions such as the embodied emissions due to the manufacturing, transportation, and disposal of hardware and datacenter infrastructure, lifetime operational emissions due to use, rebound effects, or other environmental impacts such as water consumption or mining. Thus our estimates should be viewed as lower bounds.
+
+We calculate the total power consumption for our models by measuring the power consumption of a single node every  $25\mathrm{ms}$ , calculating an average across the entire training run, and multiplying by the total number of nodes. We then account for the energy efficiency of the data center by multiplying the previous total by a power usage effectiveness (PUE) factor, which we set to 1.1, representing a conservative  $10\%$  energy consumption overhead typical of energy efficient datacenters.[1011] We estimate that pretraining our 7B models consumed 239 MWh of energy.
+
+To calculate carbon emissions, we multiply the total power consumption by a carbon intensity factor, measured in  $\mathrm{kgCO_2}$  emitted per KWh, based on the physical location of the data center where each model was trained. The model trained on A100-40GB GPUs was trained in Australia, so we assume a carbon intensity factor of 0.610, the national average for Australia in 2022.[12] The model trained on MI250X GPUs was trained in the LUMI
+
+supercomputer, which runs on  $100\%$  renewable, carbon-neutral energy, so we assume a carbon intensity factor of 0. LUMI is powered entirely by hydroelectric power and some sources (Ubierna et al., 2022) measure the carbon intensity factor of hydroelectric power to be 0.024, which would imply total carbon emissions of  $3.54\mathrm{tCO}_2\mathrm{eq}$ . However, we rely on the official LUMI data for our calculations, and thus we estimate total pretraining emissions of  $69.78\mathrm{tCO}_2\mathrm{eq}$ . In Table 6 we compare our models with other previously released models based on publicly available information.
+
+We hope that openly releasing our models can reduce future emissions by allowing others to avoid the need to pretrain models from scratch, and give insights into the true cost of developing state of the art models. We also highlight that our estimates are lower bounds, because they do not include other critical pieces of development such as debugging, hyperparameter tuning, and downtime.
+
+# C Additional Evaluation
+
+Additional perplexity results In Figure 3 we provide results for each of the 7 data sources in Paloma (Magnusson et al., 2023) that are excluded from the combined metric in Figure 2. Some of these sources such as Pile (Gao et al., 2020) and ICE (Greenbaum and Nelson, 1996) are not publicly available at this time. Dolma 100 Programming Languages (Soldaini et al., 2024) consists of code data that is not supported by the decontamination approach used in Paloma. TwitterAAE (Blodgett et al., 2016), along with ICE, are datasets for targeted analyses of disparities in performance between different dialects and as such should be evaluated separately. And finally, the Manosphere, Gab, and 4chan corpora (Ribeiro et al., 2021; Zannettou et al., 2018; Papasavva et al., 2020) are intended to examine model fit to language from fringe online communities that are studied for prevalent hate speech and toxicity. Thus minimizing perplexity on these fringe corpora is not always desirable.
+
+One notable result here is that OLMo-7B is much farther ahead of the other models on Dolma 100 Programming Languages (100 PLs). Note that this effect may be due in part to underestimation from contamination, as decontaminating code data is beyond the scope of the method in Paloma. At the
+
+<table><tr><td></td><td>OLMo-7B</td><td>LLaMA2-7B</td><td>OpenLM-7B</td><td>Falcon-7B</td><td>PaLM-8B</td></tr><tr><td>Dimension</td><td>4096</td><td>4096</td><td>4096</td><td>4544</td><td>4096</td></tr><tr><td>Num heads</td><td>32</td><td>32</td><td>32</td><td>71</td><td>16</td></tr><tr><td>Num layers</td><td>32</td><td>32</td><td>32</td><td>32</td><td>32</td></tr><tr><td>MLP ratio</td><td>~8/3</td><td>~8/3</td><td>~8/3</td><td>4</td><td>4</td></tr><tr><td>Layer norm type</td><td>non-parametric</td><td>RMSNorm</td><td>parametric</td><td>parametric</td><td>parametric</td></tr><tr><td>Positional embeddings</td><td>RoPE</td><td>RoPE</td><td>RoPE</td><td>RoPE</td><td>RoPE</td></tr><tr><td>Attention variant</td><td>full</td><td>GQA</td><td>full</td><td>MQA</td><td>MQA</td></tr><tr><td>Biases</td><td>none</td><td>none</td><td>in LN only</td><td>in LN only</td><td>none</td></tr><tr><td>Block type</td><td>sequential</td><td>sequential</td><td>sequential</td><td>parallel</td><td>parallel</td></tr><tr><td>Activation</td><td>SwiGLU</td><td>SwiGLU</td><td>SwiGLU</td><td>GeLU</td><td>SwiGLU</td></tr><tr><td>Sequence length</td><td>2048</td><td>4096</td><td>2048</td><td>2048</td><td>2048</td></tr><tr><td>Batch size (instances)</td><td>2160</td><td>1024</td><td>2048</td><td>2304</td><td>512</td></tr><tr><td>Batch size (tokens)</td><td>~4M</td><td>~4M</td><td>~4M</td><td>~4M</td><td>~1M</td></tr><tr><td>Weight tying</td><td>no</td><td>no</td><td>no</td><td>no</td><td>yes</td></tr><tr><td>Warmup steps</td><td>5000</td><td>2000</td><td>2000</td><td>1000</td><td></td></tr><tr><td>Peak LR</td><td>3.0E-04</td><td>3.0E-04</td><td>3.0E-04</td><td>6.0E-04</td><td></td></tr><tr><td>Minimum LR</td><td>3.0E-05</td><td>3.0E-05</td><td>3.0E-05</td><td>1.2E-05</td><td></td></tr><tr><td>Weight decay</td><td>0.1</td><td>0.1</td><td>0.1</td><td>0.1</td><td></td></tr><tr><td>Beta1</td><td>0.9</td><td>0.9</td><td>0.9</td><td>0.99</td><td></td></tr><tr><td>Beta2</td><td>0.95</td><td>0.95</td><td>0.95</td><td>0.999</td><td></td></tr><tr><td>Epsilon</td><td>1.0E-05</td><td>1.0E-05</td><td>1.0E-05</td><td>1.0E-05</td><td></td></tr><tr><td>LR schedule</td><td>linear</td><td>cosine</td><td>cosine</td><td>cosine</td><td></td></tr><tr><td>Gradient clipping</td><td>global 1.0</td><td>global 1.0</td><td>global 1.0</td><td>global 1.0</td><td></td></tr><tr><td>Gradient reduce dtype</td><td>FP32</td><td>FP32</td><td>FP32</td><td>BF16</td><td></td></tr><tr><td>Optimizer state dtype</td><td>FP32</td><td>most likely FP32</td><td>FP32</td><td>FP32</td><td></td></tr></table>
+
+Table 5: LM architecture and optimizer comparison at the 7-8B scale. In the "layer norm type" row, "parametric" and "non-parametric" refer to the usual layer norm implementation with and without adaptive gain and bias, respectively. All models are trained using AdamW.
+
+same time other models that are trained on code data from GitHub such as RPJ-INCITE-7B, that are just as likely to have contamination, fair much worse. Another factor then is that OLMo-7B trains on code data with exactly the same post-processing as that in 100 PLs while the code data in other models will have been processed differently. Similarly, Pile evaluation demonstrates these in-distribution and potential contamination effects as Pythia-6.9B achieves top performance despite being trained on almost an order of magnitude fewer tokens than OLMo-7B.
+
+The results on the remaining 5 targeted sources should be interpreted with care, as Paloma often finds that perplexity on these sources is dominated by superficial features such as low average document length rather than fit to that which would actually be salient to members of these speech communities. TwitterAAE and Gab have among the shortest documents in Paloma contributing to unusually high bits per byte in this figure. Other than these two, the models are notably very closely grouped in a data scaling trend in ICE, Manosphere, and 4chan.
+
+Additional end-task results Next, in Table 7, we provide results from zero-shot evaluation of
+
+OLMo-7B on 6 additional end-tasks apart from the 8 in our core evaluation suite. These tasks are headqa_en (Vilares and Gomez-Rodriguez, 2019), logiqa (Liu et al., 2020), mrpc (Dolan and Brockett, 2005), qnli (Wang et al., 2018), wic (Pilehvar and Camacho-Collados, 2018), and wnli (Wang et al., 2018).
+
+We note, however, that in contrast to our core evaluation set described in Section 4.1, we found these additional end-tasks to have less stable performance during model development, and to provide a limited signal. This is illustrated in Figure 4, where we see the progress of task performance throughout training to be more random (compare with the more stable upward trends in Figure 1). While tasks such as mrpc and wic appear more stable, they offered additional difficulties related to performance being tied to random chance (e.g., wic) or the tendency of models to make spurious predictions (e.g., always predicting a single label) that either inflate or deflate performance due to dataset class imbalances (e.g., mrpc). We therefore caution against relying too heavily on these tasks when measuring model performance throughout training and comparing models.
+
+<table><tr><td></td><td>GPU Type</td><td>GPU Power Consumption (MWh)</td><td>Power Usage Effectiveness</td><td>Carbon Intensity (kg CO2e/KWh)</td><td>Carbon Emissions (tCO2eq)</td></tr><tr><td>Gopher-280B</td><td>TPU v3</td><td>1,066</td><td>1.08</td><td>0.330</td><td>380</td></tr><tr><td>BLOOM-176B</td><td>A100-80GB</td><td>433</td><td>1.2</td><td>0.057</td><td>30</td></tr><tr><td>OPT-175B</td><td>A100-80GB</td><td>324</td><td>1.1</td><td>0.231</td><td>82</td></tr><tr><td>T5-11B</td><td>TPU v3</td><td>77</td><td>1.12</td><td>0.545</td><td>47</td></tr><tr><td>LLaMA-7B</td><td>A100-80GB</td><td>33</td><td>1.1</td><td>0.385</td><td>14</td></tr><tr><td>LLaMA2-7B</td><td>A100-80GB</td><td>74</td><td>1.1</td><td>0.385</td><td>31</td></tr><tr><td>OLMo-7B</td><td>MI250X</td><td>135</td><td>1.1</td><td>0.000*</td><td>0*</td></tr><tr><td>OLMo-7B</td><td>A100-40GB</td><td>104</td><td>1.1</td><td>0.610</td><td>70</td></tr></table>
+
+Table 6:  $\mathrm{CO}_{2}$  emissions during pretraining. We estimate the total carbon emissions for various models using publicly available data on PUE, carbon intensity of local power grid, and reported power consumption. Numbers for Gopher-280B (Rae et al., 2022), BLOOM-176B (Luccioni et al., 2022), OPT-175B (Zhang et al., 2022), T5-11B (Patterson et al., 2021), LLaMA (Touvron et al., 2023a), and LLaMA2 (Touvron et al., 2023b) are taken from their respective papers. See Section B for details on how tCO2eq was calculated.  
+* LUMI runs entirely on hydroelectric power $^{13}$  and some estimates (Ubierna et al., 2022) measure the intensity factor of hydroelectric power to be 0.024, implying total emissions of  $3.54 \mathrm{tCO}_{2} \mathrm{eq}$ .
+
+<table><tr><td></td><td>headqa_en</td><td>logiqa</td><td>mrpc</td><td>qnli</td><td>wic</td><td>wnli</td><td>avg.</td></tr><tr><td>Falcon-7B</td><td>38.6</td><td>23.7</td><td>62.8</td><td>49.8</td><td>49.5</td><td>47.9</td><td>45.4</td></tr><tr><td>LLaMA-7B</td><td>38.7</td><td>19.5</td><td>68.6</td><td>50.1</td><td>49.1</td><td>52.1</td><td>46.4</td></tr><tr><td>LLaMA2-7B</td><td>39.5</td><td>26.1</td><td>69.1</td><td>49.4</td><td>49.8</td><td>45.1</td><td>46.5</td></tr><tr><td>MPT-7B</td><td>37.4</td><td>22.9</td><td>67.7</td><td>52.1</td><td>48.1</td><td>47.9</td><td>46.0</td></tr><tr><td>Pythia-6.9B</td><td>40.1</td><td>21.5</td><td>65.4</td><td>53.8</td><td>55.0</td><td>38.0</td><td>45.6</td></tr><tr><td>RPJ-INCITE-7B</td><td>36.9</td><td>27.8</td><td>58.8</td><td>53.8</td><td>48.9</td><td>57.8</td><td>47.3</td></tr><tr><td>OLMo-7B</td><td>37.3</td><td>23.4</td><td>68.4</td><td>49.1</td><td>50.2</td><td>56.3</td><td>47.5</td></tr></table>
+
+Table 7: Zero-shot evaluation of OLMo-7B on 6 additional end-tasks apart from the 8 present in our core evaluation suite. Once again, we compare OLMo-7B to 6 other model checkpoints which are publicly available. We find that OLMo-7B outperforms the other models on aggregate taken over 6 additional end-tasks from this table, however these tasks were also found to provide limited signal during training (see Figure 4).
+
+# D Adaptation Training Details
+
+We use the following hyperparameters when instruction tuning OLMo. These were chosen through small pilot experiments.
+
+- Learning rate:  $2 \times 10^{-6}$  
+- Epochs: 3  
+- Warmup: Linear warmup for the first  $3\%$  of total training time, and then linear cooldown to a learning rate of 0 over the remaining steps.  
+Weight decay: 0  
+Gradient clipping: 0  
+Maximum sequence length: 2048  
+- Data:TÜLU V2 SFT mix, resplit such that long conversations are split into 2048-token chunks and replacing the hardcoded split with
+
+data about OLMo. Data is publicly available. $^{14}$
+
+After instruction finetuning, we then use the following hyperparameters for DPO training, following Ivison et al. (2023):
+
+- Learning rate:  $5 \times 10^{-7}$  
+$\beta$  : 0.1  
+- Epochs: 3  
+- Warmup: Linear warmup for the first  $10\%$  of total training time, and then linear cooldown to a learning rate of 0 over the remaining steps.  
+Weight decay: 0  
+Gradient clipping: 0
+
+![](images/5636d3f5f6e6868694b6aa930ff7ce3d4ffb24725e63d7269382bd18d80d9f04.jpg)
+
+![](images/bb03f684b55c534ba6c6fe128b150cf902638d92e18c6e85384e4ac250dec0c3.jpg)
+
+![](images/80ae46e26ec1752c9ff502bd16af668c80161f47cc7e97586d6b60a454b0d656.jpg)
+
+![](images/33d0392ace1e0c025763add91f8a7e1f6d2b3d1cbef642f4168a067a30eebce5.jpg)
+
+![](images/16cd6b7f966275e7e55aa2cad45acf219bff5e36a22eb2fe142e522ec7818687.jpg)  
+Figure 3: Bits per byte for each of the 7 remaining Paloma data sources not aggregated in Figure 2.
+
+![](images/95139f910598cd5962934a6d453e296541a1cf4c26d4fb1be33ef2cd81d9bd94.jpg)  
+Tokens Seen (billions)
+
+![](images/b515011f40c501f701f0f85ecea92c052ebd313e2e71e53e4de74326aa937d82.jpg)
+
+![](images/a24af444066abfc605aa61b03b2b44ea982401c9c179629808c6cd1d73e4d276.jpg)
+
+![](images/0cbff55a701e0d5624ad4219bb94ca689fd8d0d0215748fb7418289e4e57b4f7.jpg)
+
+![](images/1002a2a57cacf510c7b3ed537b1b4dace496a56063257a4699d654244ac2ed3f.jpg)
+
+![](images/cb8f1abca1580be9907a0c425cf8c52d7e95ab726be8f26b367d987db998a11c.jpg)
+
+![](images/d23e30759c0aa257b8e761321f4d14fa1fe815ee6106af917af4fc1ff4b470d0.jpg)
+
+![](images/e7dbe697485721178dc963b7563542a7a653d84326e5a1e49f3bb4df9241b6f7.jpg)  
+Tokens Seen (billions)
+
+![](images/23b7813a6dd5d08381f78a8f6a662df98b75c92bd6faf4922e0f789dfb366641.jpg)  
+Figure 4: Accuracy score progression of OLMo-7B on 6 additional end-tasks. The performance of these additional end-tasks was unstable and provided limited signal during model development.
+
+Maximum sequence length: 2048  
+- Data: A modified form of UltraFeedback (Cui et al., 2023), with TruthfulQA prompts removed. We used the 'fixed' variant released by Argilla, which uses the average of GPT-generated aspect-based scores to determine
+
+chosen and rejected pairs. 15
+
+# E Adaptation Evaluation and Model details
+
+We choose the models in Table 4 by choosing the 'canonical' best versions (that is, the best instruction-tuned or otherwise adapted models released by the same organisation) of the base models we compare against in Table 3. We additionally compare to TULU 2 to show the current best models trained using the TULU mix used to finetune OLMo. We display evaluations on MMLU, AlpacaEval, ToxiGen, and Truthfulness to focus on displaying how instruction tuning can generally help capabilities (MMLU), how the models perform in an open-ended chat setting (AlpacaEval), and to test how instruction tuning aids in model safety and truthfulness (AlpacaEval, ToxiGen). We additionally report OLMo's performance over the entire TULU evaluation suite in Table 8.
+
+We provide a brief description of each model evaluated in Table 4 below. For all models, we use the provided chat template for prompt formatting when available.
+
+- MPT Chat: A version of MPT 7B finetuned on the ShareGPT-Vicuna (Chiang et al., 2023), HC3 (Guo et al., 2023), Alpaca (Taori et al., 2023), HH-RLHF (Bai et al., 2022), and Evol-Instruct (Xu et al., 2024) datasets. Retrieved from https://huggingface.co/mosaicml/mpt-7b-chat.  
+- Falcon Instruct: A version of Falcon 7B finetuned on the Baize (Xu et al., 2023), GPT4All (Anand et al., 2023), GPTeacher (Teknium1, 2023), and Refined-Web English (Penedo et al., 2023) datasets. Retrieved from https://huggingface.co/tiiuae/falcon-7b-instruct.  
+- RPJ-INCITE Chat: A version of RPJ-INCITE 7B finetuned on the OASST1 (Köpf et al., 2023) and Dolly V2 (Conover et al., 2023) datasets. Retrieved from https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat.  
+- Llama-2 Chat: A version of Llama 2 7B finetuned on a mixture of instruction datasets and further trained with RLHF. We refer the reader to Touvron et al. (2023b) for further details.  
+- TÜLU 2: A version of Llama 2 7B finetuned on a mixture of instruction datasets (the TÜLU 2 mix).
+
+We refer the reader to Ivison et al. (2023) for further details.
+
+- TÜLU 2+DPO: TÜLU 2 further trained with DPO on the UltraFeedback dataset (Cui et al., 2023). We refer the reader to Ivison et al. (2023) for further details.  
+- OLMo+SFT: A version of OLMo 7B fintuned on the same data as TÜLU 2.  
+- OLMo+SFT+DPO: OLMo+SFT further trained with DPO on the UltraFeedback dataset (Cui et al., 2023).
+
+We additionally provide a brief description of each evaluation setting from Table 4:
+
+- MMLU: We use the official MMLU (Hendrycks et al., 2021) evaluation script and prompts available at https://github.com/hendrycks/test, with modifications to allow for batch processing. We evaluate using 0 few-shot examples, following the original setup of MMLU. We report average accuracy across test examples.  
+- ToxiGen: We follow the setup in Touvron et al. (2023b), but use the original set of prompts from Hartvigsen et al. (2022), which are designed to elicit toxic generations for certain groups. We take only the prompts designed to produce toxic language ('hateful' prompts) and use 500 prompts per group to reduce evaluation costs. For base language models, we pass in the original ToxiGen prompts unchanged and greedily decode up to the first new line (or a maximum of 512 tokens). For instruction-tuned models, we place the prompt in the corresponding template, and ask the model to complete the prompt, until the model generates a stop token (or a maximum of 512 tokens). We pass the generated text into a roberta-large model trained to detect toxic content finetuned as part of Hartvigsen et al. (2022).<sup>16</sup> We then report the percentage of generations deemed toxic by the classifier.  
+- TruthfulQA: Following Touvron et al. (2023b), we mainly use the generation setting of TruthfulQA (Lin et al., 2022). The TruthfulQA dataset contains 818 questions, which are used to prompt the tested model to generate answers. We use the default QA prompt format with 6 in-context QA
+
+<table><tr><td>Model</td><td>MMLU 0-shot</td><td>GSM8k 8-shot CoT</td><td>BBH 3-shot CoT</td><td>TydiQA 1-shot</td><td>Codex-Eval Pass@10</td><td>AlpacaEval %win</td><td>ToxiGen % Toxic</td><td>TruthfulQA % Info + True</td></tr><tr><td>OLMo-7B</td><td>28.3</td><td>8.5</td><td>31.7</td><td>32.3</td><td>21.4</td><td>-</td><td>81.4</td><td>31.6</td></tr><tr><td>+SFT</td><td>47.3</td><td>15.5</td><td>36.9</td><td>35.2</td><td>28.6</td><td>57.0</td><td>14.4</td><td>41.2</td></tr><tr><td>+SFT+DPO</td><td>46.1</td><td>11.0</td><td>35.8</td><td>21.7</td><td>27.8</td><td>69.3</td><td>1.7</td><td>52.0</td></tr></table>
+
+Table 8: Evaluation of OLMo-7B models before and after instruction finetuning and DPO training on the full TÜLU evaluation suite. Lower is better for ToxiGen and higher is better for other metrics.
+
+examples. We follow the official script in their official implementation to do greedy decoding and answer postprocessing. We train two LLaMA 2-based classifiers for judging the truthfulness and informativeness of the model response, due to the depreciation of GPT-3 making exact replication of the original TruthfulQA evaluation infeasible. We find that the LLaMA 2 judges are generally able to match the performance of the original GPT-3-based judges used by Lin et al. (2022). We report the rate of the responses being truthful and informative (\% Informative and Truthful) following Touvron et al. (2023b). We only report the  $\%$  Informative and Truthful as our primary metric.
+
+- AlpacaEval: We use the package provided by Li et al. (2023), following the default setup which asks the evaluated model to generate responses for 805 prompts and employ GPT-4 to compare the response with Davinci-003. We employ the "alpaca_eval_gpt4" annotator. We allow the evaluated model to generate up to 2048 tokens, without specifying special stop sequences. The reported win-rate is the percentage of model generations that GPT-4 reports as being preferred over the generations from Davinci-003.

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+# OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
+
+Chaoqun  $\mathsf{He}^1$ , Renjie Luo $^2$ , Yuzhuo Bai $^1$ , Shengding  $\mathsf{Hu}^1$ , Zhen Leng Thai $^1$   
+Junhao Shen $^1$ , Jinyi  $\mathsf{Hu}^1$ , Xu  $\mathsf{Han}^{1*}$ , Yujie Huang $^1$ , Yuxiang Zhang $^3$   
+Jie Liu $^3$ , Lei Qi $^3$ , Zhiyuan  $\mathsf{Liu}^{1*}$ , Maosong Sun $^1$
+
+$^{1}$ Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China
+
+$^{2}$ Institute of Artificial Intelligence, Beihang University, China
+
+$^{3}$ Wisdom Way AI Lab, China
+
+{hcq21,byz22}@mails.tsinghua.edu.cn，renjie.luo@outlook.com，{hanxu2022,liuzy}@tsinghua.edu.cn
+
+# Abstract
+
+Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenges are essential to gauge their advanced abilities. In this work, we present OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level mathematics and physics competitions, including the Chinese college entrance exam. Each problem is detailed with expert-level annotations for step-by-step reasoning. Evaluating top-tier models on OlympiadBench, we implement a comprehensive assessment methodology to accurately evaluate model responses. Notably, the best-performing model, GPT-4V, attains an average score of  $17.97\%$  on OlympiadBench, with a mere  $10.74\%$  in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. Our analysis orienting GPT-4V points out prevalent issues with hallucinations, knowledge omissions, and logical fallacies. We hope that our challenging benchmark can serve as a valuable resource for helping future AGI research endeavors. The data and evaluation code are available at https://github.com/OpenBMB/OlympiadBench
+
+# 1 Introduction
+
+Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks such as text generation (Zhao et al., 2023), code generation (Zan et al., 2023) and mathematical reasoning (Lu et al., 2023; Zhou et al., 2023), garnering significant attention from both academia and industry (Wei et al., 2022; Zhao et al., 2023; Bubeck et al., 2023). The most powerful models
+
+*Corresponding authors: Xu Han and Zhiyuan Liu.
+
+Question: Find all triples  $(x,y,z)$  of positive integers such that  $x\leq y\leq$ $z$  and  $x^{3}(y^{3} + z^{3}) = 2012(xyz + 2)$
+
+Solution: First note that  $x$  divides  $2012 \cdot 2 = 2^3 \cdot 503$ . If  $503 \mid x$  then the right-hand side of the equation is divisible by  $503^3$ , and it follows that  $503^2 \mid xyz + 2$ . This is false as  $503 \mid x$ . Hence  $x = 2^m$  with  $m \in \{0,1,2,3\}$ . If  $m \geq 2$  then  $2^6 \mid 2012(xyz + 2)$ . However the highest powers of 2 dividing 2012 and  $xyz + 2 = 2^m yz + 2$  are  $2^2$  and  $2^1$  respectively. So  $x = 1$  or  $x = 2$ , yielding the two equations
+
+$$
+y ^ {3} + z ^ {3} = 2 0 1 2 (y z + 2),
+$$
+
+$$
+y ^ {3} + z ^ {3} = 5 0 3 (y z + 1)
+$$
+
+In both cases .... It follows that  $y\equiv -z(mod503)$  as claimed. Therefore  $y + z = 503k$  with  $k\geq 1$  . In view of  $y^{3}+$ $z^3 = (y + z)((y - z)^2 +yz)$  the two equations take the form
+
+$$
+k (y - z) ^ {2} + (k - 4) y z = 8 \tag {1}
+$$
+
+$$
+k (y - z) ^ {2} + (k - 1) y z = 1 \tag {2}
+$$
+
+In (1) we have  $(k - 4)\mathrm{yz}\leq 8$  , which implies  $k\leq 4$  .... Therefore (1) has no integer solutions.
+
+Equation (2) implies  $0 \leq (k - 1)yz \leq 1$ , so that  $k = 1$  or  $k = 2$ . Also  $0 \leq k(y - z)^2 \leq 1$ , hence  $k = 2$  only if  $y = z$ . However then  $y = z = 1$ , which is false in view of  $y + z \geq 503$ . Therefore  $k = 1$  and (2) takes the form  $(y - z)^{2} = 1$ , yielding  $z - y = |y - z| = 1$ . Combined with  $k = 1$  and  $y + z = 503k$ , this leads to  $y = 251, z = 252$ .
+
+In summary the triple  $(2,251,252)$  is the only solution.
+
+Final answer: (2,251,252)
+
+Answer type: Tuple
+
+Subfield: Number theory
+
+Question type: Open-ended
+
+Figure 1: An example of IMO in OlympiadBench. Solving this example requires AI systems to span different mathematical domains and conduct advanced reasoning.
+
+such as GPT-4 (OpenAI, 2023a) and Gemini Ultra (Team, 2023) have even surpassed ordinary human level on a wide variety of benchmarks such as MMLU (Hendrycks et al., 2020), MMMU (Yue et al., 2023), and even surpassing human expert in many area. These results show a promising future that LLMs can serve as proficient assistants for human scientists (Nguyen, 2023; Qiu et al., 2023). Among the array of expert-level skills exhibited by LLMs, scientific reasoning consistently emerges as one of the most brilliant, showcasing some of the most distinguished intellectual properties that experts possess. Therefore, this paper primarily focuses on mathematical and physical reasoning.
+
+In recent years, several benchmarks related to mathematics have been proposed, such as the
+
+dataset GSM8K (Cobbe et al., 2021) as well as the dataset MATH (Hendrycks et al., 2021). However, these benchmarks, are primarily developed before the advent of highly capable LLMs, and now lack sufficient challenge for the latest models. For instance, GPT-4 with prompting techniques(Zhou et al., 2023) has achieved a  $97.0\%$  success rate on GSM8K and  $84.3\%$  on MATH. The rapid evolution of LLMs may soon lead to saturated results on these benchmarks. Concurrently, LLMs are not yet fully equipped to assist mathematicians in solving complex problems (Collins et al., 2023; Zhang et al., 2023), nor are they capable of performing expert-level mathematical reasoning independently. This discrepancy underscores the need for more challenging datasets to benchmark future advancements of LLMs in this domain. Similarly, physics presents comparable challenges for AI to those found in mathematics. Nevertheless, existing benchmarks related to physics (Lu et al., 2022; Arora et al., 2023; Wang et al., 2024) are characterized by their relatively low difficulty and limited scope. There is also a significant lack of a rigorous and challenging benchmark in physics.
+
+In addition to the issue regarding the benchmark difficulty, it is important to note that these benchmarks predominantly focus on text. This presents a significant limitation, as a wide range of scientific reasoning contexts require multimodal reasoning abilities. For example, grasping geometry reasoning in mathematics or understanding experiments designs in physics are scenarios where multimodal reasoning capabilities are crucial. Notably, various large multimodal models (LMMs) have been developed (Team, 2023; Liu et al., 2023) and demonstrate proficiency on a variety of tasks (Lu et al., 2022; Yue et al., 2023; Zhang et al., 2024b; Lu et al., 2024), offering the potential for multimodal scientific reasoning. Nevertheless, there is still a lack of sufficient benchmarks to prove whether these LMMs are capable of handling scientific problems. Consequently, a challenging multimodal benchmark is essential for advancing scientific reasoning tasks(Zhang et al., 2024a; Lu et al., 2023).
+
+To address the aforementioned inadequacies, we introduce OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark. This collection comprises 8,476 math and physics problems sourced from International Olympiads, Chinese Olympiads, and the most challenging segments of the Chinese College Entrance Exam (GaoKao). We download PDF data from official
+
+websites and utilize Mathpix<sup>1</sup> for OCR parsing. We meticulously inspect, clean, and revise the data, and further adopt LLMs for dedduplication. Finally, we annotate the data with crucial information such as answer types and subfields, yielding a dataset that is clean, accurate, and detailed. As shown in Figure 1, OlympiadBench features numerous distinct characteristics such as difficulty, free-form generation, expert-level solution annotation, detailed labeling of difficulty, wide-coverage of modality and language, etc. These features are summarized more clearly from Table 1.
+
+We conduct an evaluation of current state-of-the-art LLMs and LMMs on the OlympiadBench. The best-performing model, GPT-4V, is a multimodal version of GPT-4 developed by OpenAI that can understand images. Despite its advanced capabilities, GPT-4V achieves a score of only  $17.97\%$  on OlympiadBench, with individual scores of  $21.70\%$  in mathematics and  $10.74\%$  in physics.
+
+Importantly, the experiment results show that LMMs still struggle in computational error, incorrect reasoning or induction. For the process involved in the correct responses, the process occasionally includes hallucinated reasoning, or choosing a more complex solution when a simpler solution exists. All these results highlight the substantial challenge OlympiadBench presents to contemporary large models and point the direction of future efforts.
+
+OlympiadBench is inspired by the significant advances made by DeepMind AlphaGeometry (Trinh et al., 2024), which nearly matches the proficiency of International Mathematical Olympiad (IMO) gold medalists in geometry proofs. It is clear that OlympiadBench, along with other challenging datasets like the AI-MO challenge $^{2}$ , will witness and benchmark the swift progress towards expert-level AI assistants for solving scientific problems.
+
+# 2 Related Work
+
+This section gives an overview of the existing datasets in solving mathematics and physics problems as well as multimodal datasets.
+
+Mathematics Benchmarks. Solving mathematics problems and proving theorems in natural languages has been a key research focus in machine learning and natural language processing since the 1960s (Bobrow et al., 1964). Previous bench
+
+marks (Koncel-Kedziorski et al., 2016; Wang et al., 2017; Ling et al., 2017; Amini et al., 2019; Cobbe et al., 2021; Wei et al., 2023) focus predominantly on math word problems (WMPs) which involve four basic arithmetic operations with single or multiple operation steps (Lu et al., 2023). Typically, the GSM8K (Cobbe et al., 2021) dataset targets elementary-level questions within 8 steps of basic arithmetic operations. However, these problems are typically text-only (Lu et al., 2023) and of lower difficulty, with reasoning limited to a few computations. As the complexity of the problems rises, some works (Hendrycks et al., 2021; Frieder et al., 2023; Arora et al., 2023) introduce competition-level problems integrating mathematical logic and background knowledge. Yet, these challenging datasets are increasingly being surmounted (Zhou et al., 2023). Theorem proving is a problem to demonstrate the truth of a mathematical claim (a theorem) through a sequence of logical arguments (a proof) (Lu et al., 2023). Earlier efforts mainly focused on translating natural language proofs into formal representations, facing significant expertise and labor challenges (Zheng et al., 2022; Welleck et al., 2021). The emergence of LLMs has facilitated notable advancements in the domain of natural language proof (Jiang et al., 2023). Olympiad-Bench presents mathematical reasoning and theoretical proofs all in natural language with detailed solution annotations.
+
+Physics Benchmarks. Physics questions in SciQ (Welbl et al., 2017), ScienceQA (Lu et al., 2022) and E-EVAL (Hou et al., 2024) are mainly elementary and high school level multiple-choice questions, lacking complex reasoning and computational tasks. In MMLU-STEM (Hendrycks et al., 2020) and C-Eval-STEM (Huang et al., 2023), physics questions also adopt a multiple-choice format. JEEBench (Arora et al., 2023) extends this format to include multistep reasoning with physics knowledge, yet it is limited in scope and purely text-only. TheoremQA (Chen et al., 2023) is the first theorem-driven question-answering dataset. Curated by domain experts, it contains 800 high-quality questions that encompass 350 theorems from Mathematics, Physics, Electrical Engineering and Computer Science (EE&CS), and Finance. SciEval (Sun et al., 2023) consists of a total of about 18,000 challenging scientific questions, spanning three important basic science fields: chemistry, physics and biology. SciBench (Wang et al., 2024) and OCWCourses (Lewkowycz et al., 2022) of
+
+fer college-level physics questions in free-response formats, where SciBench contains multimodal information. In contrast, OlympiadBench escalates in difficulty, diversifies in question types, and surpasses in volume, setting a new benchmark for complexity and variety in the domain.
+
+Multimodal Benchmarks. For assessing multimodal capability, works such as Geometry3K (Lu et al., 2021), GeoQA (Chen et al., 2021), GeoQA+ (Cao and Xiao, 2022), and UniGeo (Chen et al., 2022) have employed multimodal information for tackling geometric problems, integrating natural language descriptions with diagrams. ScienceQA (Lu et al., 2022), MMMU (Yue et al., 2023), CMMMU (Zhang et al., 2024b) and CMMU (He et al., 2024) are multimodal, multidiscipline evaluation sets, encompassing a broad range of subjects. MathVista (Lu et al., 2024) integrates 28 existing and 3 newly constructed multimodal datasets involving mathematics, aiming to establish a benchmark that encapsulates challenges from a variety of mathematical and visual tasks. However, it does not concentrate on delving into the complexity of mathematics problems.
+
+In summary, we introduce a new benchmark to address these gaps. Table 1 presents a comparison between OlympiadBench and several related benchmarks, highlighting the significant advantages of OlympiadBench across all aspects.
+
+# 3 The OlympiadBench Dataset
+
+To evaluate the reasoning abilities of LLMs and LMMs in mathematics and physics problems, we have created OlympiadBench, a bilingual and multimodal scientific benchmark at the competition level. This section provides a detailed account of the construction process of OlympiadBench. Summarized statistics of the dataset is shown in Table 2, and more detailed statistics are in Appendix A.2.
+
+# 3.1 Design Principle
+
+The motivation behind OlympiadBench is to establish a benchmark that represents the pinnacle of human intellectual achievement, thereby encouraging researchers of large models to push the boundaries of mathematical and physical reasoning capabilities. We focus on curating challenges that epitomize the highest level of competition worldwide. Specifically, OlympiadBench includes:
+
+1. Inclusion of Olympiad-Level Problems. We collect mathematics and physics problems
+
+<table><tr><td rowspan="2">Benchmark</td><td colspan="2">Subject</td><td rowspan="2">Multi-modal</td><td rowspan="2">Detailed solution</td><td rowspan="2">Difficulty level</td><td colspan="2">Size</td><td rowspan="2">Answer type</td><td rowspan="2">Language type</td><td rowspan="2">Question type</td></tr><tr><td>Maths</td><td>Physics</td><td>Maths</td><td>Physics</td></tr><tr><td>SciBench</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>COL</td><td>217</td><td>295</td><td>Num</td><td>EN</td><td>OE</td></tr><tr><td>MMMU</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>COL</td><td>540</td><td>443</td><td>Num</td><td>EN</td><td>MC,OE</td></tr><tr><td>MathVista</td><td>✓</td><td></td><td>✓</td><td></td><td>-</td><td>1,000</td><td></td><td>Num</td><td>EN</td><td>MC,OE</td></tr><tr><td>ScienceQA</td><td></td><td>✓</td><td>✓</td><td></td><td>H</td><td></td><td>617</td><td></td><td>EN</td><td>MC</td></tr><tr><td>SciEval</td><td></td><td>✓</td><td></td><td></td><td>-</td><td></td><td>1,657</td><td>Num</td><td>EN</td><td>MC,FB,J</td></tr><tr><td>JEEBench</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>CEE</td><td>236</td><td>123</td><td>Num</td><td>EN</td><td>MC,OE</td></tr><tr><td>MMLU</td><td>✓</td><td>✓</td><td></td><td></td><td>COL</td><td>948</td><td>548</td><td></td><td>EN</td><td>MC</td></tr><tr><td>AGIEval</td><td>✓</td><td>✓</td><td></td><td></td><td>CEE</td><td>953</td><td>200</td><td>Num</td><td>EN,ZH</td><td>MC,FB,OE</td></tr><tr><td>GSM8K</td><td>✓</td><td></td><td></td><td>✓</td><td>E</td><td>1,319</td><td></td><td>Num</td><td>EN</td><td>OE</td></tr><tr><td>MATH</td><td>✓</td><td></td><td></td><td>✓</td><td>COMP</td><td>5,000</td><td></td><td>Num,Exp,Tup</td><td>EN</td><td>OE</td></tr><tr><td>OlympiadBench</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>COMP</td><td>6,142</td><td>2,334</td><td>ALL</td><td>EN,ZH</td><td>OE</td></tr></table>
+
+Table 1: For difficulty level, COMP: Competition, COL: College, CEE: College Entrance Examination, H: High School, E: Elementary School, and we picked the highest level; For answer type, Num: Numeric value, Exp: Expression, Equ: Equation, Int: Interval, Tup: Tuple; For language type, EN: English, ZH: Chinese; For question type, OE: Open-ended, MC: Multiple-choice, FB: Fill-in-the-blank, J: Judgement. For the statistical analysis of quantity and relevant metrics in AGIEval, we exclude 1,000 questions from the MATH benchmark to facilitate a more accurate comparison. The “-” indicates that it cannot be confirmed. Upon comparison, OlympiadBench leads in all aspects.  
+
+<table><tr><td>Statistics</td><td>Number</td></tr><tr><td>Total Problems</td><td>8,476</td></tr><tr><td>* Problems with images</td><td>4,869 (57%)</td></tr><tr><td>* Problems with solutions</td><td>8,476 (100%)</td></tr><tr><td>Difficulties (CEE: COMP)</td><td>66%: 34%</td></tr><tr><td>EN: ZH</td><td>2,125: 6,351</td></tr><tr><td>Open-ended Questions</td><td>6,728 (79%)</td></tr><tr><td>Theorem Proving</td><td>1,748 (21%)</td></tr><tr><td>Math: Physics</td><td>6,142: 2,334</td></tr><tr><td>* Maths with images</td><td>2,911</td></tr><tr><td>* Physics with images</td><td>1,958</td></tr><tr><td>Average question tokens</td><td>253</td></tr><tr><td>Max question tokens</td><td>3,745</td></tr><tr><td>Average solution tokens</td><td>347</td></tr><tr><td>Max solution tokens</td><td>4,223</td></tr></table>
+
+Table 2: Statistics of OlympiadBench. When calculating tokens, images are not included.
+
+from the International Olympiad competitions, which cater to the most outstanding high school students in a region. These problems are open-ended, differing from traditional multiple-choice or fill-in-the-blank formats. This selection aims to more accurately reflect the complexity of advanced scientific reasoning, providing insight into the actual reasoning process of the models.
+
+2. Provision of Detailed Solutions. Given the advanced difficulty of these problems, which may exceed the comprehension of individuals
+
+without a specialized background in mathematics, each problem is accompanied by expertly crafted solutions that detail the reasoning steps involved. This approach can not only reduces the difficulty of annotation and evaluation but also enhances the accuracy of the solutions provided. Furthermore, detailed expert-level solutions are valuable for research in model reasoning.
+
+3. Incorporation of Visuals. Recognizing the crucial role of visual information in conveying complex ideas, OlympiadBench incorporates problems that require understanding images, identifying spatial relationships, and other advanced reasoning tasks. This inclusion aims to assess the model's capabilities in interpreting visual data as part of its reasoning process.
+
+4. Minimization of Data Leakage Risks. To minimize the risk of data leakage, we have sourced problems from official Olympiad competitions, converting them from their original PDF files provided by official websites to the markdown format required. This strategy is aimed at reducing the likelihood of the data being inadvertently incorporated into the pre-training corpora of models.
+
+Through these carefully designed criteria, OlympiadBench aims to not only challenge but also significantly advance the capabilities of
+
+models in mathematical and physical reasoning.
+
+# 3.2 Data Processing
+
+The data processing pipeline is structured into three distinct phases: data collection, format conversion & dedduplication, and classification labeling.
+
+Data Collection. OlympiadBench is meticulously compiled from three primary sources: Global Mathematics and Physics Olympiad Problems, Regional and National Chinese Math Competitions, and Gaokao Mock Questions for Mathematics and Physics  $^{3}$ . Each chosen for its distinct advantages in creating a robust and comprehensive benchmark for evaluating LLMs and LMMs in mathematical and scientific reasoning. Their challenges progressively increase in difficulty, not only distinguishing the reasoning capabilities of models of various sizes but also offering guidance on scaling laws (Kaplan et al., 2020) for specialized models in these domains.
+
+Format Conversion and Dedduplication. After collecting all PDF files, we utilize the Mathpix tool for OCR recognition and convert them into markdown format. However, no conversion process is flawless, necessitating manual verification by our team members between the original PDF files and the converted Markdown texts. The Markdown texts are further structured into a format akin to "Problem—Solution—Answer", employing its markup language for text organization. Subsequently, we leverage a specialized small-scale language model trained on mathematical symbol corpora for vectorizing the data and performing dedduplication based on cosine similarity.
+
+Classification Labeling. We note that both mathematics and physics problems predominantly comprise two types of questions: the open-ended problems and the theorem proving problems. We also note that the dataset, enriched by both Olympiad and national examination questions, covers a broad spectrum of subfields, as illustrated in Figure 2. Therefore, we manually annotate each question with topic and problem type annotations.
+
+# 3.3 Data Characteristics
+
+In contrast to previous benchmarks, Olympiad-Bench unveils two unique characteristics within its dataset: the incorporation of Progressive Prob
+
+![](images/20f59230d93429bc9aa94f651e5774374f370c7497b720c255af9e8c1dd70af5.jpg)  
+Figure 2: Subfields Distribution of OlympiadBench
+
+<table><tr><td>Answer type</td><td>Example</td></tr><tr><td>Numeric</td><td>1/4</td></tr><tr><td>Expression</td><td>x = (1/2)at2</td></tr><tr><td>Equation</td><td>x2 + y2 = 1</td></tr><tr><td>Tuple</td><td>(x, y, z) = (0, 0, 0)</td></tr><tr><td>Interval</td><td>(−∞, −1) ∪ (1, +∞)</td></tr></table>
+
+Table 3: Examples of the five answer types
+
+lems in Physics and the categorization of answers to most open-ended questions into a limited number of types.
+
+Progressive Problems in Physics. In physics competitions such as the International Physics Olympiad (IPhO), problems are often structured around a common material or scenario, with subsequent questions potentially relying on the answers or information from previous questions. One example is given in Figure 10 and Figure 11. This design characteristic is commonly referred to as "progressive problems". By linking a series of questions together, progressive problems require participants to apply their knowledge and skills comprehensively to gradually solve more complex issues. This type of question design aims to test students' depth of understanding, application capabilities, and innovative thinking, rather than just basic knowledge. To better utilize this feature, we compile the material, questions, and their answers into the 'context' field for each set of progressive problems.
+
+Answer Type Classification. Whether in mathematics or physics, the answers to problem requiring
+
+definitive response can largely be categorized into the following types: numeric, expression, equation, interval, and tuple. Simple examples of these can be seen in Table 3.
+
+# 3.4 Automatic Scoring Pipeline
+
+We design an automated scoring pipeline (see Algorithm 1) to evaluate model-generated answers across complex fields like mathematics and physics, where answers vary from numbers to equations. This method simplifies answers into two categories: numeric values, handled through floating-point operations, and symbolic expressions, requiring symbolic computation.
+
+For equations, we ensure all terms are on one side before dividing to check for mathematical equivalence. Intervals and tuples are compared by extracting and evaluating each element. Numeric answers are verified against a small tolerance of error, defaulting to 1e-8 but adjustable for physics problems to allow for a specific error margin. For expressions, we use the SymPy library to confirm if the subtraction of two expressions approaches zero, indicating correctness.
+
+# 4 Experiments
+
+# 4.1 Settings
+
+We conduct evaluations of open-source and closed-sourced LMMs that have been selected with consideration of their comprehensive capabilities on OlympiadBench. At the same time, we have selected LLMs with strong mathematical and logical abilities for evaluation on text-only questions.
+
+As no accurate automatic evaluation method for theorem proving exists, we run full experiment on the automatic-scoring-available open-ended problems with answer type included in the Table 3, which is discussed in this section. We do manual sampling check of GPT-4V for theorem proving problems with analysis reported at Section 5.1.
+
+# 4.1.1 Prompts
+
+We evaluate the models in a zero-shot setting. Due to the high difficulty of the OlympiadBench questions, there should be considerable randomness in the results when using small batch data as the validation set, so we directly use a specific prompt template for all models instead of conducting prompt-engineering for each model respectively. The
+
+prompt template for English and Chinese open-ended questions is shown in the figure 3. To ensure the most complete extraction of the model's final results, we explicitly prescribe the types and formats of the answers in the prompt to promote the accuracy of the machine's automatic scoring. In order to test the native mathematical and physical abilities of the models, the prompts used in the test do not introduce knowledge points and other extra information contained in the dataset, but this information can be applied in subsequent research. Note that deepseek-math-7B-RL (Shao et al., 2024) requires the addition of a specific chain-of-thought prompt at the end of the input, which we adhered to during the evaluation.
+
+# 4.1.2 Evaluation Workflow
+
+We first apply each model to generate answers for questions in OlympiadBench using prompts formed by prompt template, with open-source models running on NVIDIA A800 GPUs. Then, we run the automatic scoring pipeline to judge the correctness of the answers as described in subsection 3.4. Finally, we calculate the micro-average accuracy as the comparing metric. The code of the whole workflow is provided in the supplementary material.
+
+# 4.2 Baselines
+
+In our study, we evaluate the performance of current leading bilingual large multimodal models (LMMs), as well as bilingual large language models (LLMs) that has strong mathematical and reasoning abilities. We take both open- and closed-source models into consideration, using either the largest and latest released checkpoints or the best-performing official APIs available.
+
+For LMMs, we select GPT-4V(GPT-4-Vision) (OpenAI, 2023b), Gemini-ProVision (Team, 2023), Qwen-VL-Max (Bai et al., 2023) for closed-source models, while YiVL-34B (01-ai, 2024) and LLaVA-NeXT-34B (Liu et al., 2024) for open-source models. For models that demand compulsory image input, we take their LMM counterpart (corresponding text-model API or base LLM) for evaluation. Specifically, for LLaVA-NeXT-34B, we use its base LLM, Nous-Hermes-2-Yi-34B (NousResearch, 2023). The text model corresponding to Yi-VL-34B is Yi-34B-Chat (01-ai, 2023). Similarly, for the Gemini-Pro-Vision, we utilize the Gemini-Pro API interface. To examine the impact of replacing LMM with base LLM for processing text-only
+
+The following is a question from an International <subject> competition.
+
+The answer of the question should be <ans_type>.
+
+The question has multiple answers, each of them should be <ans_type>.
+
+The question has multiple answers, with the answers in order being <ans_type>,...<ans_type>.
+
+Please calculate the answer according to the given requirements and the information provided. Please use LaTeX format to represent the variables and formulas used in the solution process and results. Please end your solution with
+
+"So the final answer is \boxed{answer}."
+
+"So the final answer is \boxed{multiple answers connected with commas}.
+
+and give the result explicitly.
+
+Single answer of the type "tuple"
+
+Single answer
+
+Multiple answers of single type
+
+Multiple answers of the same type
+
+Single answer
+
+Multiple answers
+
+以下是中国<subject>竞赛中的解答题。
+
+答案类型为<ans_type>。
+
+题目有多个答案，答案类型均为<ans_type>。
+
+题目有多个答案，答案类型分别为<ans_type>、...<ans_type>。
+
+请根据题目的要求和所提供的信息计算得出答案。解答过程和结果中使用的变量和公式请使用LaTeX格式表示。  
+请在最后以
+
+“所以最终答案是\boxed{答案}。”
+
+“所以最终答案是\boxed{用英文逗号连接的多个答案}。”
+
+显式给出结果。
+
+Figure 3: The template of the construction of the prompt for English(left) and Chinese(right) open-ended questions, among which <subject>, <ans_type>, and whether there are multiple answers can all be obtained from the data items in OlympiadBench dataset.
+
+data, we subsequently compare the performance differences between GPT-4V and GPT-4  $^{6}$  on text-only questions in OlympiadBench.
+
+For LLMs, we select DeepSeekMath-7BRL (Shao et al., 2024) as the primary baseline for text-only questions, and report the results of the selected LMMs (or their LLM counterparts) on the text-only questions for comparison, and additionally evaluate GPT-4 as described above.
+
+# 4.3 Main Results
+
+The overall experiment result is shown in table 4. Based on the results, our key findings can be summarized as the following:
+
+OlympiadBench is more challenging than existing benchmarks, which provides new perspective to compare LMMs. As shown in table 7, the most advanced model only achieves an average accuracy of  $17.97\%$  on OlympiadBench, which is much lower than that of existing benchmarks. Moreover, the gap between the models has been widened, thereby becoming more significant, which helps people to compare the differences in capabilities between different models more accurately.
+
+There still exists a huge difference between the most powerful closed-source models and open-source models, but a large model size is needed. The average accuracy of GPT-4V is more than 5 times larger than the best-performing open-source model (Yi-VL-34B). But Gemini-ProVision, being closed-source models of the second-tier size, is much less compatible on complicated tasks such as OlympiadBench, for it achieves an average accuracy that is only slightly higher than open-source model.
+
+The challenge lies more on question-with-images, Physics and none-English text. The model performance on text-only questions is significantly above average, showing the challenging spirit of multi-modal questions. Meanwhile, Physics questions, especially Physics questions with images, are more challenging than math questions, as they require knowledge of the laws of Physics as well as other world knowledge besides mathematical abilities such as calculation and reasoning. Moreover, LMMs with a focus on bilingual image-text training data, such as Qwen-VL-Max and Yi-VL-34B, perform better on Chinese questions then English questions.
+
+Open source LLMs is catching at fast speed in the area of maths and physics. Although with a relatively small size, DeepSeekMath-7BRL outperforms or is on par with Gemini-ProVision and Qwen-VL-Max on the text-only part of OlympiadBench, especially in Math problems, showing promising future of open-source model of pre-training and fine-tuning on fine-grained mathematical and reasoning data.
+
+Multi-modal training slightly hurts performance on text-only math and physics tasks, but may also bring some improvement. The text-only version GPT-4 performs slightly better on all datasets of OlympiadBench, except for the En_COMP dataset. We hypothesis that the improvement in the En_COMP dataset shows an enhancement of long-context text reasoning capabilities, which is discussed in Appendix B.2.
+
+# 5 Analysis
+
+In this section, we conduct analysis on the GPT-4V's answers of specific open-ended questions that have been sampled, as well as giving preliminary
+
+<table><tr><td rowspan="2">Models</td><td colspan="4">Maths</td><td colspan="3">Physics</td><td rowspan="2">Avg.</td></tr><tr><td>En_COMP</td><td>Zh_COMP</td><td>ZhCEE</td><td>Avg.</td><td>En_COMP</td><td>ZhCEE</td><td>Avg.</td></tr><tr><td>LLaVA-NeXT-34B†</td><td>3.98</td><td>2.60</td><td>4.64</td><td>4.30</td><td>-</td><td>1.36</td><td>2.32</td><td>2.08</td></tr><tr><td>Yi-VL-34B†</td><td>4.22</td><td>3.68</td><td>4.31</td><td>4.23</td><td>-</td><td>0.91</td><td>1.64</td><td>1.46</td></tr><tr><td>Gemini-Pro-Vision</td><td>6.92</td><td>2.59</td><td>5.05*</td><td>5.14</td><td>-</td><td>3.19*</td><td>2.12</td><td>2.45</td></tr><tr><td>Qwen-VL-Max</td><td>10.68</td><td>13.21*</td><td>13.08</td><td>12.65</td><td>-</td><td>3.76*</td><td>5.64*</td><td>5.09</td></tr><tr><td>GPT-4V</td><td>27.18</td><td>14.87</td><td>21.27</td><td>21.70</td><td>-</td><td>11.42</td><td>10.45</td><td>10.74</td></tr><tr><td colspan="9">Experiment with text-only</td></tr><tr><td>LLaVA-NeXT-34B</td><td>4.15</td><td>2.94</td><td>8.55</td><td>6.29</td><td>-</td><td>2.12</td><td>5.22</td><td>3.13</td></tr><tr><td>Yi-VL-34B</td><td>4.45</td><td>3.68</td><td>8.06</td><td>6.24</td><td>-</td><td>0.85</td><td>5.22</td><td>2.28</td></tr><tr><td>DeepSeekMath-7B-RL</td><td>19.44</td><td>2.70</td><td>22.42</td><td>18.09</td><td>-</td><td>6.78</td><td>16.52</td><td>9.97</td></tr><tr><td>Gemini-Pro-Vision</td><td>7.57</td><td>2.94</td><td>9.20*</td><td>7.63</td><td>-</td><td>4.66</td><td>6.96</td><td>5.41</td></tr><tr><td>Qwen-VL-Max</td><td>11.57</td><td>14.29</td><td>25.89</td><td>19.70</td><td>-</td><td>4.24</td><td>18.26</td><td>8.83</td></tr><tr><td>GPT-4V</td><td>28.93</td><td>15.93</td><td>37.10</td><td>31.01</td><td>-</td><td>12.71</td><td>23.48</td><td>16.24</td></tr><tr><td>GPT-4</td><td>30.42</td><td>16.42</td><td>37.98</td><td>32.00</td><td>-</td><td>12.29</td><td>24.35</td><td>16.24</td></tr></table>
+
+Table 4: Experimental results. En_COMP: COMP problems in English, Zh_COMP: COMP problems in Chinese, ZhCEE: CEE problems in Chinese. For closed-source models, the responses for some problems are not available, we mark the results with * (all of the proportion of missing answers are less than 1%, except for the result of Qwen-VL-Max in Physics-En_COMP, where 26 questions exceed maximum input length). The causes are further described in Appendix B.3. Moreover, LLaVA-NeXT-34B and Yi-VL-34B only accepts input with single image, we mark results from only one image input with †.
+
+examination of theorem proving questions.
+
+# 5.1 Examination of Theorem Proving Questions
+
+For GPT-4V, we do manual sampling check to evaluate the mathematical theorem proving questions. In the questions drawn according to the knowledge point distribution, GPT-4V only answers 6 out of 81 questions correctly in Math-Zh_COMP, all of which are relatively simple and classic conclusions (e.g. AM-GM inequality), or involved only simple computational derivations, and was basically unable to complete the proof within the token limitation in Math-En_COMP, indicating that existing models still cannot effectively solve lengthy reasoning and proofs, which is consistent with the conclusions in existing papers (Trinh et al., 2024).
+
+In solving proof problems, GPT-4V exposes several important issues, including: inability to fully utilize image information (figure 9 as an example); tending to make mistakes in simplifying and transforming algebraic expressions; proposing simple, basic incorrect conclusions; struggling with classification discussions, etc. Detailed examples can be found in the Appendix C.
+
+# 5.2 Mistake Analysis of GPT-4V
+
+We manually sample and check 97 maths (55 for English and 42 for Chinese) and 67 physics Olympiad-level open-ended problems that GPT-
+
+![](images/ee1750b164a5542ed3cb4c851ac4e3a9fee81195a0aeae4bc5a8508d60d9513e.jpg)  
+Figure 4: Distribution of the error occurring in GPT-4V's solving process of 164 sampled Olympic-level open-ended problems.
+
+4V fails, and analyze the type of mistakes, the overall results are shown in figure 4. In maths problems, the typical errors of GPT-4V include: insufficient classification discussion, especially in combinatorial problems; poor performance in problems requiring large calculations (e.g. conic curve problems), manifests as a lack of logic in the calculation process, resulting in the model being unable to provide a reasonable answer. However, we also found that GPT-4V has strong abilities in solving quadratic equations and derivative problems. In physics problems, GPT-4V tends to fall in conceptual confusion, or introduce unnecessary variables or concepts, but its capability to simplify and transform algebraic expressions is stronger than in purely mathematical situations, with nearly no numerical calculation errors.
+
+# 6 Discussion and Future Work
+
+Here, we discuss the automated evaluation of theorem problems and the future research directions in advanced disciplines.
+
+Evaluation of Theorem's Proof Problems. The inability to automatically evaluate theorem's proof problems remains a significant challenge today. Currently, mainstream methods for the automatic evaluation of proofs require formalization, necessitating domain expertise and background knowledge. Exploring how to automatically evaluate natural language proofs represents an important research direction. Our dataset includes expert-level comprehensive annotations in a fusion of natural language and LaTeX formats, making it a high-quality resource for research in natural language proof problems and fostering further development.
+
+Expansion of Disciplines. Mathematics, much like physics, serves as a litmus test for artificial intelligence, requiring a strong foundational knowledge, rigorous high-level computations, and precise logical reasoning. Currently, large models still face significant challenges in mathematics and physics, which are critical hurdles that must be overcome for the development of AGI. In our future work, we will integrate additional fields such as geography, biology, and chemistry to provide a more innovative and comprehensive evaluation of a model's reasoning capabilities.
+
+# 7 Conclusion
+
+We create OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark to assess the capabilities of large models in mathematics and physics reasoning. Each problem is detailed with expert-level annotations for step-by-step reasoning. In our benchmarking, we provide a detailed analysis of model performance, pinpointing prevalent error types and potential areas for enhancement. This significant and challenging effort fills a notable void, and we intend to open-source the benchmark to advance AGI and scientific reasoning research. Future efforts will focus on gathering more challenging questions and broadening the range of subjects to further develop rigorous scientific benchmarks.
+
+# Acknowledgements
+
+This work is supported by the National Key R&D Program of China (No.2022ZD0116312), National Natural Science Foundation of China (No.
+
+62236004), Quan Cheng Laboratory (Grant No. QCLZD202301) and Institute Guo Qiang at Tsinghua University.
+
+# Ethical Considerations
+
+In this paper, we introduce OlympiadBench, a highly challenging bilingual, multimodal scientific benchmark aimed at evaluating the mathematical and physical reasoning of large models now and AGI in the future. The paper outlines the dataset construction, including data gathering, OCR processing, cleansing, deduplication, and detailed annotation. OlympiadBench's data, derived exclusively from official sources, substantially reduces the likelihood of pre-training data leakage. We offer precise annotations for each problem and have implemented an exhaustive evaluation script for more accurate model performance assessment. Additionally, being bilingual and providing expert-level reasoning annotations for every question, OlympiadBench serves as a crucial resource for propelling AGI's prowess in scientific reasoning. Committed to environmental sustainability, we intend to release the dataset and accompanying scripts publicly to cut down on unnecessary carbon footprint. In experiments, we comply with all licenses for models and data.
+
+# Limitations
+
+In pursuit of understanding the logical reasoning abilities of LLMs and LMMs within the multimodal domains of mathematics and physics, we develop OlympiadBench, a challenging bilingual multimodal scientific benchmark. Despite filling a notable void, this work acknowledges inherent limitations. First, in the OlympiadBench, some questions feature answers that require categorical discussions or textual descriptions, such as proofs, which currently cannot be assessed using regular expressions or tools like SymPy at the code level and necessitate manual review. However, this data holds significant research value. Secondly, the automated scoring system we propose cannot perform specific analysis based on the particulars of each question. It makes logical judgments solely based on the two symbols or numerical expressions inputted, without integrating any special constraints that may exist within the actual problem context. What's more, the development of datasets for multimodal scientific reasoning requires extensive manual effort in gathering and annotating data,
+
+which constrains the diversity and difficulty of multimodal scientific challenges. As a result, this hampers AI's capacity to learn from and address more intricate scenarios.
+
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+
+# A Dataset Details
+
+# A.1 Data Sources
+
+Our data sources can be split into the following three parts:
+
+1. Global Mathematics and Physics Olympiad Problems. The Mathematics and Physics Olympiad problems are globally recognized for their complexity and quality. These problems often require multiple methods of solution and the ability to integrate sub-disciplines from within the broader fields of mathematics and physics. The participants in these competitions represent some of the most proficient individuals worldwide in logical reasoning within mathematics and physics. This not only sets a high standard for problem-solving but also fosters a diverse set of analytical skills that are crucial for the advancement of large models.  
+2. Regional and National Chinese Mathematics Competitions. In addition to maintaining a high level of difficulty, regional competitions and the CMO introduce elements specific to the Chinese context. This inclusion is instrumental in furthering the development and research of Chinese-oriented and multilingual large models. By encompassing a wide array of mathematics and physics problems, these competitions provide a unique opportunity to develop models that are adaptable and proficient across different mathematical queries, enhancing their versatility and effectiveness.  
+3. Gaokao Mock Questions for Mathematics and Physics. Given that the resolution of Olympiad-level problems typically necessitates models with substantial parameter sizes, we also incorporate Gaokao simulation problems to evaluate smaller models' capabilities in answering free-form mathematics and physics questions.
+
+The integration of data from Gaokao simulation problems, regional and national competitions, to the global Olympiads constructs a smooth difficulty transition curve. This methodology not only distinguishes the mathematical and physical problem-solving capabilities of different models but also provides guidance on the scaling laws for models specialized in these domains.
+
+<table><tr><td>Subject</td><td>Source</td><td>Coverage Years</td><td>Number</td></tr><tr><td rowspan="5">Maths</td><td>IMO</td><td>2006-2022</td><td>509</td></tr><tr><td>RMM</td><td>2011, 2013, 2015-2019, 2021, 2023</td><td>53</td></tr><tr><td>ARML</td><td>2009-2014, 2019, 2023</td><td>505</td></tr><tr><td>EMC</td><td>1998-2023</td><td>364</td></tr><tr><td>EGMO</td><td>2013, 2015-2023</td><td>64</td></tr><tr><td rowspan="6">Physics</td><td>IPHo</td><td>1984, 1986-1990, 2008-2012, 2014-2016, 2018-2019, 2021</td><td>381</td></tr><tr><td>APHo</td><td>2013-2015</td><td>200</td></tr><tr><td>EPhO</td><td>2019-2022</td><td>17</td></tr><tr><td>USAPhO</td><td>2017-2021</td><td>113</td></tr><tr><td>PUPC</td><td>2020-2022</td><td>65</td></tr><tr><td>OPHo</td><td>2020-2023</td><td>132</td></tr></table>
+
+Table 5: Summary of Problems in Math and Physics Competitions, with full acronyms listed in the Table 6
+
+# A.2 Data Curation Process
+
+Our initial step involves a comprehensive survey of well-known Olympiad competitions, and the list of which is accessible through the AoPS community platform<sup>7</sup>. We cataloged these competitions based on several criteria: difficulty level, volume of questions, availability of materials in public domains, language, discipline, and coverage years. Following the design principles outlined in Section 3.1, we meticulously select specific contests and years that not only adhere to our dataset design criteria but also try to span the widest possible range of years (Table 5).
+
+In the format conversion phase, we also manually annotated the subfield of each question in maths or physics, with their distribution detailed in Table 9.
+
+# A.3 Example of Progressive Problem in Physics
+
+Figures 10 and 11 present a sequential challenge from the International Physics Olympiad (IPHo) 2021, illustrating the intricacies of progressive problem-solving in a competitive context. This particular problem set exemplifies a common trait in advanced physics competitions: the dependency of many questions on the solutions and materials of preceding ones. These dependencies are sometimes explicit, but most are implicit, weaving a complex web of interconnected knowledge and reasoning.
+
+An explicit instance of this dependency can be observed in problem C.2, where the prompt directly requires the use of the symbol  $\beta$  defined in B.1 for the calculation of an unknown quantity. This re
+
+requirement not only tests the participants' ability to understand and apply physical concepts but also assesses their skill in navigating through and linking various parts of a problem set. Such explicit instructions are crucial for guiding participants through the logical progression of the problems, yet the majority of dependencies remain implicit, demanding a deeper level of comprehension and integration of the material.
+
+This structure of problem-solving reflects a realistic scientific inquiry, where discoveries and solutions often rely on previously established knowledge. The explicit mention of  $\beta$  in C.2 as derived from B.1 is emblematic of this educational approach, aiming to foster a holistic understanding and the ability to build upon existing information to solve complex problems. It underscores the importance of thorough comprehension of earlier sections for successful problem-solving in later sections, simulating real-world scientific challenges where new solutions are often predicated on a foundation of established knowledge.
+
+# B Evaluation Details
+
+# B.1 Details of the Evaluated Models
+
+# B.1.1 LMMs
+
+We have selected current mainstream LMMs that have performed the best on past scientific multimodal datasets for evaluation.
+
+The closed-source models include: GPT-4V (OpenAI, 2023b), developed by OpenAI, which is currently the most powerful multimodal model. Gemini (Team, 2023) is the LMM series developed by Google Deepmind, with Gemini-UltraVision being purported to have surpassed GPT-4V
+
+<table><tr><td>Subject</td><td>Acronym</td><td>Full name</td></tr><tr><td rowspan="5">Maths</td><td>IMO</td><td>International Mathematical Olympiad</td></tr><tr><td>RMM</td><td>Romanian Master of Mathematics</td></tr><tr><td>ARML</td><td>American Regions Mathematics League</td></tr><tr><td>EMC</td><td>Euclid Mathematics Competition</td></tr><tr><td>EGMO</td><td>European Girls’ Mathematical Olympiad</td></tr><tr><td rowspan="6">Physics</td><td>IPhO</td><td>International Physics Olympiad</td></tr><tr><td>APhO</td><td>Asian Physics Olympiad</td></tr><tr><td>EPhO</td><td>European Physics Olympiad</td></tr><tr><td>USAPhO</td><td>USA Physics Olympiad</td></tr><tr><td>PUPC</td><td>Princeton University Physics Competition</td></tr><tr><td>OPhO</td><td>Online Physics Olympiad</td></tr></table>
+
+on datasets like MMMU. However the unavailability of Google's API for Gemini Ultra, we test the accessible Gemini-Pro-Vision as an alternative. Qwen-VL-Max (Bai et al., 2023), developed by Alibaba, is the largest LMM, and stands on par with GPT-4V and Gemini-Ultra in multi-modal tasks. Due to the large proportion of Chinese data used in its training, Qwen-VL-Max has a certain advantage in Chinese language ability.
+
+The open-source models include: Yi-VL-34B (01-ai, 2024) is the first open-source 34B multi-modal model that has demonstrated satisfying performance on several latest datasets. With Chinese text-image pairs included in the training process, Yi-VL-34B offers adequate multilingual support. LLaVA-NeXT-34B (Liu et al., 2024) claims to be the strongest open-source LMM, with enhancements in reasoning, OCR, and world knowledge. Despite being trained exclusively with English multi-modal data, it demonstrates an emergent zero-shot Chinese multi-modal capability on Chinese benchmarks.
+
+It should be noted that an image must be passed for Gemini-Pro-Vision, LLaVA-NeXT, and Yi-VL during inference. Therefore, for the text-only questions in OlympiadBench dataset, we use the corresponding text-model api (for closed-source models), or their base LLM (for open-source models). To examine the impact of replacing LMM with base LLM for processing text-only data, we subsequently compare the performance differences between GPT-4V and GPT-4 on text-only questions in OlympiadBench.
+
+# B.1.2 LLMs
+
+The field of LLM starts early in scientific areas such as mathematics and physics, with models specifically trained occurring. We select DeepSeekMath-7B-RL (Shao et al., 2024) as the primary baseline for text-only questions. DeepSeekMath-7B-RL is pre-trained on 120B math-related data and enhanced chain-of-thought (CoT) reasoning capabilities using reinforcement learning, in the result scoring close to GPT-4 and Gemini-Ultra on the MATH (Hendrycks et al., 2021) dataset. We report the results of the selected LMMs (or their LLM counterparts) on the text-only questions for comparison, and additionally evaluate GPT-4 in order to compare with GPT-4V $^{8}$ .
+
+# B.2 Detailed Experiment Result
+
+The comparison of the performance of mainstream closed-ended models on different datasets are shown in Table 7.
+
+Table 6: Full names of all competitions'acronyms used in this paper  
+
+<table><tr><td>Benchmark</td><td>GPT-4(V)</td><td>Qwen VL-Max</td><td>Gemini Pro</td></tr><tr><td>MATH</td><td>52.9</td><td>-</td><td>32.6</td></tr><tr><td>MathVista(testmini)</td><td>49.9</td><td>50.0</td><td>45.2</td></tr><tr><td>OlympiadBench</td><td>17.97</td><td>10.09</td><td>4.22</td></tr></table>
+
+Table 7: Comparison of Performance on Different Benchmarks. The values for MATH and MathVista are obtained from Gemini and Qwen's report.
+
+To further discuss the performance difference between GPT-4 and GPT-4V on the PhysicsEn_COMP, we split the En_COMP dataset into two sub-datasets, with normal-PhO being normal
+
+PhO questions, and long-PhO being PhO questions that show in a relational series, therefore having long context. As shown in table 8, GPT-4 keeps performing slightly better on normal-PhO, but lags much behind on long-PhO, which may indicate improvement of long-context text reasoning capabilities after multimodal training.
+
+<table><tr><td></td><td>long-PhO (157)</td><td>normal-PhO (74)</td></tr><tr><td>GPT-4V</td><td>18.47</td><td>1.35</td></tr><tr><td>GPT-4</td><td>14.92</td><td>4.05</td></tr></table>
+
+Table 8: Average accuracy of GPT-4V and GPT-4 for the En_COMP dataset
+
+# B.3 Unavailable Responses for Closed-Source Models
+
+As described in table 4, the response for some problems are not available, the main causes are as follows:
+
+1. Exceeding input limit: Some of the context of the problems are too long, which exceed the input token limitation for the API. This case mainly occurs in Physics-En_COMP that contains long-context problems of over 6,000 tokens.  
+2. Inappropriate response: Some problems trigger inappropriate response, which are banned by the API to return.  
+3. No response: Some problems continuously get no or empty response from the API.  
+4. Request timed out: Some problems continuously fail to get a response.
+
+We removed the problems with unavailable response when calculating the accuracy.
+
+# C Additional Analysis and Examples
+
+# C.1 Performance analysis of GPT-4V
+
+We analyzed GPT-4V's performance (accuracy on open-ended problems) on different knowledge points based on the knowledge point labels in OlympiadBench, the results can be found at Figure 5.
+
+For Math problems, GPT-4V has poor performance in geometry, with the lowest scoring knowledge points being almost exclusively geometry-related. This may show the need of improving the
+
+ability of understanding and imaging plane or 3d situations. GPT-4V also performs poorly on knowledge points that are more computationally intensive such as conic curves; and struggles to give a complete and comprehensive classification discussion, therefore prone to making mistakes on combinatorial problems. However, GPT-4V is stronger in knowledge related to derivatives and complex numbers.
+
+As for Physics problems, none of the knowledge points surpass an accuracy of  $16\%$ , and GPT-4V struggles more in thermodynamics and mechanics.
+
+# C.2 Detailed Description of the Error Types in GPT-4V's solving or proving process
+
+The error types are as follows:
+
+1. Question Misunderstanding: GPT-4V sometimes misunderstands the intention or settings of the question.  
+2. Value Calculation Error: GPT-4V make simple calculation mistakes sometimes, such as outputting  $\frac{b}{2} + 7 = \frac{b + 7}{2}$ , these mistakes appear more in Chinese and Math contents.  
+3. Expression Calculation Error: Similar to value calculation error, but happens when transforming between two expressions.  
+4. Logical Reasoning / Induction Error / Conceptual Confusion: GPT-4V sometimes makes false reasoning or induction, as well as encounters conceptual confusion (see Figure 7 for example).  
+5. Introducing Unnecessary variables or concepts: GPT-4V sometimes suddenly introduce variables or try to use concepts that have no contribution to solving the problem, which not only makes the output longer, but also may confuse GPT-4V itself and leads to incorrect output.  
+6. Conclusion Hallucination: GPT-4V sometimes hallucinates for a conclusion that is not reached in former output, or hallucinates a theorem that does not really exist (for example, when solving geometric proving problem, GPT-4V always mention "The Power Theorem", which does not exist, and all the proof thereafter will lost their logic).  
+7. Unfinished Answering: GPT-4V sometimes says the question have confliction in settings
+
+![](images/86921e524022bec95849d8caa849f2888ea1005e845aa5b4bf45e60a2343a3ca.jpg)  
+Figure 5: An analysis of GPT-4V's accuracy on different knowledge points, blue for Math and red for Physics.
+
+![](images/ef3c93ca529f2620d66e2cdbe8509d83b8cbc5f9f52048bcd88b340455bc102b.jpg)
+
+(which is not true), or degenerates after some tokens.
+
+8. Insufficient Classification Discussions: When doing classification discussion, GPT-4V may miss some possible situation, or have overlapped discussion (see Figure 6 for example).  
+9. Incorrect Judging: Sometimes GPT-4V gives the right answer, but is judged as incorrect due to the limitation of the automated scoring system: One important problem is that many problems, especially Physics problems, accept answers that fall in a specific range due to rounding up, rather than a fixed numerical answer, so a precision is needed for automatically calculating the range, which was not given in many cases. Using relative precision (such as a percentage of  $1\%$ ), may leads to accepting answers with unacceptable error when the origin answer is big, so we manually decide the precision for Olympiad-Bench problems if there is no official precision given, which may leads to incorrect judging. For example, in a Physics question (Physics-En_COMP#995), the precision is manually set as  $1e1$ , with the official answer being  $\sqrt{2} \times 3970 \approx 5614.43$ . GPT
+
+4V calculates the answer as  $\sqrt{2} \times 3970 \approx 1.414 \times 3970 \approx 5615.88$ , which falls out of the acceptable range, but is actually correct. Another problem is the current automated scoring system can't judge the equality between expressions such as  $a\sqrt{b}$  and  $\sqrt{a^2b}$  with  $a \geq 0$ . These flaws call for future work such as automatically deciding required precision of the answer, and automatically judging equality between expressions.
+
+# C.3 Other Observation for Open-Ended Problems
+
+1. Given a simple solution, GPT-4V may choose a more complex method to solve the problem (see Figure 8).  
+2. Models may give correct answers with a false process. Mainly observed for problems with a simple answer, such as the variables takes 0 as the answer.  
+3. GPT-4V may success in giving correct overall idea, but fail in calculation (such as solving quadratic equations with extra negative signs), which leads to a wrong answer.
+
+<table><tr><td>Question</td><td>GPT-4V&#x27;s Solution</td></tr><tr><td>A die, with the numbers 1,2,3,4,6, and 8 on its six faces, is rolled. After this roll, if an odd number appears on the top face, all odd numbers on the die are doubled. If an even number appears on the top face, all the even numbers are halved. If the given die changes in this way, what is the probability that a 2 will appear on the second roll of the die?</td><td>Let&#x27;s first determine the possible outcomes after the first roll and the corresponding changes to the die.
+If an odd number appears on the top face after the first roll (which can be either 1, 3, or 6), all odd numbers are doubled. So the new faces of the die will be:
+- If 1 appears, it becomes 2. (3 should also be doubled.)
+- If 3 appears, it becomes 6. (1 should also be doubled.)
+- The number 6 is even, so it remains unchanged. (Incorrectly classifying 6 into odd numbers, which leads to failure in thorough discussion of even numbers below.)
+If an even number appears on the top face after the first roll (which can be either 2, 4, or 8), all even numbers are halved. So the new faces of the die will be:
+- If 2 appears, it becomes 1. (Same problem.)
+- If 4 appears, it becomes 2.
+- If 8 appears, it becomes 4.
+Now, let&#x27;s calculate the probability of getting a 2 on the second roll based on the first roll:
+- If the first roll is 1, the die will have faces 2, 2, 6, 4, 6, 8. The probability of rolling a 2 is 2/6.
+......
+- If the first roll is 8, the die will have faces 1, 2, 3, 2, 3, 4. The probability of rolling a 2 is 2/6.
+Now, we need to calculate the total probability of rolling a 2 on the second roll. Since each face has an equal probability of appearing on the first roll, we can average the probabilities:
+Total probability 1/6 × (2/6 + ... + 2/6) = ... = 1/4.
+So the final answer is \boxed{1/4}.</td></tr></table>
+
+Figure 6: An example of GPT-4V making insufficient classification discussions in a combinatorial problem. The red text is the wrong part of GPT-4V's answer, and the purple text is our further illustration.
+
+4. GPT-4V may not fully utilize the information from the image (see Figure 9).
+
+# D Automatic Scoring Pipeline
+
+The pipeline workflow is shown in Algorithm 1.
+
+Algorithm 1: Auto Scoring Judge  
+```txt
+Input: GroundTruth, ModelOutput;  
+Output: Boolean value indicating match;  
+Preprocess GroundTruth and ModelOutput;  
+if GroundTruth equals ModelOutput then  
+return True;  
+else  
+if GroundTruth and ModelOutput are intervals or tuples then  
+if GroundTruth equals ModelOutput as intervals or tuples then  
+return True;  
+else  
+return False;  
+end  
+else  
+if GroundTruth equals ModelOutput numerically then  
+return True;  
+else  
+if Both GroundTruth and ModelOutput contain "=" then  
+return EquationEqual(GroundTruth, ModelOutput);  
+else  
+return ExpressionEqual(GroundTruth, ModelOutput);  
+end  
+end
+```
+
+<table><tr><td>Context</td><td>GPT-4V&#x27;s Solution</td></tr><tr><td>3. Block on an Incline Plane Consider a block of mass, M, and charge, Q &gt; 0, sliding down an incline plane at an angle α with the horizontal with initial position (x0, y0). The system is exposed to an upward electric field given by E(y) = β(y0 - y) with β &gt; 0. Figure 2: An incline plane submerged in a medium.</td><td>To find the position ye where the block experiences net zero vertical force, we need to consider the forces acting on the block in the vertical direction. There are two forces to consider: the gravitational force and the electric force. The gravitational force acting on the block in the vertical direction can be found by considering the component of the weight of the block that acts perpendicular to the incline. The weight of the block is Mg, where g is the acceleration due to gravity. The component of this force in the vertical direction is Mg sin α. (The vertical gravity force should be Mg itself, and the component that is vertical to the plane should be Mg cos α, so GPT-4V gets wrong in any way.)</td></tr><tr><td>Question</td><td rowspan="2">The electric force acting on the block is due to the electric field E(y) = β(y0 - y). Since the charge of the block is Q, the electric force is Fe = QE(y) = QB(y0 - y).</td></tr><tr><td>(a) Find an expression for the ye, the position where the block experiences net zero vertical force.</td></tr><tr><td>Answer</td><td rowspan="2">At the position ye where the net vertical force is zero, these two forces must balance each other: Mg sin α = QB(y0 - y). Now we can solve for ye: ......</td></tr><tr><td>ye = y0 - gM/βQ (Full solution omitted, see Physics-En_COMP#958 if needed.)</td></tr></table>
+
+Figure 7: An example of GPT-4V getting confused between the concept of "vertical to the plane" and "vertical(to the ground)".
+
+<table><tr><td>Question</td><td>GPT-4V&#x27;s Solution</td></tr><tr><td>已知正数x,y满足:x+y=1.求1/x+4/y的最小值.</td><td rowspan="2">由已知条件x+y=1,我们可以将1/x+4/y表达为x和y的函数。首先,我们将y用x表示出来,即y=1-x。然后将y代入1/x+4/y中,得到:1/x+4/y=1/x+4/1-x</td></tr><tr><td>Standard Solution</td></tr><tr><td>由a+b-2√ab=(√a-√b)2≥0,故a+b≥2√ab.所求1/x+4/y=(1/x+4/y)(x+y)=1+4+y/x+4x/2y≥5+2√y/x·4x/2y=9,等号在x=1/3,y=2/3处取到,故最小值为9.</td><td>为了找到这个表达式的最小值,我们可以使用微分法。首先,我们对上述表达式求导: d/dx(1/x+4/1-x)=1/x2+4/(1-x)2.为了找到极值点,我们令导数等于零:···解这个方程,···我们得到:3x2+2x-1=0.这是一个二次方程,我们可以使用求根公式来解它:x=-b±√b2-4ac/2a,其中a=3,b=2,c=-1,代入求根公式得到:x=2a,我们得到两个解,分别是:x=1/3或x=-1.由于x是正数,我们排除x=-1的解,所以x=1/3.将x的值代入y=1-x,得到···。现在我们可以计算1/x+4/y的值:1/x+4/y=1/1+4/2=···=9.所以最终答案是9。</td></tr></table>
+
+Figure 8: An example of GPT-4V giving a solution that is much complex than that of the standard solution, although reaching the correct answer eventually.
+
+![](images/b999b3af2d03c37547ed7ffd6c9fe27ddcab7188fd1efccc5f431984d16e36ba.jpg)  
+Figure 9: An example of GPT-4V's failure in utilizing image information from Math-Zh_COMP. GPT-4V starts proving with "we have  $\angle DEF = \angle FBC = 59^\circ$ ", which is an error that can evidently be identified from the image, showing insufficient comprehension of the given plane geometry figure.
+
+<table><tr><td>Subset</td><td>Subfield</td><td>Number</td></tr><tr><td rowspan="13">CEE Math</td><td>Derivative</td><td>334</td></tr><tr><td>Conic Sections</td><td>350</td></tr><tr><td>Sequence</td><td>258</td></tr><tr><td>Trigonometric Functions</td><td>236</td></tr><tr><td>Set Theory</td><td>22</td></tr><tr><td>Logic</td><td>3</td></tr><tr><td>Elementary Functions</td><td>158</td></tr><tr><td>Inequality</td><td>138</td></tr><tr><td>PC&amp;PE</td><td>80</td></tr><tr><td>Probability and Statistics</td><td>758</td></tr><tr><td>Plane Geometry</td><td>790</td></tr><tr><td>Solid Geometry</td><td>1322</td></tr><tr><td>Complex Numbers</td><td>8</td></tr><tr><td rowspan="4">COMP Math</td><td>Combinatorics</td><td>369</td></tr><tr><td>Algebra</td><td>525</td></tr><tr><td>Number Theory</td><td>256</td></tr><tr><td>Geometry</td><td>535</td></tr><tr><td rowspan="5">CEE&amp;COMP Physics</td><td>Mechanics</td><td>1010</td></tr><tr><td>Electromagnetism</td><td>714</td></tr><tr><td>Thermodynamics</td><td>248</td></tr><tr><td>Optics</td><td>153</td></tr><tr><td>Modern Physics</td><td>209</td></tr></table>
+
+Table 9: Statistics of subfield in Mathematics and Physics. PC&PE stands for Polar Coordinates and Parametric Equations.
+
+# Electrostatic lens (10 points)
+
+Consider a uniformly charged metallic ring of radius  $R$  and total charge  $q$ . The ring is a hollow toroid of thickness  $2a \ll R$ . This thickness can be neglected in parts A, B, C, and E. The  $xy$  plane coincides with the plane of the ring, while the  $z$ -axis is perpendicular to it, as shown in Figure 1. In parts A and B you might need to use the formula (Taylor expansion)
+
+$$
+(1 + x) ^ {\varepsilon} \approx 1 + \varepsilon x + \frac {1}{2} \varepsilon (\varepsilon - 1) x ^ {2}, \mathrm {w h e n} | x | \ll 1.
+$$
+
+![](images/ccfe17384400033227f3c9edee8028ac5f2a2949ff451fdba5faf1859ada402f.jpg)  
+Figure 1. A charged ring of radius  $R$ .
+
+# Part A. Electrostatic potential on the axis of the ring (1 point)
+
+A.1 Calculate the electrostatic potential  $\Phi (z)$  along the axis of the ring at a  $z$  distance 0.3pt from its center (point A in Figure 1).  
+A.2 Calculate the electrostatic potential  $\Phi(z)$  to the lowest non-zero power of  $z$ , as- 0.4pt suming  $z \ll R$ .  
+A.3 An electron (mass  $m$  and charge  $-e$ ) is placed at point A (Figure 1,  $z \ll R$ ). What is the force acting on the electron? Looking at the expression of the force, determine the sign of  $q$  so that the resulting motion would correspond to oscillations. The moving electron does not influence the charge distribution on the ring.  
+A.4 What is the angular frequency  $\omega$  of such harmonic oscillations? 0.1pt
+
+# Part B. Electrostatic potential in the plane of the ring (1.7 points)
+
+In this part of the problem you will have to analyze the potential  $\Phi (r)$  in the plane of the ring  $(z = 0)$  for  $r\ll R$  (point B in Figure 1). To the lowest non-zero power of  $r$  the electrostatic potential is given by  $\Phi (r)\approx q(\alpha +\beta r^2)$
+
+B.1 Find the expression for  $\beta$ . You might need to use the Taylor expansion formula 1.5pt given above.
+
+Figure 10: An example illustrating the first section of Problem 2 in IPho 2021.
+
+B.2 An electron is placed at point B (Figure 1,  $r \ll R$ ). What is the force acting on the 0.2pt electron? Looking at the expression of the force, determine the sign of  $q$  so that the resulting motion would correspond to harmonic oscillations. The moving electron does not influence the charge distribution on the ring.
+
+# Part C. The focal length of the idealized electrostatic lens: instantaneous charging (2.3 points)
+
+One wants to build a device to focus electrons—an electrostatic lens. Let us consider the following construction. The ring is situated perpendicularly to the  $z$ -axis, as shown in Figure 2. We have a source that produces on-demand packets of non-relativistic electrons. Kinetic energy of these electrons is  $E = m v^2 / 2$  ( $v$  is velocity) and they leave the source at precisely controlled moments. The system is programmed so that the ring is charge-neutral most of the time, but its charge becomes  $q$  when electrons are closer than a distance  $d / 2$  ( $d \ll R$ ) from the plane of the ring (shaded region in Figure 2, called "active region"). In part C assume that charging and de-charging processes are instantaneous and the electric field "fills the space" instantaneously as well. One can neglect magnetic fields and assume that the velocity of electrons in the  $z$ -direction is constant. Moving electrons do not perturb the charge distribution on the ring.
+
+![](images/ceea571cef85df684e41d85585a713787fc9256b287d70d62dbfe8e249e2f1a0.jpg)  
+Figure 2. A model of an electrostatic lens.
+
+C.1 Determine the focal length  $f$  of this lens. Assume that  $f \gg d$ . Express your answer in terms of the constant  $\beta$  from question B.1 and other known quantities. Assume that before reaching the "active region" the electron packet is parallel to the  $z$ -axis and  $r \ll R$ . The sign of  $q$  is such so that the lens is focusing.
+
+In reality the electron source is placed on the  $z$ -axis at a distance  $b > f$  from the center of the ring. Consider that electrons are no longer parallel to the  $z$ -axis before reaching the "active region", but are emitted from a point source at a range of different angles  $\gamma \ll 1$  rad to the  $z$ -axis. Electrons are focused in a point situated at a distance  $c$  from the center of the ring.
+
+C.2 Find  $c$ . Express your answer in terms of the constant  $\beta$  from question B.1 and 0.8pt other known quantities.
+
+Figure 11: An example illustrating the second section of Problem 2 in IPhO 2021.

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+# On Context Utilization in Summarization with Large Language Models
+
+Mathieu Ravaut $^{1,2}$ , Aixin Sun $^{1}$ , Nancy F. Chen $^{1,2,4,5}$ , Shafiq Joty $^{1,3}$
+
+<sup>1</sup>Nanyang Technological University, Singapore
+
+$^{2}$  Institute of Infocomm Research (I²R), A*STAR, Singapore
+
+3 Salesforce Research
+
+CNRS@CREATE, Singapore
+
+<sup>5</sup> Centre for Frontier AI Research (CFAR), A*STAR, Singapore
+
+# Abstract
+
+Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. However, in question answering, language models exhibit uneven utilization of their input context. They tend to favor the initial and final segments, resulting in a U-shaped performance pattern concerning where the answer is located within the input. This bias raises concerns, particularly in summarization where crucial content may be dispersed throughout the source document(s). Besides, in summarization, mapping facts from the source to the summary is not trivial as salient content is usually re-phrased. In this paper, we conduct the first comprehensive study on context utilization and position bias in summarization. Our analysis encompasses 6 LLMs, 10 datasets, and 5 evaluation metrics. We introduce a new evaluation benchmark called MiddleSum on the which we benchmark two alternative inference methods to alleviate position bias: hierarchical summarization and incremental summarization<sup>1</sup>.
+
+# 1 Introduction
+
+Large language models (LLMs) have drastically transformed the landscape of NLP recently (Brown et al., 2020). With instruction tuning (Ouyang et al., 2022; Chung et al., 2022), LLMs made a major leap forward in conditional (prompted) content generation, and can generate satisfying outputs without the need to fine-tune on a specific task. In abstractive summarization specifically, this approach has arguably opened a new paradigm: summaries generated by LLMs are highly fluent, grammatical and relevant (Goyal et al., 2022). Despite noticeably lower scores on automatic metrics such as ROUGE (Lin, 2004) or BERTScore (Zhang
+
+et al., 2019), summaries generated by LLMs are largely preferred by humans over summaries from state-of-the-art fine-tuned models like BRIO (Liu et al., 2022b, 2023c). In fact, on XSum, GPT-3.5 summaries are even on par with re-annotated human-written summaries, and much better than the dataset's original ground-truth, according to human evaluators (Zhang et al., 2023b). LLMs also show promising capability in evaluating summaries generated by other systems, including LLMs (Fu et al., 2023; Luo et al., 2023; Shen et al., 2023a).
+
+Despite this success, a few major technological bottlenecks remain with LLMs, including the maximum length of their context window. The standard context window length for open-source LLMs is 2k tokens (Brown et al., 2020; Scao et al., 2022; Penedo et al., 2023; Touvron et al., 2023a), which drastically limits their usefulness for long-input summarization (Shaham et al., 2022). Several techniques were proposed to extend the context window, including ALiBi (Press et al., 2021), LeX (Sun et al., 2022), position interpolation (Chen et al., 2023) and YaRN (Peng et al., 2023). While some of them claim up to  $100\mathrm{k}+$  tokens processing capacity (Peng et al., 2023), it remains unclear how much such methods help on long-context summarization.
+
+Scaling up context length would only succeed if a key question gets addressed first: do LLMs make proper use of their entire context? Recent work (Liu et al., 2023a) suggested that, surprisingly, such a simple assumption may not hold: through experiments on multi-document question answering and key-value retrieval, the authors find that LLMs mostly focus on the beginning and end of the (long) context window. Plotting performance with regards to the position of the important information exhibits a  $U$ -shape, with performance high at first (beginning of the source), then dropping, and rising again at the end. Worryingly, in the middle of the context window, LLMs' performance can drop to even below random chance, calling for greater
+
+examination of LLMs' behaviors with regard to the position of information within the source.
+
+In this work, we investigate in depth how LLMs use their context window in abstractive summarization. Unlike in question-answering, mapping facts in the output to a specific snippet in the source is not straightforward in abstractive summarization, due to the high-level of re-phrasing and compression. We conduct a large-scale study with 6 LLMs, 10 datasets covering many aspects of summarization, and 5 highly diverse automatic metrics. Our contributions are threefold:
+
+- We conduct the first large-scale analysis on context utilization in abstractive summarization, and the impact of the position of salient information on performance. We show that the U-shape or middle-curve exhibited by (Liu et al., 2023a) also holds in abstractive summarization.  
+- We craft an evaluation dataset (MiddleSum) where important information is concentrated in the middle of the context, enabling us to automatically quantify how much LLMs are affected by the middle-curse.  
+- We benchmark two alternative methods for inference on MiddleSum: hierarchical summarization and incremental summarization, showing their promise at alleviating the middle curse (especially in the scientific paper domain).
+
+# 2 Experimental Setup
+
+Datasets We cover a broad set of diverse abstractive summarization tasks, varying length and domain. We include 5 datasets of standard length (source is below 2k tokens, which always fits in the context window): (i) CNN/DailyMail (Hermann et al., 2015), (ii) XSum (Narayan et al., 2018), (iii) Reddit-TIFU (Kim et al., 2019), (iv) SAMSum (Gliwa et al., 2019), and (v) Multi-XScience (Lu et al., 2020). We also include another 5 long-input summarization datasets: (i) Arxiv and (ii) PubMed (Cohan et al., 2018), (iii) GovReport (Huang et al., 2021), (iv) SummScreenFD (Chen et al., 2022), and (v) Multi-News (Fabbri et al., 2019). A high-level view of each dataset is shown in Table 1, and detailed statistics are presented in Appendix A. For all datasets, we run experiments on the test set, subsampling 1,000 data points if its size is greater than 1,000, or using the entire test set otherwise.
+
+Models We experiment with 6 popular and high-performing LLMs:
+
+<table><tr><td rowspan="2">Dataset</td><td>Input length</td><td colspan="2"># Documents</td><td>In Flan?</td><td></td></tr><tr><td>Standard Long</td><td>Single</td><td>Multi</td><td>Yes No</td><td></td></tr><tr><td>CNN/DM (Hermann et al., 2015)</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>XSum (Narayan et al., 2018)</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Reddit-TIFU (Kim et al., 2019)</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>SAMSum (Gliwa et al., 2019)</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>Multi-XScience (Lu et al., 2020)</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td></tr><tr><td>Arxiv (Cohan et al., 2018)</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr><tr><td>PubMed (Cohan et al., 2018)</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>GovReport (Huang et al., 2021)</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>SummScreenFD (Chen et al., 2022)</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Multi-News (Fabbri et al., 2019)</td><td></td><td>✓</td><td></td><td>✓</td><td></td></tr></table>
+
+Table 1: Summarization datasets under study. In standard length datasets, the context and summary fit within a 2k tokens LLM context window.
+
+- Flan-UL2 is a 20B parameters encoder-decoder model pre-trained on 1T tokens. It is based on the UL2 20B model (Tay et al., 2022), with the addition of Flan-T5 (Chung et al., 2022) instruction fine-tuning. The context window is  $2\mathrm{k}$  tokens.  
+- Llama-2 (Touvron et al., 2023b) is a recently introduced powerful decoder-only model pretrained on 2T tokens, ranging from 7B to 70B parameters, and with a 4k tokens context window. We use the 7B and 13B models.  
+- Xgen-7B (Nijkamp et al., 2023) is a 7B decoder-only model pre-trained on up to 1.5T tokens. It supports an 8k tokens context window.  
+- Mistral-7B (Jiang et al., 2023a) is also an 8k-context 7B decoder-only model, with performance slightly better than Llama-2-13B.  
+- GPT-3.5 (gpt-3.5-turbo-0125), which is known to be good at generating summaries and has a 16k tokens context window. $^2$
+
+We analyze the open-source models through HuggingFace transformers library (Wolf et al., 2020), and use the OpenAI API for GPT-3.5. We use the instruction-tuned (or chat) checkpoints for Llama-2, Xgen-7B and Mistral-7B. Note that popular instruction-tuning datasets such as Flan (Wei et al., 2021) include some of the datasets we study: CNN/DM, XSum, SAMSum and Multi-News. To run inference, we use the following prompt: Read the following text and summarize it: [text]. Summarize the above text in [n] sentences. Summary: where n is set to an average number of target sentences per dataset (see Appendix A). We infer all models in bfloat16 and sample summaries with top-k sampling (Fan et al., 2018) using  $k = 50$  and temperature  $T = 0.3$ .
+
+![](images/2858fdadf0fbcdbe6f0f02a811bdd0d423efd528d45bcc7f9efdf419dc4b7768.jpg)
+
+![](images/14d1d50d582af65641df4e2f5458d8aafe41f9ce77eb1a3e183b83033a12fe77.jpg)
+
+![](images/01e1fa7988193b41e453f235bb3fcc99ef1ad1b63fb0f0d1a57fa7b77a80a717.jpg)
+
+![](images/700edab1bd7d638b7bba3e3ca4e8cd2b2ecfffff8901563feca6ab504484a1f4.jpg)
+
+![](images/6cd9c0a193b67d35e8cb57196918b21f3fbcc4542f092889af744a72160fefdb.jpg)
+
+![](images/78ef2c6cb60ed46d4b6f19e164bf39a1033b07140d3be1916c4fde3edeecb267.jpg)  
+Figure 1: Distribution of the relative location of summary bigrams within the source. We split each source document into 20 bins of the same number of words, and plot the distribution of summary bigrams over source bins.
+
+![](images/ed6ee097f1270d55c01b79f9bfe954dfd308efd793dfa865e69cbe43afa82c6b.jpg)
+
+![](images/aa8ee3170ad83ebf0b035c2665bc35265430cc0f04fad700aa163693407499cd.jpg)
+
+![](images/3b6f3b2d150affc3e4492fb671dd1b46eefd3eba1a04c2ea3c6253869588afc2.jpg)
+
+![](images/7d52f8f653cf7108a662753aa345d2913c40b0c46f51a9d51a7624c99882d260.jpg)
+
+Evaluation Measures Summarization evaluation is especially challenging in the LLM era, as most automatic metrics poorly correlate with human preferences (Goyal et al., 2022; Liu et al., 2023b). To get a broad picture of performance, we evaluate with metrics as diverse as possible. First, we consider reference-based metrics: ROUGE-2 (Lin, 2004), which measures bigram overlap, BERTScore (Zhang et al., 2019), which measures semantic similarity with BERT (Devlin et al., 2019) embeddings, and A3CU (Liu et al., 2023c), which extracts facts in the form of Atomic Content Units (ACUs) (Liu et al., 2023b), and checks the presence of ACUs between prediction and reference. As reference-free metrics, we include SummaC (Laban et al., 2022), a leading factual consistency evaluation metric relying on entailment scores between pairs of source and summary sentences. We also leverage GPT-3.5 again (still gpt-3.5-turbo-0125), this time as a summarization evaluator, which is proven to be a strong natural language generation evaluator (Wang et al., 2023a; Shen et al., 2023a; Jain et al., 2023). We prompt the model with the source and generated summary (which fits in GPT-3.5's 16k context window) and ask to output a score on a likert scale from 1 to 5. We refer to Appendix B for the full prompt template. $^{3}$
+
+We report the performance of LLMs on all 10 datasets, alongside a comparison to SOTA, in Appendix C. FLan-UL2 dominates on standard-length
+
+datasets, but GPT-3.5 has the upper hand on the long-input ones. Performance itself is not our focus in this paper, but rather which position-related factors influence it. We discard Flan-UL2 on long-input datasets due to very poor performance.
+
+# 3 Experiments
+
+In this section, we describe a series of experiments aimed at understanding how LLMs treat information in their input depending on the position.
+
+# 3.1 RQ1: Where in the source do LLMs take their information from?
+
+We first investigate summaries generated by LLMs, and map them to specific parts of the input. Unlike in question-answering or extractive summarization, mapping salient information from a summary to the source is not trivial in abstractive summarization.
+
+We follow the approach used in (Kim et al., 2019; Zhao et al., 2022a) and compute the relative position of bigams from generated summaries within the source documents, as a proxy for the position of salient information. We only use unique bigrams from summaries, and for each bigram, find all its occurrences within the source, if there are any. We then split the source into 20 bins of the same number of words, and compute the fraction of matched bigrams found in each bin. On top of the LLMs described above, we include the position of bigrams from reference summaries, and a uniform baseline.
+
+As seen in Fig. 1, all summarization datasets except XSum, and Reddit-TIFU show some lead bias: salient bigrams from the reference (orange
+
+![](images/b01e51146bedebc6bcb43c8a35b5a1ee417db7339c69c7d7451b1e9150c66691.jpg)  
+Figure 2: Distribution of relative location of input context sentences aligned with sentences from summaries. X-axis corresponds to the source sentence bin, y-axis to the fraction of aligned sentences in each bin.
+
+![](images/e52edb7a5a4266c9ecdd399c82cdb659091f6c77c54b9a4e901047dd1b40c1d5.jpg)
+
+![](images/2a532022d9dbde878fca0a71f7f1f28eeefcd85ddd8481a6f1831488d04b4e19.jpg)
+
+![](images/c919f3a658684fee29179cd010cf782902893fedb60f29939a3c16ca44062743.jpg)
+
+![](images/d02b6d392e9ecdecbc208ab020dd5eb1c30cb734b53ed0eb7ea37c5d4a1fd763.jpg)
+
+curves) are more likely to be found at the beginning of the source. However, LLMs show a significantly stronger lead bias on all datasets: bigrams from LLMs summaries are much more likely to be found in the first  $20\%$  words of the source. It is especially striking on XSum (except for Flan-UL2), Reddit-TIFU, Arxiv, PubMed and GovReport. On XSum, Flan-UL2 closely matches the reference distribution, which we attribute to its better instruction tuning. Results in Appendix D confirm that bigram distribution for LLMs and references are statistically different (p-value of Kolmogorov-Smirnov test (Massey Jr, 1951) inferior to 0.001) in all but 4 out of the 55 (dataset, LLM) setups: Flan-UL2 on XSum and SAMSum, Llama-2-7B on SAMSum and GPT-3.5 on SAMSum. We conclude that LLMs focus on contents at the beginning of the source document(s).
+
+# 3.2 RQ2: Where do LLMs look at within their context window?
+
+In the previous experiment's design, LLMs may not see the entire source in long-input summarization datasets, due to their limited context window, which is shorter than the source on the long-input datasets. We now focus on input information accessible to LLMs, and only consider salient information if it falls within the context window. Besides, since the same bigram may occur multiple times throughout the source, we adjust the methodology for saliency estimation. We align sentences in generated summaries to sentences in the context, following the procedure described in (Zhou et al., 2018) and also used in (Adams et al., 2023). Specifically, we greedily select source sentences (among the ones fitting in context window) until the ROUGE-1 F1 score between the set of selected source sentences and the summary stops increasing. The resulting set of source sentences forms a proxy of the visible salient input information being
+
+rephrased by the model when summarizing. We split each truncated source document into 10 bins of the same number of sentences, and map each aligned source sentence to its bin. Note that bins are not directly comparable across models, as context length varies across models.
+
+As we can see in Fig. 2, sentences from the first  $10\%$  or last  $10\%$  of the input context are much more represented than others. A clear U-shape emerges on PubMed and SummScreenFD for all LLMs. This is intriguing knowing that the LLMs have different context window lengths, and the last  $10\%$  of each context window may contain content of varying saliency. In other words, LLMs seem to be mostly re-phrasing information from the beginning or the end of their context window.
+
+Results in Appendix E confirm an even stronger position bias with base models.
+
+# 3.3 RQ3: Does LLMs performance depend on the position of salient information?
+
+Results from the last experiment raise the question of whether LLMs' summarization performance changes depending on where salient information is located within the input. As an approximation for salient information, we consider the alignment between summary sentences and source sentences like in Fig. 2, but this time using the reference summaries. Each reference summary is mapped to the source sentences it maximizes ROUGE-1 F1 against, which may be scattered across the whole source. We convert each source sentence to its cumulative word count from the beginning of the source, and take the average as an approximation of the mean position of salient information within the source. We keep data points with mean salient position fitting within the LLM context window.
+
+We examine performance changes with regard to this salient position. To do so, we compute the Spearman correlation coefficient between salient
+
+<table><tr><td>Metric</td><td>Model</td><td>CNN/DM</td><td>XSum</td><td>Reddit</td><td>SAMSum</td><td>Multi-X</td><td>AVG</td><td>Arxiv</td><td>PubMed</td><td>GovReport</td><td>SummScreenFD</td><td>Multi-N</td><td>AVG</td></tr><tr><td rowspan="6">ROUGE-2</td><td>Flan-UL2</td><td>-0.296</td><td>-0.124</td><td>0.048</td><td>-0.069</td><td>-0.201</td><td>-0.128</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>-0.160</td><td>-0.023</td><td>0.063</td><td>-0.059</td><td>-0.100</td><td>-0.056</td><td>0.022</td><td>-0.113</td><td>-0.109</td><td>-0.079</td><td>-0.210</td><td>-0.098</td></tr><tr><td>Llama-2-13B</td><td>-0.166</td><td>-0.086</td><td>0.031</td><td>-0.078</td><td>-0.039</td><td>-0.068</td><td>-0.017</td><td>-0.081</td><td>-0.166</td><td>-0.139</td><td>-0.213</td><td>-0.123</td></tr><tr><td>Xgen-7B</td><td>-0.228</td><td>-0.042</td><td>0.066</td><td>-0.039</td><td>-0.041</td><td>-0.056</td><td>0.028</td><td>-0.091</td><td>-0.405</td><td>0.063</td><td>-0.283</td><td>-0.138</td></tr><tr><td>Mistral-7B</td><td>-0.289</td><td>-0.031</td><td>0.006</td><td>-0.024</td><td>-0.052</td><td>-0.078</td><td>-0.270</td><td>-0.279</td><td>-0.585</td><td>-0.132</td><td>-0.324</td><td>-0.318</td></tr><tr><td>GPT-3.5</td><td>-0.323</td><td>-0.027</td><td>-0.031</td><td>-0.097</td><td>0.088</td><td>-0.078</td><td>0.026</td><td>-0.093</td><td>-0.123</td><td>-0.061</td><td>-0.233</td><td>-0.097</td></tr><tr><td rowspan="6">BERTScore</td><td>Flan-UL2</td><td>-0.331</td><td>-0.185</td><td>0.062</td><td>-0.144</td><td>-0.399</td><td>-0.187</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>-0.173</td><td>-0.012</td><td>0.062</td><td>-0.130</td><td>-0.385</td><td>-0.128</td><td>-0.031</td><td>-0.203</td><td>-0.104</td><td>-0.067</td><td>-0.256</td><td>-0.132</td></tr><tr><td>Llama-2-13B</td><td>-0.193</td><td>-0.102</td><td>0.038</td><td>-0.089</td><td>-0.352</td><td>-0.140</td><td>-0.082</td><td>-0.209</td><td>-0.063</td><td>-0.152</td><td>-0.279</td><td>-0.157</td></tr><tr><td>Xgen-7B</td><td>-0.252</td><td>-0.106</td><td>0.046</td><td>-0.075</td><td>-0.343</td><td>-0.146</td><td>-0.017</td><td>-0.125</td><td>-0.353</td><td>-0.093</td><td>-0.345</td><td>-0.187</td></tr><tr><td>Mistral-7B</td><td>-0.278</td><td>-0.052</td><td>0.014</td><td>-0.108</td><td>-0.416</td><td>-0.168</td><td>-0.348</td><td>-0.367</td><td>-0.567</td><td>-0.356</td><td>-0.403</td><td>-0.408</td></tr><tr><td>GPT-3.5</td><td>-0.280</td><td>-0.021</td><td>-0.025</td><td>-0.174</td><td>-0.247</td><td>-0.149</td><td>-0.087</td><td>-0.210</td><td>-0.175</td><td>-0.142</td><td>-0.262</td><td>-0.175</td></tr><tr><td rowspan="6">A3CU</td><td>Flan-UL2</td><td>-0.258</td><td>-0.090</td><td>0.050</td><td>-0.123</td><td>-0.069</td><td>-0.098</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>-0.182</td><td>-0.076</td><td>0.028</td><td>-0.121</td><td>-0.090</td><td>-0.088</td><td>-0.038</td><td>-0.209</td><td>-0.154</td><td>-0.129</td><td>-0.217</td><td>-0.149</td></tr><tr><td>Llama-2-13B</td><td>-0.190</td><td>-0.098</td><td>0.009</td><td>-0.166</td><td>0.016</td><td>-0.086</td><td>-0.104</td><td>-0.232</td><td>-0.111</td><td>-0.198</td><td>-0.228</td><td>-0.175</td></tr><tr><td>Xgen-7B</td><td>-0.212</td><td>-0.130</td><td>0.023</td><td>-0.126</td><td>-0.025</td><td>-0.094</td><td>-0.036</td><td>-0.255</td><td>-0.211</td><td>-0.076</td><td>-0.287</td><td>-0.173</td></tr><tr><td>Mistral-7B</td><td>-0.291</td><td>-0.110</td><td>0.004</td><td>-0.105</td><td>-0.119</td><td>-0.126</td><td>-0.160</td><td>-0.283</td><td>-0.305</td><td>-0.010</td><td>-0.283</td><td>-0.208</td></tr><tr><td>GPT-3.5</td><td>-0.256</td><td>-0.022</td><td>-0.039</td><td>-0.216</td><td>0.141</td><td>-0.078</td><td>-0.064</td><td>-0.293</td><td>-0.201</td><td>-0.059</td><td>-0.264</td><td>-0.176</td></tr><tr><td rowspan="6">SummaC</td><td>Flan-UL2</td><td>-0.012</td><td>0.548</td><td>0.270</td><td>0.186</td><td>-0.035</td><td>0.191</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>0.088</td><td>0.552</td><td>0.375</td><td>0.227</td><td>0.224</td><td>0.293</td><td>0.090</td><td>0.108</td><td>0.126</td><td>-0.020</td><td>0.205</td><td>0.102</td></tr><tr><td>Llama-2-13B</td><td>0.162</td><td>0.556</td><td>0.394</td><td>0.173</td><td>0.096</td><td>0.276</td><td>0.090</td><td>0.265</td><td>0.192</td><td>-0.144</td><td>0.232</td><td>0.127</td></tr><tr><td>Xgen-7B</td><td>0.001</td><td>0.161</td><td>0.220</td><td>0.117</td><td>0.004</td><td>0.101</td><td>-0.208</td><td>-0.087</td><td>-0.313</td><td>-0.141</td><td>0.046</td><td>-0.141</td></tr><tr><td>Mistral-7B</td><td>-0.045</td><td>0.515</td><td>0.149</td><td>0.069</td><td>0.154</td><td>0.128</td><td>-0.250</td><td>-0.103</td><td>-0.387</td><td>0.124</td><td>-0.010</td><td>-0.125</td></tr><tr><td>GPT-3.5</td><td>0.156</td><td>0.590</td><td>0.444</td><td>0.180</td><td>0.008</td><td>0.276</td><td>0.058</td><td>0.237</td><td>0.061</td><td>-0.055</td><td>0.089</td><td>0.078</td></tr><tr><td rowspan="6">GPT-3.5</td><td>Flan-UL2</td><td>-0.020</td><td>0.196</td><td>0.027</td><td>-0.009</td><td>-0.193</td><td>0.000</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>0.036</td><td>0.036</td><td>-0.152</td><td>0.077</td><td>-0.153</td><td>-0.031</td><td>0.008</td><td>-0.116</td><td>-0.013</td><td>-0.068</td><td>-0.120</td><td>-0.062</td></tr><tr><td>Llama-2-13B</td><td>0.039</td><td>0.072</td><td>-0.038</td><td>0.066</td><td>-0.052</td><td>0.017</td><td>-0.010</td><td>-0.084</td><td>-0.084</td><td>-0.060</td><td>-0.051</td><td>-0.058</td></tr><tr><td>Xgen-7B</td><td>0.056</td><td>-0.007</td><td>-0.101</td><td>0.006</td><td>-0.174</td><td>-0.044</td><td>-0.058</td><td>-0.096</td><td>-0.317</td><td>-0.063</td><td>-0.055</td><td>-0.118</td></tr><tr><td>Mistral-7B</td><td>-0.115</td><td>0.124</td><td>-0.133</td><td>0.036</td><td>-0.204</td><td>-0.058</td><td>-0.446</td><td>-0.322</td><td>-0.580</td><td>-0.188</td><td>-0.163</td><td>-0.342</td></tr><tr><td>GPT-3.5</td><td>0.024</td><td>0.014</td><td>-0.108</td><td>0.131</td><td>-0.054</td><td>0.001</td><td>0.156</td><td>-0.008</td><td>-0.172</td><td>0.068</td><td>0.024</td><td>0.014</td></tr></table>
+
+Table 2: Spearman correlation coefficient between each LLM's metric, and the mean position of salient information within the context window. Flan-UL2 is not applied to long-context summarization datasets due to its too short context window. Multi-X is short for Multi-XScience, Multi-N is Multi-News dataset, AVG columns represent the average over standard-length and long-input datasets, respectively. Numbers in gray correspond to non-significant Spearman scores (p-value greater than 0.05).
+
+position and each evaluation metric in Table 2. A high absolute Spearman value means that summary quality (as measured by this metric) can change (and deteriorate) with the position of important information within the context.
+
+There are several takeaway findings from this Table. First, we notice that on standard-length datasets, reference-based evaluation metrics are negatively correlated to position of salient information. The correlation is only moderate, yet remarkably consistent across datasets (except Reddit-TIFU) and models. This is surprising, since such datasets fit entirely in context and are not affected by truncation. In contrast, reference-free metrics show either no significant or positive correlation to information position. For long-input datasets, the negative trend for reference-based metrics is confirmed. On these lengthy datasets, SummaC and GPT-3.5 tend to switch from positive to negative correlation, especially for Xgen-7B and Mistral-7B. We highlight that since GPT-3.5 itself is affected by the middle-curse from Liu et al. (2023a), it may not accurately evaluate summarization when salient content lays in the middle of the context. In light of these results, we conservatively conclude that LLMs' summarization performance is sensitive to the position of salient information in the context window.
+
+# 4 Analysis
+
+# 4.1 How is information in the middle treated?
+
+Previous experiments show that LLMs place more emphasis on the beginning and the end of their context. We now narrow down on how LLMs treat the middle. To remove the effect of spread of salient information, we perform two controlled experiments in multi-document summarization. This setup enables us to shuffle the order of the input, which is not realistic for the single-document setup as it would break coherence. We only consider data points with the same number  $k$  of documents:  $k = 7$  documents for Multi-XScience ( $n = 329$ ), and  $k = 5$  documents for Multi-News ( $n = 219$ ).
+
+In the first experiment, we vary the position of salient information throughout the input. We keep a single document (the abstract of the query paper on Multi-XScience, and the document with the highest BERTScore with the reference on Multi-News), and place it at position  $j$  for  $j \in \{1, \dots, k\}$ , using  $k - 1$  documents from a random data point for the other slots. The single relevant document is accompanied by a [RELEVANT] header, while the other documents have an [IRRELEVANT] header, and we prompt the LLM to only summarize the relevant document. For reference-free evaluation
+
+![](images/7aca6fd2626dbb6539e862e09a4d1a54ef960d5f00ae6b8188a285d5e8003a59.jpg)
+
+![](images/0b2a6a3eafaf53027a92a715f60ab39ca728a9e7caa8555576a6ec5d42620a8a.jpg)
+
+![](images/67bdf8dd816f16a10b8e8b97bd8fbdffd470c39ca2d5fe65c81dd1aa10f99e27.jpg)
+
+![](images/5f640e8ff7913f8f682463c3e315410da68725982072abd296e0befb8524a234.jpg)
+
+![](images/d201d56b40864d122ee42f0ce16014972c48603e343485799102f7bdcfa48143.jpg)
+
+![](images/6ef623ea575b81eab670fb2e2a975251bb882deafa0c60505fbf7f9e1b549264.jpg)  
+Figure 3: Multi-document summarization performance on Multi-XScience (top row) and Multi-News (bottom row) when a unique relevant document is used, and its position is varied (x-axis). Dashed horizontal lines correspond to the random baseline.
+
+![](images/b0444b0f2898ebcc2152b58dc6789d7c3a21159c7209602abb81ac715e77e1d5.jpg)
+
+![](images/be0840fd16e622634cf81403359f2c63b2323ec9642d92ea1f4bbd05fdcb546e.jpg)
+
+![](images/bf7931b5e84b0b3f33816fda49b26c1c95650368db3026911f330be7b8aaf8b3.jpg)
+
+![](images/b189ac0b0c7420aeff9f1cdd07ad61fbc00e831abc10710c2f3707fafa8fbc3a.jpg)
+
+![](images/e151b06da08de920ca03d63bd2b775052411632d32a0719c2d9e0c58d1dc23bb.jpg)
+
+![](images/cc8230415031b27fd2fcff4941da1877bd89eec2633a25248053cc8abcfc429f.jpg)
+
+![](images/03e2519f20a2d179a3b7c54acdae900dd998e517e47a67f9fe4a6bc570345600.jpg)
+
+![](images/96cdd819f3c8333ab808eb92fafd1357a9e80b93756038bace7b8973c2179cf7.jpg)  
+Figure 4: Fine-grained evaluation of multi-document summarization on Multi-News with GPT-3.5 when varying the position of a unique relevant input document.
+
+![](images/a209c49ddce2ebd39b7e3f9eaf0c01ee7f27e9cc575acc55088416e129708457.jpg)
+
+![](images/ba1b6bddc2a33292a693313d7a1fc92b3de8ee4c3572380500be22b5a2c480d5.jpg)
+
+metrics, we use the single relevant document as source. We also include a random baseline of shuffled inputs and model predictions. In Fig. 3, we see a noticeable drop in performance for all metrics when the salient document is not in the first or final position. Flan-UL2 seems to focus on the end of the context, Xgen-7B and Mistral-7B on the beginning, and Llama-2 models and GPT-3.5 on both. Performance can fall quite below random range, especially for reference-free metrics, confirming the worrying trend from Liu et al. (2023a).
+
+A more fine-grained analysis with GPT-3.5 in Fig. 4 evaluating specific attributes (following the
+
+<table><tr><td>Dataset</td><td>Model</td><td>Input documents</td><td>R-2</td><td>BS</td><td>A3CU</td><td>SummaC</td><td>GPT-3.5</td><td>%</td></tr><tr><td rowspan="15">Multi-X</td><td rowspan="3">Llama-2-7B</td><td>All 7</td><td>4.64</td><td>82.83</td><td>5.88</td><td>54.56</td><td>4.17</td><td>100.00</td></tr><tr><td>First + last</td><td>4.62</td><td>82.82</td><td>5.78</td><td>47.00</td><td>4.50</td><td>98.36</td></tr><tr><td>First + 5 random + last</td><td>4.43</td><td>82.64</td><td>5.37</td><td>43.25</td><td>4.01</td><td>92.40</td></tr><tr><td rowspan="3">Llama-2-13B</td><td>All 7</td><td>4.78</td><td>83.00</td><td>6.64</td><td>42.62</td><td>4.35</td><td>100.00</td></tr><tr><td>First + last</td><td>4.73</td><td>82.86</td><td>5.76</td><td>43.74</td><td>4.53</td><td>98.46</td></tr><tr><td>First + 5 random + last</td><td>4.61</td><td>82.80</td><td>5.72</td><td>46.42</td><td>4.31</td><td>98.07</td></tr><tr><td rowspan="3">Xgen-7B</td><td>All 7</td><td>5.37</td><td>82.68*</td><td>6.59</td><td>44.34*</td><td>4.19*</td><td>100.00</td></tr><tr><td>First + last</td><td>5.01</td><td>82.73</td><td>5.86</td><td>49.08</td><td>4.45</td><td>99.83</td></tr><tr><td>First + 5 random + last</td><td>3.89</td><td>82.16</td><td>5.03</td><td>55.29</td><td>3.01</td><td>88.93</td></tr><tr><td rowspan="3">Mistral-7B</td><td>All 7</td><td>5.40</td><td>82.60</td><td>6.35</td><td>63.78</td><td>4.26</td><td>100.00</td></tr><tr><td>First + last</td><td>5.12</td><td>82.67</td><td>6.15</td><td>60.91</td><td>4.76</td><td>99.80</td></tr><tr><td>First + 5 random + last</td><td>4.45</td><td>82.40</td><td>5.35</td><td>58.19</td><td>4.06</td><td>90.59</td></tr><tr><td rowspan="3">GPT-3.5</td><td>All 7</td><td>5.26*</td><td>83.45*</td><td>7.71</td><td>35.66*</td><td>4.59*</td><td>100.00</td></tr><tr><td>First + last</td><td>4.75</td><td>83.04</td><td>6.05</td><td>39.81</td><td>4.69</td><td>96.42</td></tr><tr><td>First + 5 random + last</td><td>4.37</td><td>82.90</td><td>5.67</td><td>43.26</td><td>4.63</td><td>95.63</td></tr><tr><td rowspan="15">Multi-N</td><td rowspan="3">Llama-2-7B</td><td>All 5</td><td>10.76</td><td>85.04</td><td>19.06</td><td>60.09</td><td>4.00</td><td>100.00</td></tr><tr><td>First + last</td><td>9.50</td><td>84.43</td><td>15.88</td><td>54.52</td><td>3.80</td><td>91.32</td></tr><tr><td>First + 3 random + last</td><td>7.57</td><td>83.36</td><td>12.39</td><td>50.35</td><td>2.94</td><td>78.13</td></tr><tr><td rowspan="3">Llama-2-13B</td><td>All 5*</td><td>10.42*</td><td>84.60*</td><td>18.15</td><td>57.26*</td><td>3.83*</td><td>100.00</td></tr><tr><td>First + last</td><td>9.55</td><td>84.58</td><td>16.99</td><td>49.84</td><td>3.73</td><td>93.93</td></tr><tr><td>First + 3 random + last</td><td>8.27</td><td>83.79</td><td>14.84</td><td>50.09</td><td>3.18</td><td>86.14</td></tr><tr><td rowspan="3">Xgen-7B</td><td>All 5</td><td>9.04*</td><td>83.18*</td><td>17.05</td><td>60.55*</td><td>3.32*</td><td>100.00</td></tr><tr><td>First + last</td><td>7.82</td><td>83.27</td><td>14.18</td><td>51.59</td><td>3.60</td><td>92.68</td></tr><tr><td>First + 3 random + last</td><td>6.30</td><td>81.85</td><td>11.66</td><td>49.02</td><td>2.66</td><td>79.51</td></tr><tr><td rowspan="3">Mistral-7B</td><td>All 5*</td><td>9.52*</td><td>83.55*</td><td>17.03</td><td>63.02*</td><td>3.15*</td><td>100.00</td></tr><tr><td>First + last</td><td>9.11</td><td>83.69</td><td>14.99</td><td>67.14</td><td>3.51</td><td>100.37</td></tr><tr><td>First + 3 random + last</td><td>6.59</td><td>81.66</td><td>12.50</td><td>52.14</td><td>2.45</td><td>80.17</td></tr><tr><td rowspan="3">GPT-3.5</td><td>All 5*</td><td>10.26*</td><td>85.06*</td><td>18.45*</td><td>49.21*</td><td>4.09*</td><td>100.00</td></tr><tr><td>First + last</td><td>8.94</td><td>84.52</td><td>15.85</td><td>45.94</td><td>4.01</td><td>92.76</td></tr><tr><td>First + 3 random + last</td><td>8.53</td><td>84.31</td><td>15.33</td><td>44.67</td><td>3.80</td><td>89.81</td></tr></table>
+
+Table 3: Performance in multi-document summarization on Multi-XScience (7 documents) and Multi-News (5 documents) when infilling the middle of the context window with random documents. R-2 is ROUGE-2, BS refers to BERTScore. % is the mean relative performance across all metrics compared to the baseline with all documents.
+
+method in Adams et al. (2023), see Appendix B) reveals more details. Coherence and quality remain high and stable. In other words, the text outputs from the LLMs are always of good quality. But for informativeness and attributability, the U-shape appears again: it shows that the LLMs (even the powerful GPT-3.5) are struggling to generate content specifically sticking to the document inserted in the middle.
+
+In the second experiment, we take the opposite approach, and put salient information at the beginning and the end, while the middle of the prompt
+
+![](images/dc8abceb852ffd183ec5b5492fd38d4be54c1285fa45e093018f3d8e231072d8.jpg)  
+Figure 5: Reference-based evaluation on the MiddleSum dataset. We also report (gray bars) performance achieved by uniformly sampling subsets of the same size as MiddleSum from the original datasets, alongside bootstrapping variance (black lines).
+
+![](images/1ece726d220b8f5a208aa24acf9b19f9419086a2e8568f02fd7abb793bd9c164.jpg)
+
+![](images/7d501842a249d6bb1355ca04ac8560f9cc4320ce46ce306abddb81cdf46a831b.jpg)
+
+is filled with noise. We keep the first and last documents, and fill the  $k - 2$  middle ones with random documents. We also run a baseline just using the first and last documents as input, expected to be close to the result with random documents in between. As displayed in Table 3, filling with random noise between the first and last document (which amounts to a prompt mostly irrelevant to the reference) leads to a moderate drop in performance. For instance, on Multi-XScience, with 5 random documents between the first and last, Llama-2-13B maintains  $98\%$  of its performance, and reaches a GPT-3.5 score of 4.31 as compared to 4.35 when using all 7 documents.
+
+We conclude from these two experiments that LLMs can focus on the beginning and/or the end of their input, but largely ignore the middle. The U-shape or middle curse from Liu et al. (2023a) also applies to abstractive summarization.
+
+# 4.2 Can we alleviate the middle curse?
+
+To evaluate the loss of performance due to the middle curse in a natural setup, we subsample data points from each of the 5 long-input summarization datasets. We obtain sentences from the (untruncated) source aligned with the reference summary, following the procedure from §3.2. Only data points where the start index of the earliest aligned source sentence is at least 1,200 words, are kept, ensuring no salient information at the start. We randomly sample 50 data points from each of Arxiv, PubMed, GovReport and Multi-News, and 25 from SummScreenFD, forming an evaluation dataset of 225 samples which we name MiddleSum.
+
+We evaluate LLMs on MiddleSum, keeping only reference-based evaluation as the dataset is built using saliency with regard to the reference. As expected, in Fig. 5 we see that LLMs perform noticeably worse on MiddleSum (green bars) as compared to the full set (gray bars), confirming that MiddleSum is a more challenging task.
+
+We benchmark alternative inference methods on MiddleSum: hierarchical summarization and incremental summarization, both of which are explored in the concurrent work of Chang et al. (2023). Namely, let us divide an input  $\pmb{x}$  of length  $n$  into  $k$  consecutive blocks of size at most  $m$  (yielding  $k = \left\lceil \frac{n}{m} \right\rceil$ ):  $\pmb{x} = (\pmb{x}_1, \dots, \pmb{x}_k)$ .
+
+Hierarchical summarization consists in summarizing each block and then summarizing the concatenation of summaries:
+
+$$
+\boldsymbol {y} _ {i} = \operatorname {L L M} \left(\boldsymbol {x} _ {i}\right) \quad \forall i \in \{1, \dots , k \} \tag {1}
+$$
+
+$$
+\boldsymbol {y} = \operatorname {L L M} \left(\boldsymbol {y} _ {1}, \dots , \boldsymbol {y} _ {k}\right) \tag {2}
+$$
+
+Incremental summarization consists in updating a summary of the text so far with content from the current text block (we have  $\mathbf{y}_0 = \emptyset$ ):
+
+$$
+\boldsymbol {y} _ {i} = \operatorname {L L M} \left(\boldsymbol {y} _ {i - 1}, \boldsymbol {x} _ {i}\right) \forall i \in \{1, \dots , k \} \tag {3}
+$$
+
+$$
+\boldsymbol {y} = \boldsymbol {y} _ {k} \tag {4}
+$$
+
+In both methods, the final output is  $\pmb{y}$ . Noting  $l$  the output length, standard inference has complexity in  $\mathcal{O}(l.n^2)$ , while both alternative methods have complexity in  $\mathcal{O}(k.l.m^2) = \mathcal{O}(l.n.m)$ , which is lower. For both methods and all models, we use a block size  $m$  of 1,500 words (roughly 2,000 tokens), and preserve coherence by ending blocks at the earliest end of sentence reaching the length.
+
+Results are shown in Fig. 5 (blue and purple bars), with detailed numbers in Appendix F. We also compare to a baseline consisting in adding the prompt Please also pay attention to the middle section of the input when constructing the summary, which we refer to as the Focus prompt (brown bars). Both alternative methods show promising results on open-source LLMs, notably on Mistral-7B for which they improve performance significantly. However, they are not successful and lag behind
+
+![](images/c92597154a1f187c8aa843aba7b212b231c368e19bb0de786032385b27322bb5.jpg)
+
+![](images/df18912073cab3e716e9448589a09cccfb58b8d9efc33bd6a7f4801aadb46cee.jpg)
+
+![](images/1d7798075ea8e97437b009c07958370d028ed2b1e8ecc8238e2911284baadb9e.jpg)
+
+![](images/15dd45ab44336d2285535cad1a41668a1e7071b3f05fa60eb27c22c494fa3b9e.jpg)
+
+![](images/b70bbb42d7eb388b06410cf3138d866399678f78ccf21eec8165a4e1e0b39efd.jpg)
+
+Figure 6: Long-input summarization performance on Arxiv (top) and GovReport (bottom) with 5 LLMs and all 5 metrics. X-axis represents the truncated maximum source length. Xgen-7B and Mistral-7B cannot infer beyond 8k tokens.  
+![](images/1c8265ccc191be16f51d44db7e16a68f5c8401d5d7f1f8163f4da59d5963f4f2.jpg)  
+Xgen-7B (8k)  $\rightarrow$  Mistral-7B (8k)  $\rightarrow$  Vicuna-1.5-7B-16k  $\rightarrow$  Llama-2-7B-32k  $\rightarrow$  GPT-3.5 (16k)
+
+![](images/2deb43f580a90aacfa868909f3ee67fcb1aebb044bcbe13f15a5c8855a192a6a.jpg)
+
+![](images/90c93d4d2962bc2bc34a9029667304e77391242925709a4c427c5bc97c2dd390.jpg)
+
+![](images/131c58bbff424af60ef38692d6966783260f43d54819f5848582bc394876e130.jpg)
+
+![](images/d77d491c62b271b75459bf65b050a91f59181569f0e5c0c4ba40a59d4b62b080.jpg)
+
+Focus Prompt with GPT-3.5. Across domains (see Table 8), hierarchical and incremental inference are very effective on scientific publications, which we hypothesize is due to the natural division in structured sections of such inputs. Yet, they seem to harm summaries on the other domains.
+
+# 4.3 Is scaling context length really useful?
+
+Experiments from §4.1 confirm that LLMs struggle to summarize information contained in the middle of their context window. This poses issues for long-input summarization: after the initial part with (usually) high saliency, important information becomes sparser, and at the same time LLMs processing capability weakens. To investigate this issue, we infer long-document summarization with length truncated at  $m * 2k$  tokens, varying  $m$  from 1 to 6. We use our longest context LLMs {Xgen-7B, Mistral-7B, GPT-3.5}; as well as two open-source LLMs extending Llama-2-7B context window with position interpolation (Chen et al., 2023), a method gaining traction as an efficient way to scale LLMs' context window. We use Vicuna-7B-1.5-16k $^6$ , and Llama-2-7B-32k $^7$ , with context of 16k tokens and 32k tokens, respectively.
+
+Results on Arxiv and GovReport in Fig. 6 confirm our intuition: all metrics plateau or even decrease (see Mistral-7B) from 4k context window upwards. Two conflicting forces are at play when increasing length: giving more information to the model helps it retrieve key elements further to make
+
+a richer summary, while at the same time reasoning over a longer context is more challenging. Yet, such a drop for Xgen-7B and Mistral-7B at 8k inference length is concerning. Both position interpolated models show more robustness ; while GPT-3.5 seems to plateau at 8k tokens. Our results suggest that in the current LLMs inference and evaluation framework, there is no need to exceed 4k tokens in the context window for open-source model.
+
+# 4.4 Does the decoding method impact the middle curse?
+
+We now turn our attention to the process controlling summary generation. While we had sampled all summaries with top-k sampling with  $k = 50$  and temperature  $T = 0.3$  so far; we now also experiment with greedy decoding, and top-p sampling (where we use  $p = 0.95$  and temperature  $T = 1.0$ ).
+
+In Fig. 7, we reproduce the salient bigrams and sentences alignment experiments from §3.1 and §3.2, respectively, with the aforementioned decoding methods. As seen, the decoding method does not affect position bias: for all setups, the LLMs show similar patterns as with our previous default decoding method. We conclude that the middle curse is independent from the decoding method.
+
+# 5 Related Work
+
+Summarization with LLMs It is widely acknowledged that LLMs have propelled forward abstractive summarization research (Pu et al., 2023), with their summaries being highly rated by human annotators (Goyal et al., 2022; Zhang et al.,
+
+![](images/622cbd9a08942fe28acdb1d4f7970fa83cabb43abebe495645f10716c75d4dd1.jpg)
+
+![](images/1f6ef586b977690a34f6feead2c724df1315bd523dc42b84a1404e9e7afcd2fe.jpg)
+
+![](images/b3d389f4d97cb11959370ee5fd2f6da753c98e5379b4a672d6fa431e18c89833.jpg)
+
+![](images/ab0c6aeb191c6ba18c3638513ce5a37c874ccabca8f9554633d7a43f97c672f5.jpg)
+
+![](images/200b70d52fcd0c88acac671030708074fdf8715a732c7783731b2f17f653f790.jpg)  
+Figure 7: Summary bigrams (top) and aligned source sentences (bottom) distribution on Arxiv and GovReport for Llama-2-7B and XGen-7B, for several decoding strategies.
+
+2023b). Liu et al. (2023d) proposes to train smaller models like BART (Lewis et al., 2020) or BRIO (Liu et al., 2022b) with contrastive learning using LLMs like ChatGPT as evaluator providing signal on which generated summary candidate is better. Summary chain-of-thought designs a custom chain-of-thought method which first prompts the LLM to list important facts, then integrates these facts into a coherent summary (Wang et al., 2023c). SummIt utilizes ChatGPT to iteratively write then refine summaries given feedback from an evaluator LLM (Zhang et al., 2023a). Chain-of-density gradually makes GPT-4 generated summaries contain more and more entities while keeping length budget constant, creating more informative albeit a bit less readable summaries (Adams et al., 2023). Ravaut et al. (2022) noticed that data points with higher compression are generally harder to summarize with pre-trained models.
+
+Position bias in LLMs Sun et al. (2021) showed that for Transformer-based models, most recent tokens play a greater role compared to older tokens for next-token prediction. It was later found that for in-context learning, the order of examples within the prompt impacts GPT-3's performance (Liu et al., 2022a; Lu et al., 2021). Reliance on positional information affects LLMs capabilities in arithmetic (Shen et al., 2023b), in multiple
+
+choice question-answering (Zheng et al., 2023; Pezeshkpour and Hruschka, 2023), and as text generation evaluators (Wang et al., 2023b); making it hard to rank LLMs (Alzahrani et al., 2024). Liu et al. (2023a) were the first to show that LLMs' performance weakens in the middle of the prompt (the middle curse), yet, how LLMs make use of their full context window remains poorly understood. The passkey retrieval evaluation, which consists in prompting the LLM to recall a complex string or long number inserted in its prompt, is becoming popular recently as a way of verifying LLM's processing capability at each position (Liu et al., 2023a; Jiang et al., 2024). However, this task does not measure position bias on complex, abstract reasoning tasks like summarization. A line of work attempts to solve the middle curse through compressing the prompt (Jiang et al., 2023b,c), with very promising results albeit at the cost of prompt fluency. Another approach marginalizes results over different permutations of the input to suppress dependency on input order (Tang et al., 2023). Concurrent work to ours also finds that in zero-shot summarization, LLMs tend to prefer lead content (Chhabra et al., 2024).
+
+# 6 Conclusion
+
+Behind the recent hype around LLMs and their amazing instruction following and content generation capabilities, our study showcases a major weakness in abstractive summarization: LLMs suffer from the middle curse and struggle to use information in the middle of their context window. LLMs do not make a consistent use of their context window as they mostly look at the beginning and (to a lesser extent) the end, which at first glance may be hidden by the prevalent lead bias in summarization datasets. Extending context window beyond 4k tokens, which has been an intense area of focus lately, is not justified in the current inference and evaluation setup in abstractive summarization. We benchmarked two alternative inference methods (hierarchical inference and incremental inference) on MiddleSum, an evaluation subset designed to showcase the middle curse. Despite encouraging results on scientific paper datasets, these methods are far from a silver bullet to the middle curse. We call for a better evaluation of LLMs, which accounts for the salient spans of the source which are effectively being processed.
+
+# Limitations
+
+Our work only considers open-source LLMs for summary generation and ignores closed-source LLMs such as the popular OpenAI's GPT-3.5 and GPT-4, or Anthropic's Claude. We made this decision to advocate for openness in LLM research; yet we acknowledge that it would be interesting to also investigate properties of summaries generated by these paying API LLMs.
+
+Another limitation lays in the saliency estimation. We approximate salient content in the source through maximizing ROUGE-1 overlap with summary sentences. Other methods are also well-suited for this task, albeit at greater computational cost; for instance semantic similarity through BERTScore or BARTScore; or saliency estimation through a LLM in zero-shot.
+
+Lastly, we can only evaluate a finite number of LLMs, and we settled for the evaluation of 5 recent and popular open-source LLMs. Findings may change as LLMs undergo changes and improvements in their training process.
+
+# Acknowledgements
+
+This research was supported by the SINGA scholarship and partially supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation, Singapore.
+
+We thank anonymous ARR Reviewers 1 and 3 for their great review, which have led us to include plenty more important results enriching our analysis. We also thank the AC for their constructive meta-review. We thank Florian Le Bronnec and Ruochen Zhao for their helpful proof-reading of the draft.
+
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+
+# A Statistics
+
+In Table 4, we include statistics on each of the abstractive summarization datasets under consideration. We use the non-anonymized version for CNN/DM (See et al., 2017). For Reddit-TIFU, we use the Long subset, and SummScreenFD, is the ForeverDreaming (FD) subset of SummScreen. GovReport and SummScreenFD are part of the long-input benchmarks Scrolls (Shaham et al., 2022) and ZeroScrolls (Shaham et al., 2023).
+
+In Fig. 8, we illustrate how much of the source document(s) is visible with a 4k context window (Llama-2).
+
+# B GPT-3.5 Evaluation
+
+To evaluate LLM-generated summaries with GPT-3.5, we use the following prompt template:
+
+Score the following summary generated by another system given the source on a scale from 1 to 5 with regards to overall general summary quality. 1-point indicates a low quality summary, and 5 points a very high quality summary. A high quality summary is grammatical, fluent, informative, relevant, coherent and factually consistent with the source. Let's think step-by-step and just output the score.
+
+Source:
+
+[source]
+
+Instruction:
+
+Summarize the above text in [n] sentences.
+
+Summary:
+
+[summary generated by the LLM to evaluate]
+
+Your score:
+
+When evaluating for the specific aspect of informativeness, the first paragraph becomes:
+
+Score the following summary generated by another system given the source on a scale from 1 to 5 with regards to how informative the summary is. 1 point indicates a not informative summary, and 5 points a very informative summary. An informative summary captures the important information in the article and presents it accurately and concisely. Let's think step-by-step and just output the score.
+
+When evaluating for the specific aspect of overall quality, the first paragraph becomes:
+
+Score the following summary generated by another system given the source on a scale from 1 to 5 with regards to its quality. 1 point indicates a low quality summary, and 5 points a very high quality summary. A high quality summary is comprehensible and understandable. Let's think step-by-step and just output the score.
+
+When evaluating for the specific aspect of coherence, the first paragraph becomes:
+
+Score the following summary generated by another system given the source on a scale from 1 to 5 with regards to its coherence. 1 point indicates an incoherent summary, and 5 points a very coherent summary. A coherent summary is well-structured and well-organized. Let's think step-by-step and just output the score.
+
+When evaluating for the specific aspect of attributability, the first paragraph becomes:
+
+Score the following summary generated by another system given the source on a scale from 1 to 5 with regards to how attributable it is. 1 point indicates a not very attributable summary, with many hallucinations, and 5 points a summary very attributable to the source, consistent with the source. In a very attributable summary, all the informa
+
+<table><tr><td rowspan="2">Dataset</td><td rowspan="2">Domain</td><td rowspan="2">#Docs</td><td colspan="3"># Data points</td><td colspan="3"># Sentences</td><td colspan="2"># Words</td><td colspan="3"># Tokens</td></tr><tr><td>Train</td><td>Val</td><td>Test</td><td>Doc.</td><td>Summ.</td><td>Inf.</td><td>Doc.</td><td>Summ.</td><td>Doc.</td><td>Summ.</td><td>Max gen.</td></tr><tr><td>CNN/DM (Hermann et al., 2015)</td><td>News</td><td>1.00</td><td>287,113</td><td>13,334</td><td>11,490</td><td>33.37</td><td>3.79</td><td>3</td><td>773.23</td><td>57.75</td><td>994.56</td><td>84.47</td><td>192</td></tr><tr><td>XSum (Narayan et al., 2018)</td><td>News</td><td>1.00</td><td>204,045</td><td>11,332</td><td>11,334</td><td>19.11</td><td>1.00</td><td>1</td><td>433.05</td><td>23.19</td><td>566.79</td><td>31.63</td><td>64</td></tr><tr><td>Reddit-TIFU (Long) (Kim et al., 2019)</td><td>Social Media</td><td>1.00</td><td>33,701</td><td>4,214</td><td>4,221</td><td>22.21</td><td>1.45</td><td>2</td><td>444.20</td><td>23.37</td><td>532.18</td><td>29.82</td><td>128</td></tr><tr><td>SAMSum (Gliwa et al., 2019)</td><td>Dialogue</td><td>1.00</td><td>14,732</td><td>818</td><td>819</td><td>8.96</td><td>2.01</td><td>2</td><td>126.93</td><td>23.12</td><td>175.54</td><td>29.69</td><td>128</td></tr><tr><td>Multi-XScience (Lu et al., 2020)</td><td>Science</td><td>5.06</td><td>30,369</td><td>5,066</td><td>5,093</td><td>30.55</td><td>4.86</td><td>5</td><td>773.36</td><td>120.65</td><td>965.99</td><td>157.77</td><td>384</td></tr><tr><td>Arxiv (Cohan et al., 2018)</td><td>Science</td><td>1.00</td><td>203,037</td><td>6,436</td><td>6,440</td><td>250.37</td><td>6.23</td><td>6</td><td>6,446.11</td><td>166.72</td><td>8,940.00</td><td>225.58</td><td>512</td></tr><tr><td>PubMed (Cohan et al., 2018)</td><td>Science (medical)</td><td>1.00</td><td>119,924</td><td>6,633</td><td>6,658</td><td>101.61</td><td>7.59</td><td>7</td><td>3,142.92</td><td>208.03</td><td>4,602.62</td><td>324.97</td><td>512</td></tr><tr><td>GovReport (Huang et al., 2021)</td><td>Legal</td><td>1.00</td><td>17,517</td><td>973</td><td>973</td><td>282.86</td><td>23.14</td><td>22</td><td>8,363.22</td><td>649.01</td><td>11,025.02</td><td>879.10</td><td>768</td></tr><tr><td>SummScreenFD (Chen et al., 2022)</td><td>TV Transcripts</td><td>1.00</td><td>3,673</td><td>338</td><td>337</td><td>727.06</td><td>5.26</td><td>5</td><td>7,618.20</td><td>123.34</td><td>10,067.39</td><td>157.44</td><td>512</td></tr><tr><td>Multi-News (Fabbri et al., 2019)</td><td>News</td><td>2.73</td><td>44,972</td><td>5,622</td><td>5,622</td><td>79.02</td><td>9.88</td><td>10</td><td>2,101.49</td><td>256.55</td><td>2,998.52</td><td>324.29</td><td>512</td></tr></table>
+
+Table 4: Statistics on the 10 datasets used for experiments. Doc. is the source document, Summ. the ground-truth summary, Inf. refers to the number of desired sentences to be in the summary prompted to each LLM during inference. Statistics are computed on the entire test set. # Tokens is calculated with Llama-2's tokenizer. Max gen. is the maximum tokens size that we set when decoding summaries. Underlined test sizes correspond to datasets where we subsample randomly 1,000 test data points for evaluation.  
+
+<table><tr><td>Model</td><td>Metric</td><td>CNN/DM</td><td>XSum</td><td>Reddit-TIFU</td><td>SAMSum</td><td>Multi-X</td><td>AVG</td><td>Arxiv</td><td>PubMed</td><td>GovReport</td><td>SummScreenFD</td><td>Multi-N</td><td>AVG</td></tr><tr><td rowspan="2">SOTA</td><td>ROUGE-2</td><td>24.17</td><td>27.09</td><td>11.13</td><td>29.88</td><td>4.60</td><td>19.37</td><td>21.93</td><td>23.26</td><td>30.90</td><td>10.70</td><td>13.60</td><td>20.07</td></tr><tr><td># sents</td><td>2.89</td><td>1.00</td><td>1.34</td><td>2.08</td><td>2.28</td><td>1.19</td><td>1.78</td><td>1.27</td><td>1.80</td><td>5.49</td><td>2.79</td><td>2.63</td></tr><tr><td rowspan="5">Flan-UL2</td><td>ROUGE-2</td><td>20.28</td><td>22.74</td><td>8.61</td><td>28.21</td><td>3.04</td><td>16.58</td><td>9.37</td><td>7.42</td><td>4.76</td><td>4.33</td><td>7.79</td><td>6.73</td></tr><tr><td>BERTScore</td><td>88.05</td><td>91.94</td><td>87.42</td><td>92.29</td><td>81.87</td><td>88.31</td><td>83.82</td><td>83.07</td><td>83.53</td><td>84.85</td><td>84.97</td><td>84.05</td></tr><tr><td>A3CU</td><td>32.69</td><td>32.11</td><td>16.89</td><td>49.48</td><td>5.98</td><td>27.43</td><td>14.79</td><td>13.83</td><td>12.00</td><td>8.37</td><td>16.99</td><td>13.20</td></tr><tr><td>SummaC</td><td>69.96</td><td>24.27</td><td>35.76</td><td>30.19</td><td>57.98</td><td>43.63</td><td>67.56</td><td>60.96</td><td>73.80</td><td>56.00</td><td>76.80</td><td>67.02</td></tr><tr><td>GPT-3.5</td><td>3.16</td><td>3.52</td><td>1.61</td><td>2.92</td><td>3.23</td><td>2.89</td><td>3.11</td><td>3.16</td><td>2.46</td><td>2.11</td><td>3.36</td><td>2.84</td></tr><tr><td rowspan="6">Llama-2-7B</td><td># sents</td><td>3.00</td><td>1.27</td><td>2.00</td><td>1.83</td><td>7.77</td><td>3.17</td><td>5.80</td><td>6.61</td><td>12.88</td><td>5.77</td><td>18.69</td><td>9.95</td></tr><tr><td>ROUGE-2</td><td>14.16</td><td>7.27</td><td>4.17</td><td>15.53</td><td>4.87</td><td>9.20</td><td>13.84</td><td>12.89</td><td>16.22</td><td>5.36</td><td>12.37</td><td>12.14</td></tr><tr><td>BERTScore</td><td>87.32</td><td>87.47</td><td>85.87</td><td>89.95</td><td>83.32</td><td>86.79</td><td>83.84</td><td>82.82</td><td>85.28</td><td>85.41</td><td>85.63</td><td>84.60</td></tr><tr><td>A3CU</td><td>29.04</td><td>14.18</td><td>12.15</td><td>35.64</td><td>6.39</td><td>19.48</td><td>16.78</td><td>16.60</td><td>17.23</td><td>9.66</td><td>22.23</td><td>16.50</td></tr><tr><td>SummaC</td><td>35.58</td><td>25.24</td><td>26.38</td><td>24.56</td><td>49.39</td><td>32.23</td><td>53.22</td><td>51.82</td><td>70.47</td><td>39.01</td><td>57.49</td><td>54.40</td></tr><tr><td>GPT-3.5</td><td>4.10</td><td>4.24</td><td>2.83</td><td>3.61</td><td>4.42</td><td>3.84</td><td>4.21</td><td>4.19</td><td>3.43</td><td>2.71</td><td>3.91</td><td>3.69</td></tr><tr><td rowspan="6">Llama-2-13B</td><td># sents</td><td>3.01</td><td>1.16</td><td>2.00</td><td>1.98</td><td>5.22</td><td>2.67</td><td>5.92</td><td>7.22</td><td>27.75</td><td>5.16</td><td>12.79</td><td>11.77</td></tr><tr><td>ROUGE-2</td><td>14.10</td><td>8.61</td><td>4.22</td><td>14.16</td><td>5.29</td><td>9.28</td><td>13.52</td><td>15.24</td><td>17.28</td><td>5.62</td><td>12.58</td><td>12.85</td></tr><tr><td>BERTScore</td><td>87.40</td><td>87.94</td><td>85.85</td><td>89.45</td><td>83.58</td><td>86.84</td><td>83.88</td><td>84.24</td><td>85.29</td><td>85.42</td><td>85.84</td><td>84.93</td></tr><tr><td>A3CU</td><td>29.57</td><td>16.30</td><td>12.94</td><td>34.19</td><td>7.29</td><td>20.06</td><td>16.44</td><td>19.21</td><td>17.01</td><td>10.30</td><td>23.09</td><td>17.21</td></tr><tr><td>SummaC</td><td>33.83</td><td>24.07</td><td>25.76</td><td>24.81</td><td>41.70</td><td>30.03</td><td>55.09</td><td>56.00</td><td>76.44</td><td>38.74</td><td>53.12</td><td>55.88</td></tr><tr><td>GPT-3.5</td><td>4.12</td><td>4.34</td><td>2.91</td><td>3.45</td><td>4.45</td><td>3.85</td><td>4.06</td><td>4.13</td><td>3.66</td><td>2.69</td><td>3.89</td><td>3.69</td></tr><tr><td rowspan="6">Xgen-7B</td><td># sents</td><td>3.93</td><td>2.24</td><td>2.62</td><td>2.34</td><td>5.94</td><td>3.41</td><td>8.07</td><td>13.50</td><td>22.46</td><td>10.60</td><td>9.05</td><td>12.74</td></tr><tr><td>ROUGE-2</td><td>14.55</td><td>6.00</td><td>3.98</td><td>14.51</td><td>5.54</td><td>8.92</td><td>12.31</td><td>13.99</td><td>14.68</td><td>4.24</td><td>11.07</td><td>11.26</td></tr><tr><td>BERTScore</td><td>87.07</td><td>87.12</td><td>85.84</td><td>89.53</td><td>83.42</td><td>86.60</td><td>83.07</td><td>82.87</td><td>83.94</td><td>83.91</td><td>84.95</td><td>83.75</td></tr><tr><td>A3CU</td><td>27.88</td><td>12.75</td><td>12.56</td><td>33.18</td><td>7.45</td><td>18.76</td><td>15.28</td><td>18.79</td><td>15.65</td><td>8.44</td><td>21.33</td><td>18.90</td></tr><tr><td>SummaC</td><td>52.25</td><td>37.95</td><td>28.40</td><td>25.63</td><td>44.36</td><td>37.72</td><td>53.28</td><td>60.56</td><td>65.22</td><td>42.03</td><td>56.45</td><td>55.51</td></tr><tr><td>GPT-3.5</td><td>3.82</td><td>3.97</td><td>2.78</td><td>3.52</td><td>4.37</td><td>3.69</td><td>3.96</td><td>3.99</td><td>2.78</td><td>2.42</td><td>3.58</td><td>3.35</td></tr><tr><td rowspan="6">Mistral-7B</td><td># sents</td><td>3.10</td><td>1.12</td><td>2.64</td><td>2.25</td><td>7.73</td><td>3.37</td><td>12.00</td><td>11.88</td><td>25.83</td><td>27.13</td><td>16.43</td><td>18.65</td></tr><tr><td>ROUGE-2</td><td>16.37</td><td>7.05</td><td>4.34</td><td>14.66</td><td>5.57</td><td>9.60</td><td>9.77</td><td>14.32</td><td>11.36</td><td>3.11</td><td>12.61</td><td>10.23</td></tr><tr><td>BERTScore</td><td>87.50</td><td>87.45</td><td>85.71</td><td>89.78</td><td>83.16</td><td>86.72</td><td>81.44</td><td>82.85</td><td>82.43</td><td>81.46</td><td>85.07</td><td>82.65</td></tr><tr><td>A3CU</td><td>30.60</td><td>13.14</td><td>13.08</td><td>32.21</td><td>6.96</td><td>19.20</td><td>12.66</td><td>16.31</td><td>14.92</td><td>8.40</td><td>22.22</td><td>14.90</td></tr><tr><td>SummaC</td><td>53.67</td><td>24.79</td><td>30.51</td><td>26.82</td><td>62.49</td><td>39.66</td><td>57.81</td><td>69.03</td><td>67.07</td><td>35.76</td><td>68.50</td><td>59.63</td></tr><tr><td>GPT-3.5</td><td>3.92</td><td>4.30</td><td>2.73</td><td>3.63</td><td>4.47</td><td>3.81</td><td>2.83</td><td>3.63</td><td>2.01</td><td>1.88</td><td>3.60</td><td>2.79</td></tr><tr><td rowspan="6">GPT-3.5</td><td># sents</td><td>3.00</td><td>1.00</td><td>2.00</td><td>1.98</td><td>4.99</td><td>2.59</td><td>5.60</td><td>6.46</td><td>15.82</td><td>5.01</td><td>9.27</td><td>8.43</td></tr><tr><td>ROUGE-2</td><td>13.17</td><td>8.19</td><td>5.17</td><td>14.47</td><td>5.37</td><td>9.27</td><td>13.78</td><td>13.90</td><td>17.94</td><td>6.55</td><td>12.28</td><td>12.89</td></tr><tr><td>BERTScore</td><td>87.26</td><td>87.90</td><td>86.38</td><td>89.79</td><td>83.82</td><td>87.03</td><td>84.14</td><td>84.29</td><td>85.99</td><td>86.13</td><td>86.02</td><td>85.31</td></tr><tr><td>A3CU</td><td>26.72</td><td>15.21</td><td>13.37</td><td>35.85</td><td>7.90</td><td>19.81</td><td>17.98</td><td>18.76</td><td>19.32</td><td>12.69</td><td>22.95</td><td>18.34</td></tr><tr><td>SummaC</td><td>35.14</td><td>23.22</td><td>26.00</td><td>24.92</td><td>37.56</td><td>29.37</td><td>50.11</td><td>47.36</td><td>73.23</td><td>40.58</td><td>48.91</td><td>52.04</td></tr><tr><td>GPT-3.5</td><td>4.12</td><td>4.58</td><td>3.08</td><td>3.81</td><td>4.61</td><td>4.04</td><td>4.41</td><td>4.36</td><td>4.03</td><td>3.39</td><td>4.14</td><td>4.07</td></tr></table>
+
+Table 5: Performance achieved by the LLMs on each dataset for all 5 metrics. # sents is the average number of sentences in generated summaries. Multi-X is short for Multi-XScience, Multi-N is Multi-News dataset, AVG columns represent the average over standard-length and long-input datasets, respectively. SOTA numbers are taken from (Xie et al., 2023) on CNN/DM, from (Zhao et al., 2022b) XSum, Reddit-TIFU and SAMSum, from (Pang et al., 2022) for Arxiv and PubMed, from (Xiong et al., 2023) for GovReport and SummScreenFD, from (Xiao et al., 2022) for Multi-News and Multi-XScience. Due to a lack of reported results for other metrics, we only include ROUGE-2 scores for SOTA models. Best scores (outside of SOTA) are in bold.
+
+![](images/a0202d3fce4d90d44d5560d8a234858685226b8aa22464d286f71827e3ccf201.jpg)
+
+![](images/dae089be0bc55c7acd986c1b259b4c38bafb10ad5a84ae2a2032d308e45e9af9.jpg)
+
+![](images/815f4c5076726cea320c5babd9a8cd356b2598d49a68a8ee2d2a3f883284725c.jpg)
+
+![](images/128c2c9a05d88af8e44bb82ef36c8ba828625b17189922ed416530b3a011accc.jpg)
+
+![](images/1eb8a5c78b7bbd7e565ef069a1fdf6c6b4df82adfe9150a8489b954a59468d78.jpg)
+
+![](images/98e9c119aa8f035cfeee7522346d3f51aa8184d9fa08f61909990567c194a703.jpg)  
+Figure 8: Fraction of the source which fits into the context window, for several context window lengths with Llama-2 tokenization. The black dashed lines correspond to Llama-2 context window length of 4k tokens. On standard-length datasets, 4k is enough to access  $100\%$  of all source documents; but on the long-input datasets such as GovReport or SummScreenFD, such a context window may not even fit  $50\%$  of the source.
+
+![](images/e96927c7c1a0a95495b374b0e91237f64c85cc3aa066e6206bceb3b3f9b0dab7.jpg)
+
+![](images/d1cbe3dc799a28abf043463d0c9f34dde32d76fb2e19cc9e79593681968e8af0.jpg)
+
+![](images/e4c2d0e7aaecaaa2bd64dd043d054eb145068865e54e3b8c0158e09c8d06ddea.jpg)
+
+![](images/4847944c33aa4a5add086f4d9bc9fef366260183fe7053220e528608dd5d9ea7.jpg)
+
+<table><tr><td>Model</td><td>CNN/DM</td><td>XSum</td><td>Reddit-TIFU</td><td>SAMSum</td><td>Multi-XScience</td><td>Arxiv</td><td>PubMed</td><td>GovReport</td><td>SummScreenFD</td><td>Multi-News</td></tr><tr><td>Flan-UL2</td><td>0.000</td><td>0.012</td><td>0.000</td><td>0.057</td><td>0.000</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Llama-2-7B</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.490</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Llama-2-13B</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Xgen-7B</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Mistral-7B</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>GPT-3.5</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.135</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr></table>
+
+Table 6: P-value of a 2-sample Kolmogorov-Smirnov test between the position distribution of bigrams in LLM-generated summaries and bigrams in reference summaries. We round numbers to 3 decimals. Numbers in gray correspond to non-significant differences (p-value above 0.01).
+
+![](images/4e6827a2c7e3584b097b0efd7597978d563df0efbb7375bcbd3505db12559a66.jpg)  
+Figure 9: Relative location of summary bigrams within the source on MiddleSum for Llama-2-13B with different inference methods.
+
+tion is fully attributable to the source. Let's think step-by-step and just output the score.
+
+# C Baseline Performance
+
+In Table 5, we report zero-shot performance with the 6 LLMs described in §2. We note that for standard-length datasets, Flan-UL2 is dominating, perhaps due to its better instruction-tuning; while for long-context ones, GPT-3.5 takes the lead. GPT-3.5 is always consistently ahead on the GPT-3.5
+
+metric, showing the preference of the LLM for its own generation when used as an evaluator.
+
+# D Statistical Significance on RQ1
+
+In Table 6, we run a 2-sample Kolmogorov-Smirnov statistical significance test to compare the bigrams position distribution of LLMs with the reference summaries.
+
+# E Results with base Models
+
+In Fig. 10, we repeat the analysis from §3.1 (bigrams alignment) and §3.2 (sentences alignment) but this time using the base model versions (not instruction-tuned) of Llama-2-7B and Llama-2-13B. As we can see, the position bias is even stronger for base models than for chatbot models, suggesting that this bias is acquired during pre-training.
+
+# F More Results on MiddleSum
+
+In Table 7, we show statistics on the MiddleSum evaluation dataset.
+
+<table><tr><td rowspan="2">Dataset</td><td rowspan="2">Domain</td><td rowspan="2"># Docs</td><td rowspan="2"># Data points</td><td colspan="2"># Sentences</td><td colspan="2"># Words</td><td colspan="2"># Tokens</td></tr><tr><td>Doc.</td><td>Summ.</td><td>Doc.</td><td>Summ.</td><td>Doc.</td><td>Summ.</td></tr><tr><td>Arxiv</td><td>Science</td><td>1.00</td><td>50</td><td>299.36</td><td>5.84</td><td>7,605.52</td><td>165.80</td><td>10,846.00</td><td>226.22</td></tr><tr><td>PubMed</td><td>Science (medical)</td><td>1.00</td><td>50</td><td>157.60</td><td>6.96</td><td>5,090.84</td><td>203.04</td><td>7,783.02</td><td>329.12</td></tr><tr><td>GovReport</td><td>Legal</td><td>1.00</td><td>50</td><td>445.64</td><td>22.80</td><td>13,308.70</td><td>656.10</td><td>17,462.90</td><td>883.10</td></tr><tr><td>SummScreen</td><td>TV transcripts</td><td>1.00</td><td>25</td><td>762.24</td><td>3.56</td><td>9,732.44</td><td>88.60</td><td>12,878.08</td><td>115.00</td></tr><tr><td>MultiNews</td><td>News</td><td>3.18</td><td>50</td><td>211.34</td><td>9.86</td><td>5,939.32</td><td>269.84</td><td>8,130.78</td><td>336.16</td></tr><tr><td>Overall</td><td>Mixed</td><td>1.48</td><td>225</td><td>332.24</td><td>10.50</td><td>8,180.13</td><td>297.57</td><td>11,258.16</td><td>407.15</td></tr></table>
+
+Table 7: Statistics on the MiddleSum evaluation dataset, breaking down on each domain. Doc. is the source document, Summ. the ground-truth summary. # Tokens is calculated with Llama-2's tokenizer.
+
+![](images/0b73f473eb01c15f989c66da53fd0bebc95aa7fb4a6607a78c79379ab759558a.jpg)
+
+![](images/e7bdad980c7778b6493a75b99f12ee70cc456dddb31ddce865b84ca7d305370e.jpg)
+
+![](images/e4d52ef489bdf02543d5eee93944e357e7dce5e8714d7a23526c42fd0103232f.jpg)
+
+![](images/10fefb476034f6f60bc262ccb83b399e56e900a6a3c2c82d12a1a2342b49c609.jpg)
+
+![](images/ddd89c2f517ac68f5bb29b90fea70eb4ae2b13fc172868d88a3a50b921824e1d.jpg)
+
+![](images/156c033aecf88f991e3d7b6c6d9c16607336fefc78636b12fac882e5ad459e8d.jpg)  
+Figure 10: Summary bigrams (top) and aligned source sentences (bottom) distribution for Llama-2-7B and Llama-2-13B on the long-input datasets, both with base (dashed lines) and chat (full lines) models.
+
+![](images/410086a411cb1cb35dd5d9821ffb6986d685a73533eaa2ab1befe780fdb80f3b.jpg)
+
+![](images/af94f1d2e4c4e8000c792198883dec0603e6115c18837df2374c17613c89aa92.jpg)
+
+![](images/ae05469c93eaa0201d1c2a7570b2c86e4192bae4d7013c539fee2fd32c39df04.jpg)
+
+![](images/b6fee170773b368f71726348f730cc704487c0b9de42ccabf25842c54397c13f.jpg)
+
+In Fig. 9, we repeat the salient bigrams analysis from Fig. 1 for Llama-2-13B on MiddleSum. We note that both hierarchical and incremental inference notably decrease reliance on lead bigrams compared to standard inference, while the simple Focus Prompt does not.
+
+In Table 8, we show reference-based evaluation on each of the 5 subsets of MiddleSum.
+
+# G Software
+
+We use the following Python libraries, all open source:
+
+- numpy, version 1.24.3  
+- torch, version 2.0.1  
+scikit-learn, version 1.0.2  
+- sentencepiece, version 0.1.97  
+- ntlk, version 3.8.1  
+- spacy, version 3.6.0  
+scipy, version 1.10.1
+
+- rouge-score, version 0.1.2  
+- bert-score, version 0.3.13  
+summac, version 0.0.03  
+- tiktoken, version 0.4.0  
+openai, version 0.28.0  
+- huggingface-hub, version 0.17.2  
+- datasets, version 2.14.5  
+accelerate, version 0.21.0  
+- tokenizers, version 0.14.1  
+- transformers, version 4.34.0
+
+<table><tr><td>Model</td><td>Metric</td><td>Inference</td><td>MiddleSum (MS)</td><td>MS/Arxiv</td><td>MS/PubMed</td><td>MS/GovReport</td><td>MS/SummScreen</td><td>MS/Multi-News</td></tr><tr><td rowspan="12">Llama-2-7B</td><td rowspan="4">ROUGE-2</td><td>Standard</td><td>10.96</td><td>12.62</td><td>10.97</td><td>13.26</td><td>4.07</td><td>10.43</td></tr><tr><td>Focus prompt</td><td>10.70</td><td>12.10</td><td>11.51</td><td>13.28</td><td>3.57</td><td>9.48</td></tr><tr><td>Hierarchical</td><td>11.33</td><td>14.63</td><td>13.36</td><td>13.51</td><td>4.85</td><td>7.06</td></tr><tr><td>Incremental</td><td>12.56</td><td>14.43</td><td>13.54</td><td>17.26</td><td>5.19</td><td>8.68</td></tr><tr><td rowspan="4">BERTScore</td><td>Standard</td><td>84.19</td><td>83.43</td><td>83.13</td><td>84.63</td><td>85.33</td><td>85.01</td></tr><tr><td>Focus prompt</td><td>84.07</td><td>83.33</td><td>83.00</td><td>84.77</td><td>85.27</td><td>84.59</td></tr><tr><td>Hierarchical</td><td>84.13</td><td>83.86</td><td>83.74</td><td>84.26</td><td>85.23</td><td>84.10</td></tr><tr><td>Incremental</td><td>84.26</td><td>83.73</td><td>83.80</td><td>84.67</td><td>85.70</td><td>84.14</td></tr><tr><td rowspan="4">A3CU</td><td>Standard</td><td>12.81</td><td>13.55</td><td>12.12</td><td>11.71</td><td>7.31</td><td>16.61</td></tr><tr><td>Focus prompt</td><td>12.41</td><td>13.54</td><td>13.39</td><td>10.84</td><td>7.15</td><td>14.52</td></tr><tr><td>Hierarchical</td><td>13.21</td><td>15.64</td><td>15.14</td><td>12.26</td><td>9.71</td><td>11.57</td></tr><tr><td>Incremental</td><td>12.88</td><td>15.34</td><td>14.45</td><td>10.51</td><td>10.24</td><td>12.55</td></tr><tr><td rowspan="12">Llama-2-13B</td><td rowspan="4">ROUGE-2</td><td>Standard</td><td>11.07</td><td>11.68</td><td>11.63</td><td>13.56</td><td>5.09</td><td>10.38</td></tr><tr><td>Focus prompt</td><td>10.82</td><td>11.51</td><td>11.30</td><td>12.42</td><td>4.83</td><td>11.03</td></tr><tr><td>Hierarchical</td><td>10.24</td><td>13.45</td><td>13.26</td><td>10.21</td><td>4.93</td><td>6.71</td></tr><tr><td>Incremental</td><td>11.90</td><td>12.53</td><td>13.84</td><td>17.34</td><td>5.06</td><td>7.33</td></tr><tr><td rowspan="4">BERTScore</td><td>Standard</td><td>84.04</td><td>83.29</td><td>83.15</td><td>84.43</td><td>85.24</td><td>84.70</td></tr><tr><td>Focus prompt</td><td>84.12</td><td>83.26</td><td>83.06</td><td>84.49</td><td>85.08</td><td>85.19</td></tr><tr><td>Hierarchical</td><td>83.17</td><td>83.74</td><td>83.75</td><td>80.07</td><td>85.03</td><td>84.20</td></tr><tr><td>Incremental</td><td>83.43</td><td>82.45</td><td>83.45</td><td>84.96</td><td>83.24</td><td>82.95</td></tr><tr><td rowspan="4">A3CU</td><td>Standard</td><td>12.94</td><td>13.01</td><td>13.42</td><td>10.92</td><td>8.47</td><td>16.65</td></tr><tr><td>Focus prompt</td><td>13.10</td><td>13.17</td><td>11.76</td><td>11.91</td><td>7.06</td><td>18.58</td></tr><tr><td>Hierarchical</td><td>12.85</td><td>16.28</td><td>14.11</td><td>10.40</td><td>8.36</td><td>12.86</td></tr><tr><td>Incremental</td><td>12.47</td><td>14.64</td><td>14.80</td><td>11.75</td><td>10.76</td><td>9.54</td></tr><tr><td rowspan="12">Xgen-7B</td><td rowspan="4">ROUGE-2</td><td>Standard</td><td>8.92</td><td>11.94</td><td>9.34</td><td>10.02</td><td>4.53</td><td>6.57</td></tr><tr><td>Focus prompt</td><td>9.79</td><td>12.87</td><td>10.79</td><td>11.46</td><td>4.15</td><td>6.85</td></tr><tr><td>Hierarchical</td><td>9.87</td><td>13.06</td><td>11.37</td><td>11.77</td><td>2.79</td><td>6.83</td></tr><tr><td>Incremental</td><td>9.11</td><td>10.97</td><td>10.55</td><td>13.16</td><td>3.71</td><td>4.44</td></tr><tr><td rowspan="4">BERTScore</td><td>Standard</td><td>82.51</td><td>82.45</td><td>81.96</td><td>82.65</td><td>83.28</td><td>82.60</td></tr><tr><td>Focus prompt</td><td>82.75</td><td>82.45</td><td>82.14</td><td>83.08</td><td>83.81</td><td>82.80</td></tr><tr><td>Hierarchical</td><td>82.86</td><td>82.73</td><td>82.56</td><td>82.67</td><td>83.79</td><td>83.03</td></tr><tr><td>Incremental</td><td>82.49</td><td>81.94</td><td>81.82</td><td>83.11</td><td>83.39</td><td>80.83</td></tr><tr><td rowspan="4">A3CU</td><td>Standard</td><td>12.50</td><td>15.14</td><td>12.85</td><td>11.99</td><td>8.48</td><td>12.05</td></tr><tr><td>Focus prompt</td><td>12.23</td><td>15.82</td><td>12.04</td><td>11.57</td><td>8.20</td><td>11.50</td></tr><tr><td>Hierarchical</td><td>11.74</td><td>14.55</td><td>12.61</td><td>10.76</td><td>7.33</td><td>11.25</td></tr><tr><td>Incremental</td><td>11.51</td><td>12.73</td><td>13.15</td><td>9.13</td><td>8.97</td><td>9.69</td></tr><tr><td rowspan="12">Mistral-7B</td><td rowspan="4">ROUGE-2</td><td>Standard</td><td>7.16</td><td>9.28</td><td>9.56</td><td>5.86</td><td>2.19</td><td>6.41</td></tr><tr><td>Focus prompt</td><td>6.20</td><td>7.62</td><td>8.44</td><td>4.85</td><td>1.42</td><td>6.29</td></tr><tr><td>Hierarchical</td><td>10.78</td><td>11.67</td><td>13.71</td><td>13.36</td><td>4.76</td><td>7.39</td></tr><tr><td>Incremental</td><td>10.15</td><td>11.36</td><td>12.66</td><td>12.89</td><td>3.46</td><td>7.02</td></tr><tr><td rowspan="4">BERTScore</td><td>Standard</td><td>81.07</td><td>80.98</td><td>81.19</td><td>80.41</td><td>80.10</td><td>82.20</td></tr><tr><td>Focus prompt</td><td>80.72</td><td>80.31</td><td>80.81</td><td>79.91</td><td>80.12</td><td>82.13</td></tr><tr><td>Hierarchical</td><td>83.10</td><td>82.56</td><td>83.00</td><td>83.81</td><td>83.90</td><td>82.62</td></tr><tr><td>Incremental</td><td>82.25</td><td>81.92</td><td>82.39</td><td>83.67</td><td>83.14</td><td>80.56</td></tr><tr><td rowspan="4">A3CU</td><td>Standard</td><td>10.35</td><td>11.55</td><td>11.46</td><td>7.94</td><td>8.53</td><td>11.34</td></tr><tr><td>Focus prompt</td><td>9.96</td><td>10.13</td><td>10.65</td><td>7.81</td><td>7.59</td><td>12.44</td></tr><tr><td>Hierarchical</td><td>11.15</td><td>12.15</td><td>14.21</td><td>10.62</td><td>7.48</td><td>9.47</td></tr><tr><td>Incremental</td><td>10.43</td><td>14.25</td><td>11.00</td><td>8.90</td><td>7.44</td><td>9.05</td></tr><tr><td rowspan="12">GPT-3.5</td><td rowspan="4">ROUGE-2</td><td>Standard</td><td>12.33</td><td>12.99</td><td>12.78</td><td>16.16</td><td>6.05</td><td>10.53</td></tr><tr><td>Focus prompt</td><td>12.17</td><td>12.61</td><td>12.56</td><td>15.95</td><td>6.35</td><td>10.46</td></tr><tr><td>Hierarchical</td><td>8.59</td><td>9.58</td><td>10.27</td><td>10.30</td><td>4.33</td><td>6.36</td></tr><tr><td>Incremental</td><td>12.04</td><td>11.85</td><td>12.17</td><td>18.46</td><td>5.38</td><td>9.03</td></tr><tr><td rowspan="4">BERTScore</td><td>Standard</td><td>84.77</td><td>83.81</td><td>83.70</td><td>85.41</td><td>86.32</td><td>85.39</td></tr><tr><td>Focus prompt</td><td>84.80</td><td>83.76</td><td>83.81</td><td>85.50</td><td>86.28</td><td>85.39</td></tr><tr><td>Hierarchical</td><td>84.27</td><td>83.34</td><td>83.62</td><td>85.30</td><td>85.37</td><td>84.28</td></tr><tr><td>Incremental</td><td>84.43</td><td>83.48</td><td>83.74</td><td>85.41</td><td>85.76</td><td>84.44</td></tr><tr><td rowspan="4">A3CU</td><td>Standard</td><td>15.40</td><td>15.22</td><td>15.58</td><td>12.72</td><td>13.15</td><td>19.21</td></tr><tr><td>Focus prompt</td><td>15.07</td><td>14.51</td><td>15.59</td><td>12.34</td><td>13.63</td><td>18.54</td></tr><tr><td>Hierarchical</td><td>12.24</td><td>12.54</td><td>13.01</td><td>13.29</td><td>9.03</td><td>11.74</td></tr><tr><td>Incremental</td><td>12.08</td><td>13.59</td><td>13.24</td><td>10.90</td><td>9.05</td><td>12.08</td></tr></table>
+
+Table 8: Reference-based results for all models and inference methods on MiddleSum, breaking down by subset. The best number across inference methods is in bold.

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