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@@ -57,3 +57,67 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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2025/100-LongBench_ Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability_/71a796ed-673f-4c79-8d95-9a8d776a6688_origin.pdf

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2025/100-LongBench_ Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability_/full.md

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+# 100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability?
+
+Wang Yang $^{1}$ , Hongye Jin $^{2}$ , Shaochen Zhong $^{3}$ , Song Jiang $^{4}$ , Qifan Wang $^{5}$ , Vipin Chaudhary $^{1}$ , Xiaotian Han $^{1}$
+
+$^{1}$ Case Western Reserve University  $^{2}$ Texas A&M University  $^{3}$ Rice University  $^{4}$ University of California, Los Angeles  $^{5}$ Meta
+
+{wxy320,vipin,xhan}@case.edu, jhy0410@amu.edu, hz88@rice.edu  
+songjiang@ucla.edu, wqfcr@meta.com
+
+# Abstract
+
+Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM shall enable its users to effortlessly process many originally exhausting tasks — e.g., digesting a long-form document to find answers v.s., directly asking an LLM about it. However, existing real-task-based long-context evaluation benchmarks have a few major shortcomings. For instance, some Needle-in-a-Haystack-like benchmarks are too synthetic, and therefore do not represent the real world usage of LLMs. While some real-task-based benchmarks like Long-Bench avoid this problem, such benchmarks are often formed in a way where each data sample has a fixed sequence length, which not only makes them solely suitable for models with a certain range of context windows, but also lacks a proxy to know at what length the model/method-of-interest would fail. Last, most benchmarks tend to not provide proper metrics to separate long-context performance from the model's baseline ability, so when conducting a cross-model/recipe comparison, such conflation makes the user unable to understand how exactly one model or recipe excels at the long-context task in relation to its baseline ability. To address these issues, we introduce a length-controllable, real-life reflective benchmark with a novel metric that disentangles baseline knowledge from long-context capabilities. Experiments demonstrate the superiority of our datasets in effectively evaluating LLMs. All assets are available at https://github.com/uservan/100-LongBench.git.
+
+# 1 Introduction
+
+The long-context capability has become one of the fundamental competencies (Gao et al., 2024; Liu et al., 2024b; Li et al., 2024; Agarwal et al., 2024) of contemporary large language models (LLMs) because it takes the average human critical
+
+Table 1: Models' ranking on Ruler (Hsieh et al., 2024) with different metrics. Base Ability represents model's score within  $4k$  context. Old/Proposed Metric represents the average score across various lengths using traditional metric/our proposed metric.  $96.5_{(1)}$  indicates a score of 96.5 with a rank of 1. More details are in Table 5. Comparing the ranking of Old Metric and Proposed Metric reveals that the rankings of the old metrics are heavily influenced by the model's inherent abilities, which might not really reflect long-context ability.
+
+<table><tr><td>Model (size,length)</td><td>Base 
+Ability</td><td>Old 
+Metric</td><td>Proposed 
+Metric</td></tr><tr><td>Llama3.1 (70B, 128K)</td><td>96.5(1)</td><td>88.2(1)</td><td>-8.6(2)</td></tr><tr><td>Yi (34B, 200K) (Young et al., 2024)</td><td>93.3(2)</td><td>86.3(2)</td><td>-7.5(1)</td></tr><tr><td>Phi3-medium (14B, 128K)</td><td>93.3(3)</td><td>79.1(3)</td><td>-15.1(4)</td></tr><tr><td>LWM (7B, 1M) (Liu et al., 2024a)</td><td>82.3(4)</td><td>70.8(4)</td><td>-13.9(3)</td></tr></table>
+
+time and effort to digest long-form context, making a long-context-capable LLM beyond desirable. To assess the long-context capabilities of LLMs, various evaluation benchmarks and metrics have been proposed, including LongBench (Bai et al., 2023), L-Eval (An et al., 2023), NIAH (Needle in the Haystack), RULER (Hsieh et al., 2024), AdaLEval (Wang et al., 2024) and Loogle (Li et al., 2023a). However, these benchmarks often exhibit at least one of the following three shortcomings:
+
+(1) They rely on purely synthetically-constructed content that is not real-life reflective. Synthetic benchmarks such as NIAH or Passkey Retrieval often demand the retrieval of a source (e.g., a string of digits or a phrase) that bears no semantic or task relevance to the padding content (e.g., unrelated blog posts). This kind of highly artificial task does not properly reflect how LLMs are utilized in typical real-world settings, where information of similar nature is often joined together for a reader to understand and digest.
+
+(2) They adopt a fixed input length per data sample, making them suitable only for certain LLMs with compatible context windows. This is a major problem because context windows have grown significantly, thanks to the development of context extension techniques and post-training
+
+![](images/7f32438245991c359b14b835c7669d673c0edde0f61a848744c7801155b0e058.jpg)  
+Figure 1: Illustration of LM-Infinite (Han et al., 2024), a long-context enhancement method's performances on three LongBench tasks. The colored dashed lines represent the average score of each model on the corresponding task. The size of the markers corresponds to the proportion of each text length within the entire dataset. The larger the marker, the higher the proportion. The results exhibit significant variation across tasks of different lengths within the same dataset. More results of other methods are in Appendix A.1.
+
+![](images/a85f8d3840bdeb41ba122e8fbcc5c652d78b9bc8e24a83e10fe3f5538ae75165.jpg)
+
+![](images/d8074ac3aa71de5422257bf1181e83c08eba2fa2a644506ca746947a6be9c989.jpg)
+
+recipes. With Llama 3.1 (Dubey et al., 2024) claiming a context window of  $128\mathrm{k}$  (in contrast to the  $4\mathrm{k}$  context of Llama 2), many once "long-context" datasets have already become outdated. It is therefore foreseeable that many evaluations we see today will no longer be reflective as time passes. Moreover, having different lengths per individual data sample makes the evaluation reading unintuitive in several ways: E.g., for model evaluation, it is hard to tell at what length it would fail or prevail, because we only get the aggregated reading upon questions of different lengths. For method evaluation, many constant-budget compression works like StreamingLLM (Xiao et al., 2023a) and InfLLM (Xiao et al., 2024) — have an arbitrarily set constant budget that is applied to all inputs, ignoring the fact that this budget may exceed certain data samples. As a result, the reported "compressed performance" often turns into an unknown mixture of both compressed and uncompressed results, complicating the transparency of assessments.
+
+(3) They do not address the conflation between base ability and long-context capability, as these benchmarks evaluate long-context capabilities solely based on long-context tasks without isolating the influence of a model's baseline abilities. Thus, some readings can be tricky to digest when factors cannot be perfectly ablated. For instance, suppose we have two different base models, each has undergone their own continuous pretraining recipes for context extension (e.g., Llama and Qwen), which extension recipe is likely better? Applying both recipes to the same base model for direct comparison is often impractical due to compute and dataset resource limitations. Naturally, one avenue of evaluation is to measure the long context performance relative to the short context performance for an educated understanding, but such kind of measurements is largely missing in most existing long-context benchmarks.
+
+In this work, we attempt to alleviate such problems by providing a new benchmark involving a rich set of length-controllable real-life-reflective tasks —  $100$ -LongBench — and a new evaluation metric — LongScore — which leads to significant shifts in model rankings compared to traditional performance-based evaluations, as shown in Table 1. We first validate the reliability of the proposed  $100$ -LongBench and the effectiveness of LongScore. We then comprehensively benchmark various open-source models, providing fresh insights into long-context evaluation and offering a more accurate assessment that better reflects models' true abilities to handle extended contexts.
+
+# 2 Motivation: why do we need to refine long-context benchmarks?
+
+Performance variance across task lengths Evidenced by Figure 1, the performance of LM-Infinite exhibits significant variation across tasks of different lengths within the same dataset. Many longcontext datasets have uneven length distributions, introducing biases in evaluating a model's longcontext capability. To validate this hypothesis, we train models using five different long-context enhancement methods and evaluate their performances across varying lengths on the LongBench dataset. From Figure 1, we observe the following: (1) Performance Variation: All five models demonstrate performance differences across different text lengths within the same dataset. (2) Alignment with Dominant Lengths: A model's average performance aligns closely with its performance on the length range with the highest proportion of samples. For instance, on Multi-News dataset, each model's average performance is close to its performance on samples within the  $0 - 4\mathrm{k}$  length range, which represents the largest share of the dataset. These findings highlight the need for length-aware evaluations of long-context capabilities. A more robust approach
+
+![](images/37a8e96aca490640f6dd2505ea46d0f5f798cf56c99f34b6740a429b7a166387.jpg)  
+Figure 2: Comparison between LLaMA 3.1-8B-Instruct and Qwen 2.5-7B-Instruct on the Counting Star task across varying text lengths. The dashed line represents the average score across all context lengths. LLaMA 3.1-8B-Instruct performs worse than Qwen 2.5-7B-Instruct on short texts but excels on extremely long texts, indicating its superior long-context extension capability.
+
+involves testing model performance on  $N$  samples across diverse lengths to obtain a comprehensive assessment of its long-context capability. More results of other methods are in Appendix A.1.
+
+Ineffectiveness of current metrics for evaluating long-context capability Evidenced by Figure 2, existing long-context metrics primarily rely on the average performance across the benchmark. However, this approach can be misleading as it conflates the model's inherent task-specific ability with its pure long-context capability. As illustrated in Figure 2, LLaMA 3.1-8B-Instruct performs worse than Qwen 2.5-7B-Instruct on short texts but excels on extremely long texts, such as  $128k$  and  $255k$ , indicating its superior long-context extension capability. In this task, the average performance suggests that Qwen 2.5-7B-Instruct is the better model. But a closer inspection reveals that LLaMA 3.1-8B-Instruct has a distinct advantage in handling extremely long texts, despite its weaker performance on shorter inputs. This discrepancy underscores the need to separate a model's base ability (on short texts) from its long-context capability. To address this, we propose a novel metric that accurately captures a model's true capability to handle long contexts from Base Ability.
+
+# 3 How to truly evaluate Language Models' long-context capability?
+
+To address the two identified problems, we 1) construct a length-controllable long-context benchmark to reduce performance variance across task lengths, and 2) introduce LongScore, a new metric designed to accurately evaluate long-context
+
+capabilities by disentangling the model's baseline abilities. In detail, we restructure the long-context datasets, based on LongBench, L-EVAL, and other benchmarks. We then design a new pipeline to generate controllable-length long contexts by combining different articles. Additionally, we introduce a filtering mechanism in QA-related tasks to mitigate prior knowledge. Subsequently, We propose a new metric to isolate a model's long-text capability from Base Ability (performance on short texts).
+
+# 3.1 Construct a new long-context benchmark
+
+We categorize tasks into four types, each consisting of two tasks with different levels of difficulty, resulting in a total of eight tasks. The types and their corresponding tasks are: Key Retrieval (including KV Retrieval and Counting Stars), Information Retrieval (including Passage Retrieval and Passage Count), Information Comprehension (including Single-doc QA and Multi-doc QA) and Information Summarization (including Single-doc Sum and Multi-doc Sum). Table 2 provides details for each task, including: Real Context Sources(the original context of the question used in the task), Noisy Context Sources(the source of additional context that may contain irrelevant or distracting information) and Evaluation Metric(the metric used to assess model performance for each task). All of these datasets are from other benchmarks like LongBench, etc. Detailed information on context construction, question setup, and evaluation metrics, are in Appendix A.2.
+
+How to generate a controllable-length context? In LongBench, the context for each task is controllable, such as generating a context of approximately  $128k$  tokens. To achieve this, we first randomly select one article from Real Context Sources as the ground truth article. Then, we randomly sample a number of articles from Noisy Context Sources as distractor articles. These distractor articles are combined with the ground truth article to construct the whole context, ensuring that the total context length is close to but less than  $128k$ . Finally, the order of all articles is shuffled to create the context. Figure 3 illustrates the data generation process for Single-Doc QA task, showing how questions, answers, and contexts are prepared.
+
+QA Filtering Mechanism. For Multi-Doc QA and Single-Doc QA tasks, we introduce a filtering mechanism to eliminate the influence of the model's inherent prior knowledge. When evaluating a model's long-context capabilities, prior
+
+Table 2: Details of dataset construction for each task. To generate a context of a specified length like  $128k$ , we randomly select multiple articles from the Noisy Context Source datasets as distractor articles. A single article is randomly chosen from Real Context Source datasets as the ground truth article. Distractor articles and the ground truth article are combined to form the whole context, ensuring that the whole context length is less than  $128k$  and the order of all articles is shuffled. The bottom of the table contains different datasets from other benchmarks. N/A indicates that the task does not require Context Sources because the questions are synthetic rather than derived from a dataset. More details about how to construct each task are in Appendix A.2.
+
+<table><tr><td>Task Name</td><td>Real Context Sources</td><td>Noisy Context Sources</td><td>Evaluation Metric</td></tr><tr><td>KV Retrieval</td><td>N/A</td><td>1 2 3 4 5 6 7 8 9</td><td>Accuracy</td></tr><tr><td>Counting Stars</td><td>N/A</td><td>1 2 3 4 5 6 7 8 9</td><td>Accuracy</td></tr><tr><td>Passage Retrieval</td><td>9 10 11 12 13 14 15</td><td>9 10 11 12 13 14 15</td><td>Accuracy</td></tr><tr><td>Passage Count</td><td>1 2 3 4 5 6 7 8 9</td><td>N/A</td><td>Accuracy</td></tr><tr><td>Single-doc QA</td><td>1 2 3 4 5 6 7 8</td><td>1 2 3 4 5 6 7 8</td><td>LLM-based Metric</td></tr><tr><td>Multi-doc QA</td><td>16 17 18 19</td><td>1 2 3 4 5 6 7 8</td><td>LLM-based Metric</td></tr><tr><td>Single-doc Sum</td><td>1 11 12 13 14 15</td><td>1 11 12 13 14 15</td><td>LLM-based Metric</td></tr><tr><td>Multi-doc Sum</td><td>20</td><td>1 11 12 13 14 15</td><td>LLM-based Metric</td></tr></table>
+
+① qasper ② multifieldqa_en ③ narrativeqa ④ multidoc_qa ⑤ legal_contract_qa  
+$⑥$  financial_qa  $⑦$  natural_question  $⑧$  scientific_qa  $⑨$  cnn_dailymail  $⑩$  gov_report  
+$①$  qmsum  $⑫$  patent_summ  $⑬$  tv_show_summ  $⑭$  review_summ  $⑮$  meeting_summ
+
+hotpotqa 172wikimqa 18musique 19rag-mini-bioasq 20 multi_news_e
+
+![](images/d9b644db399e693d46f2330cabaca890fef8dbe7012f16fa6fe522947770ca8e.jpg)  
+Figure 3: Illustration of the Data Generation Process for the Single-Doc QA Task
+
+knowledge is often overlooked. For instance, in question-answering (QA) tasks, the model might memorize the answers to certain questions during pretraining. As shown in Figure 4, the model accurately answer questions based on its prior knowledge even without any contexts. In such cases, the model's response is not derived from the provided context but from its memorized knowledge. This oversight can lead to inflated performance metrics, misrepresenting the model's actual ability to process and comprehend long contexts. To filter out the model's prior knowledge, we introduce a QA filtering mechanism. In a no-context scenario, if the model's response score exceeds a certain threshold, it indicates that the model is relying on prior knowledge, showing the data should be excluded.
+
+Although our length-controlled datasets are synthetically constructed, they are carefully designed to better reflect real-world usage scenarios, which we called as real-life reflective. Specifically, each instance is composed by selecting a task-relevant
+
+![](images/c36289c12a67639f5fd931fa6eb255f93f9ff00e30a0b625cba72c29852aaed5.jpg)  
+Figure 4: One sample in Question Answering where models provide accurate answers regardless of context
+
+example as the source (e.g., a summarization prompt and document), and padding it with additional samples that belong to the same domain or task type (e.g., other documents suitable for summarization). This construction ensures that all components of the input are contextually aligned and task-compatible, mimicking common usage patterns in long-context settings, such as concatenated inputs in retrieval-augmented generation pipelines.
+
+# 3.2 LongScore: a new long-context metric
+
+As illustrated in Figure 2, directly using a model's scores across various text lengths to assess its long-context capability introduces inherent biases. To address this limitation, we propose a new metric that disentangles the model's base ability from its long-context capability, allowing for a more accurate and comprehensive evaluation.
+
+Base Ability. It refers to the model's score when
+
+Table 3: Comparison of long-context benchmarks: Longbench (Bai et al., 2023), L-Eval (An et al., 2023),  $\infty$ -Bench (Zhang et al., 2024), NIAH (Needle In A Haystack), RULER (Hsieh et al., 2024), Helmet (Yen et al., 2024), and our  $100$ -LongBench. L: input tokens. Controllable: The benchmark can generate contexts of specified lengths. Diverse Tasks: The tasks are varied and not limited to a single type. LLM-based Metric: Metrics in some tasks are designed based on large language models for more accurate evaluation. LC Distinction: Effectively separates the model's base ability from its long-text capability. QA Filter: Implements measures to remove the influence of the model's prior knowledge in QA tasks. The tasks in NIAH and RULER are synthetic, so they do not require LLM-based metrics or QA filtering.  
+
+<table><tr><td>Dataset</td><td>L&gt;128k</td><td>Controllable</td><td>Diverse Tasks</td><td>LLM-based Metric</td><td>LC distinction</td><td>QA Filter</td></tr><tr><td>Longbench</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td></tr><tr><td>L-EVal</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>∞-Bench</td><td>✓</td><td>X</td><td>✓</td><td>X</td><td>X</td><td>X</td></tr><tr><td>NIAH</td><td>✓</td><td>✓</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>RULER</td><td>✓</td><td>✓</td><td>✓</td><td></td><td>X</td><td></td></tr><tr><td>Helmet</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>100-LongBench</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></table>
+
+conducting short-context tasks. To estimate Base Ability, we sample  $N$  instances from short text lengths (like  $2k$ ,  $4k$ ,  $6k$ ). For each length,  $N/3$  samples are selected, and the model's average score across these lengths is computed:
+
+$$
+\text {B a s e A b i l i t y} = \frac {S _ {2 k} + S _ {4 k} + S _ {6 k}}{3} \tag {1}
+$$
+
+where  $S_{*k}$  represents the performance of model with the  $* - k$  length.
+
+$\mathrm{LongScore}(\mathrm{LC}_l)$  is our proposed metric. For longer lengths (e.g., 8k, 16k, 32k), we calculate the score on  $N$  instances for each length.  $\mathrm{LC}_l$  at a given length  $l$  is then defined as:
+
+$$
+\mathrm {L C} _ {l} = \frac {S _ {l} - \text {B a s e A b i l i t y}}{\text {B a s e A b i l i t y}} \tag {2}
+$$
+
+LongScore separates the model's Base Ability from Long-context Capability. Our metric focuses on the relative improvement or decline at longer lengths and provides a more precise assessment of long-context capabilities without being influenced by the model's Base Ability. It enables consistent and unbiased comparisons of long-context capabilities across different models and datasets.
+
+# 3.3 Compare to other benchmarks
+
+This section compares other long-context benchmarks with 100-LongBench, highlighting their similarities and differences. The overall distinctions between benchmarks are presented in Table 3.
+
+- LongBench (Bai et al., 2023) is an early benchmark used to evaluate the long-context capabilities. It was the first to use a variety of tasks for evaluation, but the context length is generally
+
+limited to around  $8k$ , and the length distribution is uneven. As many current LLMs support context lengths of 128k and beyond, these benchmarks are no longer suitable.
+
+-  $\infty$ -Bench (Zhang et al., 2024) and L-Eval (An et al., 2023) are an improvement over benchmarks like LongBench, increasing the data length to over  $128k$ . However, the context length is not controllable, which limits its ability to comprehensively evaluate LLMs.  
+- NIAH and RULER (Hsieh et al., 2024) are designed with controllable context lengths and can control the position of the answer, specifically for evaluating long-context capabilities. These benchmarks are currently the leading tools to assess the long-context capabilities of LLMs.  
+- Helmet (Yen et al., 2024) is a newly proposed benchmark that not only allows for controllable context lengths but also designs a wide variety of tasks. It introduces the use of LLM-based metrics, providing a more refined way to evaluate long-context capabilities.  
+- Long-Bench generates controllable context-length tasks. Additionally, it introduces a new metric to distinguish between a model's base ability and long-context capability, offering a more comprehensive and novel approach to evaluating long-context capabilities.
+
+# 4 Experimental Analysis
+
+In this section, we conduct comprehensive experiments to first validate the reliability of LongBench and the effectiveness of the proposed
+
+![](images/6af97086899bf18a9a48222eaaf6ab0e2c7c49c8b2e4e348df93cd65dfb7e578.jpg)  
+Figure 5: Verification of the reliability of LongBench: results of two models of different sizes from the same LM family tree, showcasing their average scores in different tasks. These findings confirm a well-established trend: within the same series, larger models generally outperform smaller ones, reinforcing the reliability of LongBench.
+
+![](images/b29fb68a76763e5c747fdbf570b74b271825f88461a8e19bd77615f78ce3c5dc.jpg)
+
+![](images/4639c59128a6a934de081ee55f1ce1c520bbf640ec1942853b7af58c63ea2f93.jpg)
+
+Table 4: Results of the average performance of five models across all tasks on  $\underline{100}$ -LongBench. Base Ability represents the model's score within lengths of  $2k$ ,  $4k$  and  $6k$ . Avg score represents the average of score across lengths including  $8k$ ,  $16k$ ,  $32k$ ,  $64k$  and  $128k$ . Avg LC represents the average of score by using our proposed metric, LongScore.  $59.1_{(1)}$  indicates that the current model has a score of 59.1 at the given context length, with a ranking of 1. Claimed Length refers to the maximum context length that the model claims to support. Qwen 2.5-14B and Qwen 2.5-7B use YaRN to extend their context length to 128k. The original context length is specified in Claimed Length.  
+
+<table><tr><td>Model</td><td>Claimed Length</td><td>Base Ability</td><td>Avg socre</td><td>Avg LC</td></tr><tr><td>Qwen2.5-14B-Instruct</td><td>32K</td><td>59.1(1)</td><td>40.7(1)</td><td>-31.1(4)</td></tr><tr><td>Qwen2.5-7B-Instruct</td><td>32K</td><td>57.4(2)</td><td>39.8(2)</td><td>-30.6(3)</td></tr><tr><td>Llama3.1-8B-Instruct</td><td>128K</td><td>44.0(3)</td><td>36.3(3)</td><td>-17.4(1)</td></tr><tr><td>Llama3.2-1B-Instruct</td><td>128K</td><td>28.7(4)</td><td>20.4(4)</td><td>-28.8(2)</td></tr></table>
+
+metric. They are then used to evaluate the longcontext capabilities of several open-source models.
+
+# 4.1 Verification of the reliability of the proposed benchmark
+
+To verify the reliability of LongBench, we evaluate three model families (Llama 3.2, Llama 3.1, and Phi 3), selecting two different model sizes from each family. Since these are models of different sizes within the same series, the expected trend in the dataset would be: for the same series, larger models generally perform better in all tasks across different context lengths. As shown in Figure 5, this overall trend is observed, indicating that the dataset generation itself is reliable and can be used for evaluating long-context capabilities. For instance, compare to Llama 3.2-1B-Instruct, Llama 3.2-3B-Instruct gets higher average scores in each task. For more detailed results of models across various context lengths, refer to Appendix A.4.
+
+![](images/d78b4e9933ea56a782c4958eb0c5e205c2140a1f68525f7e70c39e270b09c3f9.jpg)  
+Figure 6: Results of four open-source models on all tasks in  $100$ -LongBench, showing their average scores of all eight tasks at different context lengths.
+
+# 4.2 Verification of the effectiveness of the proposed metric
+
+Following the setting of Lu et al. (2024), we compare two long-context enhancement methods, NTK and PI, using LongBench and 100-LongBench. On 100-LongBench, we evaluate performances with two metrics: score and LongScore  $(LC)$ . We include three evaluations to further validate the discriminative power and practical value of our proposed LongScore metric. These comparisons were chosen to reflect real-world modeling choices and align with community intuition: (1) NTK vs. PI on long-context tasks, (2) performance of LLaMA3-8B-Instruct under different RoPE theta ratios, and (3) Gemini-1.5 model variants like Gemini-1.5-Flash and Gemini-1.5-Pro from HEMLET benchmark.
+
+There are some reasons why we choose these three comparisons: (1) NTK and PI are chosen for comparison because it is well-established that NTK provides a more fine-grained extension of PI. (2) We choose LLaMA 3-8B-Instruct (8k claimed context length) with different RoPE theta ratios. Generally speaking, appropriately increasing the RoPE
+
+Table 5: Results of 4 models' ranking in Ruler(Hsieh et al., 2024) on different metrics. Base Ability represents the model's score with a 4k-length context. Avg represents the average of scores excluding the base score.  $95.8_{(1)}$  indicates that the current model has a score of 95.8 at the given context length, with a ranking of 1. LC represents the score by our proposed metric, LongScore.  
+
+<table><tr><td rowspan="2">Models</td><td rowspan="2">Claimed Length</td><td rowspan="2">Base Ability</td><td colspan="2">8k</td><td colspan="2">16k</td><td colspan="2">32k</td><td colspan="2">64k</td><td colspan="2">128k</td><td colspan="2">Avg</td></tr><tr><td>score</td><td>LC</td><td>score</td><td>LC</td><td>score</td><td>LC</td><td>score</td><td>LC</td><td>score</td><td>LC</td><td>score</td><td>LC</td></tr><tr><td>Llama3.1 (70B)</td><td>128K</td><td>96.5(1)</td><td>95.8(1)</td><td>-0.7(2)</td><td>95.4(1)</td><td>-1.1(1)</td><td>94.8(1)</td><td>-1.7(1)</td><td>88.4(1)</td><td>-8.3(1)</td><td>66.6(2)</td><td>-30.9(3)</td><td>88.2(1)</td><td>-8.6(2)</td></tr><tr><td>Yi (34B (Young et al., 2024))</td><td>200K</td><td>93.3(2)</td><td>92.2(3)</td><td>-1.1(3)</td><td>91.3(2)</td><td>-2.1(2)</td><td>87.5(2)</td><td>-6.2(2)</td><td>83.2(2)</td><td>-10.8(2)</td><td>77.3(1)</td><td>-17.1(1)</td><td>86.3(2)</td><td>-7.5(1)</td></tr><tr><td>Phi-medium (14B)</td><td>128K</td><td>93.3(3)</td><td>93.2(2)</td><td>-0.1(1)</td><td>91.1(2)</td><td>-2.3(3)</td><td>86.8(3)</td><td>-6.9(3)</td><td>78.6(3)</td><td>-15.7(3)</td><td>46.1(4)</td><td>-50.5(4)</td><td>79.1(3)</td><td>-15.1(4)</td></tr><tr><td>LWM (7B) (Liu et al., 2024a)</td><td>1M</td><td>82.3(4)</td><td>78.4(4)</td><td>-4.70(4)</td><td>73.7(4)</td><td>-10.4(4)</td><td>69.1(4)</td><td>-16.0(4)</td><td>68.1(4)</td><td>-17.2(4)</td><td>65.0(3)</td><td>-21.0(2)</td><td>70.8(4)</td><td>-13.9(3)</td></tr></table>
+
+Table 6: Comparison of models and methods under our proposed LongScore metric. We present three evaluations to validate the discriminative power of LongScore: (1) NTK vs. PI on 100-LongBench; (2) LLaMA3-8B with different RoPE theta ratios; (3) Gemini-1.5 variants from the HEMLET benchmark. In all cases, LongScore reflects performance differences that align with common understanding (e.g., NTK > PI, Gemini-Pro > Gemini-Flash), while amplifying meaningful gaps that are not visible with raw accuracy. The results highlight the discriminative ability and effectiveness of our proposed benchmark and metric.  
+
+<table><tr><td>Benchmark</td><td>Model / Method</td><td>base</td><td>8k</td><td>16k</td><td>24k / 32k</td><td>48k / 64k</td><td>128k / 256k</td><td>avg(score)</td><td>avg(LONGSCORE)</td></tr><tr><td rowspan="2">100-LongBench</td><td>PI</td><td>19.18</td><td>16.47</td><td>17.67</td><td>17.10</td><td>17.67</td><td>0.44</td><td>13.87</td><td>-27.68</td></tr><tr><td>NTK</td><td>19.39</td><td>15.72</td><td>16.53</td><td>16.70</td><td>17.17</td><td>12.88</td><td>15.83</td><td>-18.40</td></tr><tr><td rowspan="2">100-LongBench</td><td>LLaMA3-8B (ratio=1)</td><td>35.37</td><td>37.08</td><td>1.45</td><td>1.87</td><td>0.52</td><td>0.99</td><td>7.13</td><td>-79.84</td></tr><tr><td>LLaMA3-8B (ratio=64)</td><td>32.52</td><td>31.94</td><td>25.34</td><td>26.08</td><td>26.94</td><td>1.63</td><td>18.83</td><td>-42.12</td></tr><tr><td rowspan="2">HEMLET</td><td>Gemini-1.5-Flash</td><td>59.6</td><td>-</td><td>60.2</td><td>58.1</td><td>55.0</td><td>50.7</td><td>56.00</td><td>-6.04</td></tr><tr><td>Gemini-1.5-Pro</td><td>59.5</td><td>-</td><td>60.1</td><td>59.9</td><td>57.0</td><td>54.1</td><td>57.77</td><td>-2.90</td></tr></table>
+
+theta improves the model's long context capability (within a reasonable extent). (3) we choose Gemini-1.5-Flash and Gemini-1.5-Pro because they have an obvious difference in long-context ability.
+
+On the LongBench tasks, both NTK and PI methods perform similarly, failing to provide a clear distinction. However, as shown in Table 6, on LongBench, the differences between NTK and PI became much more apparent across the selected tasks, effectively differentiating the two methods. Moreover, it is obvious that the differences of NTK and PI measured by LongScore are greater than those measured by the traditional metric, showing that LongScore demonstrates a greater ability to highlight these differences compared to the traditional metric and a more effective tool for distinguishing long-context capabilities.
+
+In other pairwise comparison, LongScore readings show a much more pronounced difference compared to the original scoring metrics of the datasets, while the win-loss order remains consistent with our general understanding of a model or method's long context capability (NTK > PI, ratio = 64 > ratio = 1, Gemini-1.5-Pro > Gemini-1.5-Flash). These results highlight the discriminative power and effectiveness of our LongScore.
+
+# 4.3 Experiments on frontier open-source LLMs
+
+This section introduces the experiments conducted using 100-LongBench and the proposed met
+
+ric, aimed at evaluating the long-context capabilities of various popular open-source large models.
+
+We select four models, due to GPU resource limitations, as they can be used to generate outputs with a  $256k$  context length. For each of the eight tasks, we generated 100 samples at each context length  $(8k, 16k, 32k, 64k, 128k)$  to obtain the scores, using the performance at  $2k$ ,  $4k$ , and  $6k$  as Base Ability. Finally, the average scores across all tasks are computed. Table 4 presents average results and the corresponding rankings. Figure 6 displays average scores at each context length.
+
+Here we explain why we choose the appropriate context lengths (e.g. 2k, 4k, 6k) for measuring Base Ability. We evaluate 8 models spanning the LLaMA 3.1, Phi-3, and Qwen 2.5 families. These models typically undergo pretraining with context lengths of 4K or 8K tokens before undergoing further continuous pretraining for long-context extension. Given this, we generalize that most models in our study have a pre-extension context window of either 4K or 8K. To probe their base reasoning ability, we evaluate performance under 2K, 4K, and 6K context lengths. These values are chosen to provide representative coverage of the model's original pretraining range without exceeding it, thereby offering a stable measure of Base Ability.
+
+Interestingly, as shown in Table 4, the rankings obtained by the traditional metric are almost identical to the rankings based on Base Ability.
+
+![](images/bb313bc075a14be6ea89c195feca44e08c3292082c845f31a6b89864880e6b85.jpg)  
+Figure 7: Results of eight open-source models on eight tasks are presented, with their scores calculated using LongScore metric. Each marker represents a single model. The darker the color of the line, the stronger the base ability of the model.
+
+![](images/c229f225f1a7503d19ddc8d65a8f95d55223e6e080251e92021130d79db44b4a.jpg)
+
+![](images/26e42a10b07791d5a1785de1cb7db4ea0bc467321cd985822ef32f43adc271a2.jpg)
+
+![](images/92f1964e4db8a407e1d006cf834a04dd1c36b64a35fac1cedb8208c580645511.jpg)
+
+![](images/0fee1c0d5968e817e22ba8f435a2de7263e6bae1fa28182928d933c697dba8dc.jpg)  
+Figure 8: Results of eight models on  $100$ -LongBench by using LongScore metric. The gray shading indicates either anomalous models' scores or cases where the model is unable to generate outputs for  $256k$ -long contexts.
+
+![](images/098aebfaa00305a0ff2e213c2ea10b732e9dadc3c7d34a2221a453be932a1e1e.jpg)
+
+![](images/27a44e1b3a1895302bf818b0fe598139d48b0894a248ff8c7920d7b622cacf66.jpg)
+
+![](images/3eae947a0a306b9d5980742558e5b048b71d4da8ee66b16693e8e140ab911fef.jpg)
+
+However, rankings using LongScore metric show a significant difference from Base Ability rankings, as demonstrated by models like Qwen 2.5-14B-Instruct and Qwen 2.5-7B-Instruct. From Figure 6, it can be observed that while these two models have higher scores at shorter context lengths (e.g. 8k, 16k), their scores drop significantly at longer context lengths (128k, 256k). This indicates that current long-text evaluation metrics are heavily influenced by Base Ability, while LongScore (the metric proposed in this paper) separates base ability from long-context capability, providing a more accurate reflection of the model's long-context performance. For comparisons of more open-source models on  $\underline{100}$ -LongBench and their long-context capability evaluation, please refer to Appendix A.5.
+
+We also present the results of eight models from four LLM family trees (Llama 3.1, Llama 3.2, Qwen 2.5 and Phi 3) on LongBench. The evaluation uses LongScore metric and the detailed results about each task are shown in Figure 7 and Figure 8.
+
+Long-context ability is important in certain specialized domains such as healthcare and law. To
+
+this end, we additionally include several domain-specific long-context tasks, including Medical-Summary, MedOdyssey (Fan et al., 2024), and CaseSumm (Heddaya et al., 2024). We re-evaluate the performance of the LLaMA 3.2-1B-Instruct model with and without these datasets. The detailed results are shown in Appendix A.6.
+
+# 4.4 Experiments on Ruler with different metrics
+
+We utilize data from Ruler (Hsieh et al., 2024), using a  $4k$ -length context to represent the model's base ability. The results are shown in Table 5, where we evaluate four models' performance at different context lengths using both LongScore and the traditional metric. Compared to LLaMA 3.1 (70B), Yi (34B) (Young et al., 2024) has a slightly lower overall score before reaching 128k context length, but at 128k, Yi (34B) performs significantly better. Similarly, compared to Phi3-medium (14B), LWM (7B) shows lower base ability and shorter text handling but clearly outperforms Phi3-medium at 128k. If ranking is based solely on scores,
+
+LLaMA 3.1 (70B) and Phi3-medium (14B) would be ranked higher than their counterparts, but this does not show their true long-context capabilities. By using LongScore, we correct this discrepancy.
+
+# 5 Related Works
+
+In this section, we review relevant prior research connected to our study. We summarize cutting-edge models known for their strong long-text processing capabilities, explore methods designed to enhance these abilities, and examine the benchmarks commonly used to assess long-text proficiency. Additionally, wwe discuss the limitations of existing benchmarks, not disentangling Base Ability from true long-context capabilities.
+
+Long-context language models. Both open-source and closed-source state-of-the-art models now support extended context lengths of up to 128K tokens or more, including GPT-4 (Achiam et al., 2023), Gemini (Team et al., 2024), Claude (Caruccio et al., 2024), LLaMA-3 (Dubey et al., 2024), and Phi-3 (Abdin et al., 2024). These models typically achieve long-context capabilities through a combination of improved pretraining and post-training techniques. For instance, many models adopt two-stage or continued pretraining pipelines, where an initial short context window (e.g., 4K or 8K) is later extended to longer lengths (e.g., 128K) using scalable attention mechanisms such as FlashAttention (Dao et al., 2022) and optimized positional encoding schemes (Li et al., 2021; Xiong et al., 2023; Hsu et al., 2024). This trend is well-documented in recent technical reports (Yang et al., 2024; Abdin et al., 2024; Dubey et al., 2024), which highlight how careful adjustments to training schedules, data distribution, and architecture design contribute to stable performance in extreme long-context settings. Nonetheless, despite these advancements, effectively evaluating and comparing the true reasoning ability of such models in long-context scenarios remains a significant challenge in the real situations and scenarios.
+
+Long context methods. Many studies have explored methods to extend the context window length of models during fine-tuning, with some approaches even achieving this without fine-tuning. Techniques such as Position interpolation (PI) (Chen et al., 2023a), NTK (Peng and Quesnelle, 2023), YaRN (Peng et al., 2023) and SelfExtend (Jin et al., 2024) manipulate RoPE (Rotary Position Embedding) (Su et al., 2024) to do length extension. Other methods, including Retrievers (Xu
+
+et al., 2023), StreamingLLM (Xiao et al., 2023b), LM-Infinite (Han et al., 2024), Longlora (Chen et al., 2023b), Inf-LLM (Xiao et al., 2024) and Landmark (Mohtashami and Jaggi, 2023), focus on designing new attention architectures or exploiting specific phenomena in attention mechanisms (Sun et al., 2024) to achieve length extension. Additionally, some works (Jiang et al., 2023; Li et al., 2023b) focus on reducing length extension to length compression via a summarization step, where long contexts are compressed or summarized before being processed by the model.
+
+Long-context benchmarks. LongBench (Bai et al., 2023) and L-Eval (An et al., 2023) are early benchmarks for evaluating long-context capabilities. Later benchmarks, such as  $\infty$ -Bench (Zhang et al., 2024), extended the context length of datasets further. Subsequently, synthetic task-related benchmarks like NIAH(Needle In A Haystack), and Ruler (Hsieh et al., 2024) emerged, focusing not only on evaluating contextual capabilities but also on examining models' sensitivity to the positional appearance of text. More recently, benchmarks such as HELMET (Yen et al., 2024) and LVEval (Yuan et al., 2024) introduced controllable context lengths and LLM-based metrics. Building on them, this work further considers prior model knowledge, and introduces a novel metric.
+
+# 6 Conclusion
+
+Our benchmark and metric address key shortcomings in current evaluation methodologies, such as the inability to isolate long-context reasoning from baseline performance and reliance on insufficiently representative tasks. By incorporating real-world data, diverse task types and difficulties, and a novel metric (LongScore), Long-Bench provides a robust platform to evaluate and compare LLMs across varying context lengths. This allows for a deeper understanding of how models handle extended contexts while minimizing the influence of prior knowledge or base abilities. As LLMs continue to evolve, the ability to rigorously assess their long-context reasoning will play a critical role in identifying bottlenecks and guiding the design of next-generation models. Our approach sets a new standard for assessing LLMs, paving the way for more robust innovations in long-context evaluation. Furthermore, it will provide an actionable insight for optimizing model architectures and training strategies to enhance long-context capabilities.
+
+# Limitations
+
+The proposed metric requires models to demonstrate relatively strong base ability on the task. If a model's base ability is insufficient, subsequent evaluations of long-context capabilities may exhibit significant fluctuations, making it less effective for comparing models' long-context performance. Besides, when constructing the benchmark, it is necessary to select articles of varying lengths to assemble into noisy contexts. For shorter target lengths, such as 2k tokens, the selected articles should also have shorter lengths — preferably less than 1k tokens — to ensure the context can be formed with two or more documents. Therefore, it is essential to collect texts of diverse lengths, particularly shorter ones, to enable effective assembly of the desired contexts.
+
+# Acknowledgements
+
+This research was partially supported by NSF Awards OAC-2117439. Further, this work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University (CWRU). We give special thanks to the CWRU HPC team for their prompt and professional help and maintenance. The views and conclusions in this paper are those of the authors and do not represent the views of any funding or supporting agencies.
+
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+
+# A Appendix
+
+# A.1 Results of models' long-text enhancement methods on Longbench
+
+These section introduces four long-context enhancement method's performances on three Long-Bench tasks. The colored dashed lines represent the average score of each model on the corresponding task. The size of the markers corresponds to the proportion of each text length within the entire dataset. The larger the marker, the higher the proportion. The results exhibit significant variation across tasks of different lengths within the same dataset. All results are in Appendix A.1.
+
+# A.2 Details about how to construct each task
+
+KV Retrieval. This task primarily evaluates the model's ability to extract critical information while ignoring irrelevant content and noisy information. (1) Context Construction: Three pairs of key-value  $(k_{1}, v_{1}; k_{2}, v_{2}; k_{3}, v_{3})$  are generated using UUIDs. The value of the previous pair serves as the key for the subsequent pair ( $v_{1} = k_{2}$ ;  $v_{2} = k_{3}$ ). These key-value pairs are randomly inserted into different noisy contexts. The noise introduces irrelevant or distracting information, simulating real-world challenges. (2) Question Setup: The question asks the model to identify the value corresponding to a specific key. (3) Evaluation Metric: The task is evaluated using accuracy (Acc). If the model correctly identifies the value associated with the queried key, its accuracy score is incremented by one.
+
+Counting Stars. Following (Song et al., 2024), this task assesses the model's ability to extract critical information across multiple documents, maintain the correct sequence when aggregating information and resist distractions from misleading or altered options. (1) Context Construction: Four noisy context passages are selected from all noisy context passages and each passage is appended with a sentence in the format: The little penguin counted  $N \star$ , where  $N$  represents a specific number of stars counted in that passage. (2) Question Setup: The model is tasked with identifying the sequence of star counts in the order of sentence appearance, like [38, 10, 90, 42]. The task provides multiple-choice options, including the correct sequence and several distractors. Distractors are generated by swapping numbers, modifying values, or changing the order to increase difficulty. (3) Evaluation Metric: The task is evaluated using accuracy (Acc). If the model
+
+selects the correct sequence, its accuracy score is incremented by one.
+
+Passage Retrieval. By focusing on comprehension and recognition, this task challenges the model's ability to extract and correlate key information in a multi-document setting. (1) Context Construction: A single data sample comprises multiple articles, each sourced from a distinct domain. These articles are concatenated to form the context. (2) Question Setup: The model is provided with the summary of one specific article from the context. The task is to identify which article in the context corresponds to the given summary. (3) Evaluation Metric: The task is evaluated using accuracy (Acc). If the model correctly identifies the article corresponding to the summary, its accuracy score is incremented by one.
+
+Passage Count. The task assesses a model's ability to understand and integrate global key information by determining the number of unique articles within a multi-article context. (1) Context Construction: Each data sample comprises multiple articles sourced from different domains. Some articles are repeated multiple times within the context to add redundancy and complexity. (2) Question Setup: The model is tasked with identifying the total number of unique (non-repeated) articles in the context. (3) Evaluation Metric: The task is evaluated using accuracy (Acc). If the model correctly identifies the count of unique articles, its accuracy score is incremented by one.
+
+Single-Doc QA. The task evaluates a model's ability to answer questions specific to a single article within a multi-article context. (1) Context Construction: Each data sample consists of multiple articles from different domains. A specific question is posed about one particular article within the context. (2) Evaluation Metric: The model's answers are assessed using another large language model (like GPT-4o-mini). Evaluation is based on two dimensions: Fluency is scored on a 3-point scale (0, 1, 2), evaluating the coherence and readability of the answer. Correctness is scored on a 4-point scale (0, 1, 2, 3), assessing the factual accuracy of the response in relation to the context. The final score is calculated as the product of the Fluency and Correctness scores: Final Score = Fluency × Correctness (3) Prior Knowledge Filtering: To filter out the model's prior knowledge, we introduce a filtering process. In a no-context scenario, if the model's response score exceeds a certain threshold, it indicates that the
+
+![](images/f99ef0c5621cf23d03875b2cb9dc985e05c3a5e5ae0951b3a46e34d02e4c6397.jpg)  
+Figure 9: Illustration of NTK's performances on three LongBench tasks.
+
+![](images/ac5d571dcfffc15f33a14d72d9e26b8ba9014c4367ed33988c6e4fc68979698d.jpg)
+
+![](images/c82b4cb133e636120d089bc53ce2e7e398e16e1d303c069a7b46181fa25aed36.jpg)
+
+![](images/1e3439e1b797a5aadba164c0b6d32092f8ef0f8eccc0a6a48dcb408559166b18.jpg)  
+Figure 10: Illustration of PI's performances on three LongBench tasks.
+
+![](images/8c6f97f3967fdc7927b146a5a3cbf0918c87f739a255cb1c4d52093665479aa3.jpg)
+
+![](images/142964e7ffa7b1b6e4574d23a6c1b0b6aa9d925fc841615005a5f3dfc33b116e.jpg)
+
+![](images/5a1e69753eadfbd2b655655bda96d79556c65bfda75b65420a010ac92e16cca0.jpg)  
+Figure 11: Illustration of YaRN's performances on three LongBench tasks.
+
+![](images/f395604b6330fa52c011242d09308e1caa1c8257832e4671e55819ddf45f97d9.jpg)
+
+![](images/8355289f9e8c0cc5fe9e7a1e08776acf9c2dccd5400d7fc098a8d031e185440f.jpg)
+
+![](images/efc844bfca90bf176802363f05d2414370e146e021e45b420845de2257a3c621.jpg)  
+Figure 12: Illustration of Longlora's performances on three LongBench tasks.
+
+![](images/feea94e022073006b88c91060790ee47ccb5bdeae684d62c8806a41ff8387a5b.jpg)
+
+![](images/e9e1832f46322c916af05dc4470d02d7e9203955f300fe252169b5a7d08bc3a8.jpg)
+
+![](images/b6c225b2a43977880a253e8054447c793b34ecf020f57703d4f6a97327e9c1ce.jpg)  
+Figure 13: Verification the reliability of LongBench: results of two models of different sizes from the same LM family tree, showcasing their scores in different tasks across various context lengths. One color represents a specific task, with solid lines indicating larger models and dashed lines representing smaller models. The results of different LMs from the same LM family tree basically validate the general trend: the larger model tends to get a higher score while the score decreases as the context length increases.
+
+![](images/0c550797a00a77b6bec9db237e659b97c2b021f1fa603ac1decfffc07b7d6e89.jpg)
+
+![](images/bb9f1459b5a92cdd1318fb825908359003db753dfa229a95cccd74ec5cd3dea4.jpg)
+
+model is relying on prior knowledge. In such cases, the data is excluded from the statistical analysis.
+
+Multi-Doc QA. The task evaluates a model's ability to integrate information from multiple articles and provide coherent, accurate answers to questions that require a global understanding of the context. (1) Context Construction: Each data sample contains multiple articles from different domains. The question posed requires the model to synthesize information across multiple articles to generate the correct answer. (2) Evaluation Metric: Similar to the Single-Doc QA task, the model's answers are evaluated using another large language model and evaluated by the same dimensions. (3) Prior Knowledge Filtering is similar to the Single-Doc QA task.
+
+Single-Doc Sum. The task evaluates a model's ability to generate concise and accurate summaries for a specific article within a multi-article context. (1) Context Construction: Each data sample consists of multiple articles from different domains. (2) Question Setup: The model is tasked with summarizing the content of one specific article from the context. (3) Evaluation Metric: The generated summary is assessed by another large language model. Two scoring dimensions are considered: Fluency evaluates the coherence and readability of the summary and is scored on a 2-point scale: 0 (poor fluency), 1 (good fluency). Precision measures the relevance of the summary by comparing each sentence in the model's output to the reference summary, and is calculated as Precision =  $\frac{\text{Number of relevant sentences}}{\text{Total number of sentences in the summary}}$ . The final score is the product of these two dimensions: Final Score = Fluency × Precision. By requiring accurate and readable summaries, this task emphasizes the model's capacity for effective information synthesis and integration.
+
+Multi-Doc Sum. The task evaluates a model's ability to integrate information from multiple articles and produce a coherent and accurate summary of their shared content. (1) Context Construction: Each data sample consists of multiple articles from different domains. (2) Question Setup: The model is tasked with summarizing the relevant content from all provided articles. (3) Evaluation Metric: Similar to the Single-Doc Sum task, the model's answers are evaluated using another large language model and evaluated by the same dimensions. By requiring effective summarization of multi-document content, this task highlights the model's ability to synthesize and generalize infor
+
+mation across diverse sources.
+
+# A.3 Prompts used in each task
+
+This section presents the prompts used in each task. Here, {context} represents the entire context constructed from articles in the noisy context sources and real context sources. {input} represents the question for the task, and {instruction} represents the model-specific instructions. For example, in Single-Doc QA, the instruction might be "Answer the question related to Passage 1", indicating that the question is specifically based on Passage 1.
+
+KV Retrieval. There are some passages below sourced from many different fields.  $\backslash \backslash n$  {context}  $\backslash \backslash n$  Given several key-value pairs in these passages, you need to find the value of the key. Read the question related with these key-value pairs and give the correct answer. {input}
+
+Counting Stars. There are some passages below sourced from many different fields.\n\nOnly output the results without any explanation. Read the following question and give the correct answer: {input} The final answer is:
+
+Passage Retrieval. Here are some passages from many different fields, along with an summarization. Please determine which passage the summarization is from.  $\backslash n\backslash n$  {context}  $\backslash n\backslash n$  The following is a summarization.  $\backslash n\backslash n$  {input}  $\backslash n\backslash n$  Please enter the number of the passage that the summarization is from. The answer format must be like "Passage 1", "Passage 2", etc.  $\backslash n\backslash n$  The answer is Passage
+
+Passage Count. There are some paragraphs below sourced from many different fields. Some of them may be duplicates. Please carefully read these paragraphs and determine how many unique paragraphs there are after removing duplicates. In other words, how many non-repeating paragraphs are there in total?  $\backslash \backslash n$  {context}  $\backslash \backslash n$  Please enter the final count of unique paragraphs after removing duplicates. The output format should only contain the number, such as 1, 2, 3, and so on.  $\backslash \backslash n$  The final answer is:
+
+Single-Doc QA. Answer the question based on the given passages. Only give me the answer and do not output any other words.  $\backslash n\backslash n$  The following are given passages and these passages are from many different fields.  $\backslash n\backslash n$  {context}  $\backslash n\backslash n$  Answer the question based on the given passages following the instruction:  $\backslash n$  {instruction}  $\backslash n$ $\backslash n$  Question: {input}  $\backslash n$  Only give me the answer and do not output any other words. Answer:  $\backslash n$
+
+Multi-Doc QA. Answer the question based on the given passages. Only give me the answer and do not output any other words.  $\backslash n\backslash n$  The following are given passages and these passages are from many different fields.  $\backslash n$  {context}  $\backslash n$ $\backslash n$  Answer the question based on the given passages following the instruction:  $\backslash n$  {instruction}  $\backslash n$ $\backslash n$  Question: {input}  $\backslash n$  Only give me the answer and do not output any other words. Answer:  $\backslash n$
+
+Single-Doc Sum. You are given several passages as follows, but not all of them need to be summarized.  $\backslash n\backslash n$  {context}  $\backslash n\backslash n$  Please follow these instructions:  $\backslash n$  1.{input}  $\backslash n$  2.Import and do not summarize any passages not listed above.  $\backslash n$  3.For the selected passages, the summary should include: the main arguments or conclusions of each article, the key evidence or supporting data presented and any unique or innovative points made in the passages.  $\backslash n$  4.The summary should be concise, focusing only on the most important information from the passages. Now, please generate the summary for the specified passage, following the instructions carefully.  $\backslash n$  Summary:  $\backslash n$
+
+Multi-Doc Sum. You are given several passages as follows, but not all of them need to be summarized.\n\nInstructions:\n1.{input} \n2.Import and do not summarize any passages not listed above.\n3.All the selected passages should be summarized into a few short sentences and do not summarize each selected passages separately. The summary should include: the main arguments or conclusions of each article, the key evidence or supporting data presented and any unique or innovative points made in the passages.\n4.The summary should be concise, focusing only on the most important information from the passages. Now, please combine and summarize the main ideas from the selected relevant passages into one cohesive summary, following the instructions carefully.\n\nSummary:  $\backslash n$
+
+# A.4 Further verification of the reliability of the proposed benchmark
+
+To further verify the reliability of the generated dataset, we evaluate three model families (Llama 3.2, Llama 3.1, and Phi 3), selecting two different model sizes from each family. Given that these models are from the same series but vary in size, the expected trends on the dataset are as follows: (1) Model Size Effect: Larger models should generally achieve higher scores compared to smaller models within the same series. (2) Text Length
+
+Table 7: Results of the average performance of five models across all tasks on  $\underline{\underline{100}}$ -LongBench. Base Ability represents the model's score within lengths of  $2k$ ,  $4k$  and  $6k$ . Avg score represents the average of score across lengths including  $8k$ ,  $16k$ ,  $32k$ ,  $64k$  and  $128k$ . Avg LC represents the average of score by using our proposed metric.  $57.4_{(1)}$  indicates that the current model has a score of 57.4 at the given context length, with a ranking of 1. Claimed Length refers to the maximum context length that the model claims to support. Qwen 2.5-14B and Qwen 2.5-7B use YaRN to extend their context length to 128k. so, the original context length is specified in Claimed Length.
+
+<table><tr><td>model</td><td>Claimed Length</td><td>Base Ability</td><td>Avg score</td><td>Avg LC</td></tr><tr><td>llama-3.1-70B-Instruct</td><td>128K</td><td>67.5(1)</td><td>52.55(1)</td><td>-22.18(2)</td></tr><tr><td>Qwen2.5-14B-Instruct</td><td>32K</td><td>59.1(2)</td><td>40.77(3)</td><td>-31.12(7)</td></tr><tr><td>Phi-3-128k-medium</td><td>128K</td><td>57.4(3)</td><td>43.28(2)</td><td>-24.65(4)</td></tr><tr><td>Qwen2.5-7B-Instruct</td><td>32K</td><td>57.4(4)</td><td>39.80(4)</td><td>-30.69(6)</td></tr><tr><td>Llama3.2-3B-Instruct</td><td>128K</td><td>51.2(8)</td><td>34.81(7)</td><td>-32.06(8)</td></tr><tr><td>Phi-3-128k-mini</td><td>128K</td><td>48.2(6)</td><td>36.78(5)</td><td>-23.85(3)</td></tr><tr><td>Llama-3.1-8B-Instruct</td><td>128K</td><td>44.0(7)</td><td>36.37(6)</td><td>-17.46(1)</td></tr><tr><td>Llama3.2-1B-Instruct</td><td>128K</td><td>28.7(8)</td><td>20.45(8)</td><td>-28.88(5)</td></tr></table>
+
+![](images/66ff1f928d7b25fc8e9a6b9d239ec6e244e3fb48b7a2bea89078440ca34ef210.jpg)
+
+![](images/29516794af79382e41ac2a52dd0c9d0929cee92c287b2124f5e4f665ea392015.jpg)  
+Figure 14: Results of eight open-source models on all tasks in 100-LongBench, showing their scores at different context lengths.
+
+Effect: As the text length increases, the performance scores should decrease across all models. As shown in Figure 13, the results basically follow these expected trends: larger models tend to score higher, and performance decreases as text length increases. This consistent pattern indicates that the dataset generation process is accurate and reliably.
+
+# A.5 Results of different Open-source models on our proposed benchmark
+
+This section first introduces the experiments conducted using 100-LongBench and the proposed metric, aimed at evaluating the long-context capabilities of various popular open-source large models.
+
+We select eight open-source models. For each of the eight tasks, we generated 100 samples at each context length (8k, 16k, 32k, 64k and 128k)
+
+Table 8: Performance of LLaMA 3.2-1B-Instruct with and without domain-specific tasks. We report scores across different context lengths and two average metrics: overall average and average on long contexts (32k+). Adding healthcare and law tasks leads to a slight drop in average long-context performance.  
+
+<table><tr><td>Benchmark</td><td>base</td><td>8k</td><td>16k</td><td>32k</td><td>64k</td><td>128k</td><td>avg(score)</td><td>avg(LongScore)</td></tr><tr><td>original</td><td>24.41</td><td>22.42</td><td>20.55</td><td>18.54</td><td>17.92</td><td>15.44</td><td>18.97</td><td>-22.27</td></tr><tr><td>original + healthcare &amp; law</td><td>24.58</td><td>21.97</td><td>18.49</td><td>15.77</td><td>16.64</td><td>12.83</td><td>17.14</td><td>-30.27</td></tr></table>
+
+to obtain the scores. The model's Long-context Capability was then calculated, using the performance at  $2k$ ,  $4k$ , and  $6k$  as the base ability. Finally, the average scores across all tasks for the five models are computed. Table 7 presents the final average results and the corresponding rankings of the five models. Figure 14 displays the average scores for all tasks at each context length for the five models.
+
+# A.6 Results of models with and without domain-specific tasks
+
+We have added long text datasets from the recommended domains (law and healthcare) to enhance the comprehensiveness of our benchmark. Evaluating the capability of LLMs to handle such domain-specific scenarios is indeed a crucial need.
+
+Specifically, we mix up CaseSumm, MedOdyssey, and Medical Summary into our original dataet. We reevaluate the performance of the LLaMA 3.2 1B-Instruct model with and without such datasets.
+
+As is shown in Table 8, incorporating healthcare and law-focused domain-specific data leads to a slight performance decline in long text scenarios, likely because the model lacks comprehensive knowledge in these specialized fields. However, the overall trend is steady. We plan to incorporate this additional evaluation to our updated manuscript and add more discussion regarding domain-specific long context evaluations.

2025/100-LongBench_ Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability_/images.zip

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2025/2M-BELEBELE_ Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF/01f08187-e16e-4ec5-be1b-7fef9667f6ed_content_list.json

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+    {
+        "type": "text",
+        "text": "2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset",
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+        "type": "text",
+        "text": "Marta R. Costa-jussà, Bokai Yu, Pierre Andrews, Belen Alastruey, Necati Cihan Camgoz Joe Chuang, Jean Maillard, Christophe Ropers, Arina Turkantenko, Carleigh Wood FAIR, Meta",
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+        "type": "text",
+        "text": "{costajussa,bokai, mortimer,alastruey,neccam,",
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+        "type": "text",
+        "text": "joechuang, jeanmm, chrisropers, arinatur, carleighwood}@meta.com",
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+    {
+        "type": "text",
+        "text": "Abstract",
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+    {
+        "type": "text",
+        "text": "We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 91 spoken languages at the intersection of BELEBELE and FLEURS, and one sign language (ASL). As a by-product we also extend the Automatic Speech Recognition Benchmark, FLEURS, by  $20\\%$ .",
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+        "text": "We evaluate 2M-BELEBELE dataset for both 5-shot and zero-shot settings and across languages, the speech comprehension accuracy is  $\\approx 10\\%$  average lower compared to reading comprehension.",
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+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
+            114,
+            478,
+            260,
+            494
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "From an AI perspective, text understanding and generation services are used globally in more than a hundred languages, but the scarcity of labeled data poses a significant challenge to developing functional systems in most languages. Although natural language processing (NLP) datasets with extensive language coverage, such as FLORES-200 (NLLBTeam, 2024), are available, they mainly concentrate on machine translation (MT). Multilingual evaluation benchmarks such as those for multilingual question answering (Lewis et al., 2020; Clark et al., 2020), natural language inference (Conneau et al., 2018), summarization (Hasan et al., 2021; Ladhak et al., 2020), and reasoning datasets (Ponti et al., 2020; Lin et al., 2021) collectively cover only about 30 languages. Furthermore, the extension of such datasets to speech or American Sign Language (ASL) is lacking, with the exception of FLEURS (Conneau et al., 2022; Tanzer, 2024), which is based on FLORES-200.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "The recent BELEBELE benchmark is the first corpus that addresses text reading comprehension for a large number of languages following a multi-way parallel approach (Bandarkar et al., 2023). The entire BELEBELE text statistics are summarized in Table 1 in Appendix A.",
+        "bbox": [
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+    {
+        "type": "image",
+        "img_path": "images/67dbaae73cd2450c2d6eabface2782ad4809e7c00fa90fdd0131e9a82d9b718c.jpg",
+        "image_caption": [
+            "Figure 1: 2M-BELEBELE entry: beyond passage, question and multiple choice answers in text from BELEBELE, we extend to ASL and 91 speech languages."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "In this work, we extend the BELEBELE dataset to speech and sign (Section 3). By doing so, we create the first highly multilingual speech and sign comprehension dataset: 2M-BELEBELE, which is composed of human speech recordings covering 91 languages and human sign recordings for ASL. This dataset will enable researchers conducting experiments on multilingual speech and ASL understanding.",
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+    },
+    {
+        "type": "text",
+        "text": "As a by-product of 2M-BELEBELE, we also extend the FLEURS dataset (which is widely used to benchmark language identification and ASR) by providing recordings for more FLORES-200 sentences than were previously available and adding sign language, creating a new 2M-FLORES. This 2M-FLORES extends FLEURS by  $20\\%$ .",
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+    {
+        "type": "text",
+        "text": "Finally, we provide a very basic set of experiments that evaluate 2M-BELEBELE and provide some reference results. We use direct and/or cascaded systems to evaluate 2M-BELEBELE dataset (Section 4). We also list several further experimentation that 2M-BELEBELE unblocks. Note that the main contribution of this paper is the creation of the first highly multilingual speech and sign comprehension dataset. The complete set of experiments",
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+    },
+    {
+        "type": "page_number",
+        "text": "10893",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: ACL 2025, pages 10893-10904",
+        "bbox": [
+            220,
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "footer",
+        "text": "July 27 - August 1, 2025 ©2025 Association for Computational Linguistics",
+        "bbox": [
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "is out of the scope of this paper (Limitations). By open-sourcing our dataset, we encourage the scientific community to pursue such experimentation.",
+        "bbox": [
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+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
+        "bbox": [
+            112,
+            143,
+            270,
+            159
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Speech Comprehension The outstanding performance of some MT and text-to-speech (TTS) models has enabled a rise in the number of works using synthetically generated training data. Furthermore, some recent works propose to also use synthetic data for evaluation; e.g., (Ustun et al., 2024; SEAM-LESSCommunicationTeam, 2025; Nguyen et al., 2024; Nachmani et al., 2023). This strategy allows researchers to extend datasets to low-resource languages and to other modalities, such as speech. However, we prove that using synthetic data for evaluation does not provide comparable conclusions as relying on human speech for the particular task of automatic speech recognition (ASR) and the FLEURS domain (Appendix C). The evaluation dataset that is closest to the speech comprehension evaluation dataset presented in this paper is the generative QA dataset proposed in (Nachmani et al., 2023). The dataset covers 300 questions in English.",
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+        "text": "ASL Comprehension Compared to spoken languages, sign languages are considered low-resource languages for natural language processing (Yin et al., 2021). Most popular datasets cover small domains discourse; e.g., weather broadcasts (Camgoz et al., 2018), which has limited real world applications. There have been previous releases of large scale open domain sign language datasets; e.g., (Albanie et al., 2021; Shi et al., 2022; Uthus et al., 2024). However, the results and challenges on such datasets suggest that computational sign language research still requires additional datasets to reach the performance of their spoken language counterparts (Müller et al., 2022, 2023). With the release of the ASL extension of the BELEBELE dataset, we aim to provide additional, high quality sign language data with gloss annotations to underpin further computational sign language research. Furthermore, due to the paragraph-level nature of the BELEBELE dataset, we enable paragraph-context sign language translation, which has been reported to improve translation performance (Sincan et al., 2023).",
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+    {
+        "type": "text",
+        "text": "3 2M-BELEBELE",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "FLEURS and BELEBELE passage alignment. Since BELEBELE uses passages constructed from",
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+        "type": "text",
+        "text": "sentences in the FLORES-200 dataset, and FLEURS (Conneau et al., 2022) is a human speech version of FLORES-200 for a subset of its languages, we create a speech version of BELEBELE by aligning its passages with the speech segments available in FLEURS. This extension can be done without extra human annotation, just by computing the alignment between FLEURS and BELEBELE passages. However, such alignment does not cover the entire BELEBELE corpus because FLEURS does not cover the entirety of FLORES-200. There are 91 languages shared between FLEURS and BELEBELE. FLEURS does not cover the same passages as BELEBELE in all those 91 languages, which means that some languages have more speech passages than others. In general, we are able to match almost  $\\approx 80\\%$  of the passages. Figure 2 shows the number of FLEURS paragraphs we can match, thus obtaining the number of paragraphs that must be recorded in order to cover all passages BELEBELE.",
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+        "type": "text",
+        "text": "Speech recordings. We commission human recordings for the part of the BELEBELE dataset that is not covered by existing FLEURS recordings, as well as for elements of BELEBELE that do not exist in FLEURS (i.e. questions and answers). Recording participants must be native speakers of the languages they record. They must have an impeccable grasp of the conventions used in their respective languages for the narration of texts. The three tasks that participants are asked to perform are: (1) Read aloud and record the text passages provided (from FLORES-200); (2) Read aloud and record the provided written questions; (3) Read aloud and record the provided written answers. For the task, we provide the participants with (a) the text of the sentences to be recorded in TSV format (the number of passages may differ from language to language), (b) the written questions (900 per language, and (c) the written answer options (3,600 per language). Additional details on the recording guidelines provided to annotators are reported in the appendix B. We verify the quality of the recordings by randomly selecting 270 recordings (30% of sample size) and ensuring that the recordings do not contain background or ambient noise and that the voices of the participants are clearly audible.",
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+        "text": "Sign recordings. To obtain ASL sign recordings, we provide translators of ASL and native signers with the English text version of the sentences to be recorded. The interpreters are then asked to",
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+        "text": "10894",
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+    {
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+        "img_path": "images/637e03ba16e7d6f02ccec028a4e76f28b4bd0c2b59140524a0171fa03239b4b1.jpg",
+        "image_caption": [
+            "Figure 2: FLEURS vs New Recordings from 2M-BELEBELE for sentences in passages."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "translate these sentences into ASL, create glosses for all sentences, and record their interpretations into ASL one sentence at a time. The glosses are subjected to an additional quality check by expert annotators to harmonize the glossing format. To harmonize the recording conditions and eliminate visual bias, the videos are recorded against plain monochrome backgrounds (e.g., white or green), and signers are requested to wear monochrome upper body clothing (e.g., black). All videos are captured in 1920x1080p resolution with all of the signing space covered in FOV. The recordings are done in 60 frames per second to address most of the motion blur that happens during signing.",
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+        "text": "2M-BELEBELE Statistics. The final dataset is composed of 91 in speech plus 1 in sign. Each of the languages' respective subsets includes 2,000 utterances organized in 488 distinct passages, 900 questions, and 4 multiple choice answers per question. For our recorded data (the red portion of Figure 2 plus questions and answers), we have one audio file or two per sentence, depending on the number of available participants (one participant only in 23 languages, and two participants in 51 languages). When two speakers are available, we request that one should represent a higher-pitch range, and the other a lower-pitch range for each passage. More details are available in Appendix A.",
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+    {
+        "type": "text",
+        "text": "In addition, the data set includes video recordings in ASL for 2,000 FLORES sentences (not including the test partition) and is similarly organized in 488 distinct passages, as well as 900 questions and 4 multiple-choice answers for each question (see summary table 1). The ASL dataset was recorded by two interpreters, but, contrary to what was possible in other languages, each interpreter could only cover one-half of the dataset each.",
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+    {
+        "type": "table",
+        "img_path": "images/5756291adcaa54abd737f8fefcc2cb5f89d7b46237e455cb76a06844561a656a.jpg",
+        "table_caption": [],
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+        "table_body": "<table><tr><td colspan=\"2\">Passages</td><td colspan=\"2\">Questions/Answers</td></tr><tr><td>Distinct Passages</td><td>488</td><td>Distinct Q</td><td>900</td></tr><tr><td>Questions per passage</td><td>1-2</td><td>Multiple-choice A</td><td>4</td></tr><tr><td>Avg words (std)</td><td>79.1 (26.2)</td><td>Avg words Q (std)</td><td>12.9 (4.0)</td></tr><tr><td>Avg sentences (std)</td><td>4.1 (1.4)</td><td>Avg words A (std)</td><td>4.2 (2.9)</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 1: Statistics for 2M-BELEBELE, which covers 91 spoken languages plus ASL. Average words are computed for English.",
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+    {
+        "type": "text",
+        "text": "4 Experiments",
+        "text_level": 1,
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+        "type": "text",
+        "text": "We evaluate 2M-BELEBELE, and compare performance across modalities. Our comparison is limited in number of systems and combination of modalities. 2M-BELEBELE offers the opportunity to check multimodal comprehension by combining speech/text/sign passages; questions and answers. In our case, we only provide results for entire text passages, questions and answers and speech passages, text questions and answers. A more comprehensive set of experiments is out of the scope of this paper, which aims at unblocking such experimentation by open-sourcing the dataset itself.",
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+        "type": "text",
+        "text": "Systems. We use the speech section of the 2M-BELEBELE dataset to evaluate the speech comprehension task with a cascaded system consisting of first speech recognition (ASR) using the WHISPER-LARGE-V3 model (Radford et al., 2022) (hereinafter, WHISPER) and SEAMLESSM4T (corresponding to SEAMLESSM4T-LARGE V2) model (SEAMLESSCommunicationTeam, 2025) feeding into LLAMA-3<sup>1</sup>. We also provide results with a unified system SPIRITLM (Nguyen et al., 2024), which is a multimodal language model that freely mixes text and speech. Since the size of this model is 7B and is based on LLAMA-2, we also add a comparison to the LLAMA-2 model. We compare these results with LLAMA-3 and LLAMA-3-CHAT",
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+    {
+        "type": "page_footnote",
+        "text": "1https://ai.meta.com/blog/meta-llama-3/",
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+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "10895",
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+    },
+    {
+        "type": "table",
+        "img_path": "images/585e943aa8dffbed4cebe413f52177e8e12e8f94402ddc5830deebd3f9f83412.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Dataset</td><td>Model</td><td>Size</td><td>Vocab</td><td>#Lang</td><td>AVG</td><td>% ≥ 50</td><td>% ≥ 70</td><td>Eng</td><td>non-Eng AVG</td></tr><tr><td colspan=\"10\">5-Shot In-Context Learning (examples in English)</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>85.4</td><td>96.6</td><td>94.9</td><td>94.8</td><td>85.2</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>77.4</td><td>88.1</td><td>72.9</td><td>94.4</td><td>77.1</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>84.9</td><td>97.4</td><td>94.9</td><td>94.8</td><td>84.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>77.1</td><td>89.7</td><td>71.8</td><td>94.4</td><td>76.6</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>81.7</td><td>94.9</td><td>92.7</td><td>93.5</td><td>81.4</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-2</td><td>7B</td><td>32K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>49.9</td><td>-</td></tr><tr><td>2M-BELEBELE</td><td>SPIRITLM</td><td>7B</td><td>37K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>25.9</td><td>-</td></tr><tr><td colspan=\"10\">Zero-Shot</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>87.5</td><td>98.3</td><td>96.6</td><td>95.8</td><td>87.3</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>79.4</td><td>93.2</td><td>78.0</td><td>95.7</td><td>79.2</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>87.0</td><td>97.4</td><td>94.9</td><td>95.8</td><td>86.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>79.1</td><td>92.3</td><td>76.9</td><td>95.7</td><td>78.7</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>84.8</td><td>94.9</td><td>94.9</td><td>95.5</td><td>84.5</td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "Table 2: Summary of accuracy results on 2M-BELEBELE compared to BELEBELE across models and evaluation settings. AVG and non-Eng AVG refers to QA accuracy; and  $\\geq 50 / 70$  refers to the proportion of languages for which a given model performs above  $50 / 70\\%$  for question and answer in text and passage in speech.",
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+    },
+    {
+        "type": "text",
+        "text": "using the BELEBELE text passage as input.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Languages For the mentioned systems, we report the results in 5-shot in-context learning and zero-shot on 59 languages at the intersection of language coverage between WHISPER and 2M-BELEBELE and 39 languages at the intersection of WHISPER, SEAMLESSM4T and 2M-BELEBELE (see Table 3 in Appendix A with the detailed list of languages per system).",
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+    },
+    {
+        "type": "text",
+        "text": "Zero-shot Evaluation. We use the same evaluation strategy as in (Bandarkar et al., 2023). SPIRITLM is not available in chat mode.",
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+    },
+    {
+        "type": "text",
+        "text": "5-shot In-Context Learning. The few-shot examples are taken randomly from the English training set and they are prompted as text format to the model. Different from (Bandarkar et al., 2023), we do not pick the answer with the highest probability but directly assess the predicted letter of the answer. For 5-shot and zero-shot settings, our instruction prompt is as follows \"Given the following passage, query, and answer choices, output the letter corresponding to the correct answer. Do not write any explanation. Only output the letter within A, B, C, or D that corresponds to the correct answer.\" and we report the averaged accuracy over 3 runs<sup>2</sup>.",
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+    {
+        "type": "text",
+        "text": "Results. Table 2 reports the summary of the results at the intersection of languages between system availability (either 59 or 39 as reported in detail in Table 3). The English drop from direct text to speech task does not vary much between 5-shot and zero-shot strategies, being slightly higher in the zero-shot setting (coherently with previous",
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+    {
+        "type": "text",
+        "text": "LLAMA-3 results that show better performance in zero-shot in other tasks $^{3}$ ). When comparing speech and text comprehension, we observe that speech decreases performance in about  $10\\%$  when comparing for 59 languages (using WHISPER for ASR). However, this decrease shortens (to about  $2 - 3\\%$  average) when comparing for 39 languages (using SEAMLESSM4T for ASR). 2M-BELEBELE accuracy results per language compared to BELEBELE are shown in Figure 3 in the 59 languages at the intersection of WHISPER and 2M-BELEBELE languages for LLAMA-3 (reading comprehension) and WHISPER + LLAMA-3 (speech comprehension).",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Differences in speech and text vary slightly depending on the languages. Low-resource languages have a greater variation between text and speech BELEBELE. The ten languages with the largest gap are: Burmese, Maltese, Assamese, Mongolian, Southern Pashto, Sindhi, Telugu, Javanese, Tajik, Georgian.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Additionally, Table 2 reports English results for SPIRITLM, a direct multimodal model. One of the reasons SPIRITLM may be performing worse is that 5-shot examples are in text, while the passage on the asked question is in speech. Best results in average for speech comprehension are achieved with the SEAMLESSM4T + LLAMA-3 cascade.",
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+    {
+        "type": "text",
+        "text": "ASL We know from previous large-scale translation attempts (Albanie et al., 2021; Müller et al., 2022) that models struggle to generalize over both individuals/appearance and large domain of discourse. Compared to speech and text models, sign",
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+        "page_idx": 3
+    },
+    {
+        "type": "page_footnote",
+        "text": "2Random seeds: 0, 1, 2.",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "3https://ai.meta.com/blog/meta-llama-3-1/ and https://ai.meta.com/blog/meta-llama-3/",
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+    {
+        "type": "page_number",
+        "text": "10896",
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+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/052ecf45a644ca967eb01b6420c8c9542c5844298696cd0ba3a8507f0f617d4c.jpg",
+        "image_caption": [
+            "Figure 3: Speech and Text BELEBELE accuracy results in 59 languages. We compare text performance with LLAMA-3-CHAT (zero-shot) and speech performance with WHISPER +LLAMA-3-CHAT (asr+zero-shot)."
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "language models suffer from having to learn generalized representations from high-dimensional inputs, i.e. videos, without overfitting to limited training dataset. Previous attempts have been made to create a more generalizable abstraction layer in the form of subunits (Camgoz et al., 2020), similar to phonemes for speech, which achieved promising results on a translation task with a small discourse domain. However, this work is yet to be applied to large discourse domain translation tasks. The best results in the FLORES domain have been achieved with close models that are not available (Zhang et al., 2024). Trying (Rust et al., 2024) as an open model did not perform above chance in the final reading comprehension dataset. However, we believe that the release of this new dataset with the additional gloss annotation will help in training models that generalize over individuals better and improve large-scale sign language translation.",
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+    {
+        "type": "text",
+        "text": "5 Conclusions",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "The 2M-BELEBELE data set<sup>4</sup> allow evaluating natural language comprehension in a large number of languages, including ASL. 2M-BELEBELE is purely human-made and covers BELEBELE passages, questions, and answers for 91 languages in the speech modality and ASL. As a by-product, 2M-FLORES extends FLEURS by  $20\\%$",
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+    },
+    {
+        "type": "text",
+        "text": "Limitations and ethical considerations",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Our speech annotations do not have the entire set completed with two annotators. Due to the high volume of the dataset, not every recording has been thoroughly verified. Some of the languages in 2M-BELEBELE are low-resource languages, which pose a challenge in sourcing professionals to record. Therefore, some of the audios were recorded in home settings and may contain minor background noise, static noise, echoes, and, occasionally, the speech could be slightly muffled or soft. All annotators are native speakers of the target language, but they may have regional accents in their speech, and their personal speech styles may be present in the audio as well. However, these are minor limitations since the mentioned imperfections should not affect intelligibility; all the recordings can be clearly understood by human standards. Regarding regional accents, from a linguistic perspective, they do not imply \"incorrectness.\" We have collected data from several speakers to ensure that the dataset reflects the diversity present in the languages.",
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+    {
+        "type": "text",
+        "text": "We can group the ASL limitations under two categories, namely visual and linguistic. For visual limitations, ASL sequences are recorded in what can be considered laboratory environments with few signer variance. This makes it harder for models trained on them to generalize to unseen environments and signers. However, it is a justified and minor limitation. Using controlled environments allows us to break down the task into two parts: translating sign language from videos and generalizing to new environments and signers. Since sign language translation is a low-resource task,",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "42M-BELEBELE dataset is freely available in Github https://github.com/facebookresearch/belebele and in HuggingFace https://huggingface.co/datasets/facebook/2M-Belebele",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "52M-FLORES is freely available in HuggingFace https: //huggingface.co/datasets/facebook/2M-Flores-ASL",
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+    {
+        "type": "page_number",
+        "text": "10897",
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+    {
+        "type": "text",
+        "text": "we prioritize improving translation from controlled videos, while acknowledging the need for future work on generalizing to new settings. For linguistic limitations, ASL sequences are collected one sentence at a time. Although this enables pairwise training and evaluation, such as classical text-based NMT, the generated sequences may not be fully realistic in terms of real-world signing. An example would be the use of placement. In sentence-per-sentence sequence generation, a signer would refer to an entity with their sign each sentence, whereas in long-form conversation, a signer would place the entity in their signing space after first reference and refer them in via use of placement in the following sentences.",
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+    {
+        "type": "text",
+        "text": "Our benchmarking is limited compared to the potential capabilities of the dataset. For example, since we have spoken questions, passages and responses, instead of just using a fix modality (spoken passages, text questions and responses), we could explore the performance when using all combinations among modalities (e.g., question in speech, answer in speech, passage in speech; or question in speech, answer in text, passage in speech; or question in speech, answer in speech and passage in text.)",
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+    },
+    {
+        "type": "text",
+        "text": "In terms of compute budget, we estimate it as 47K Nvidia A100 hours by taking into account the product of following factors: number of languages (59 / 39), number of random seeds (3), number of GPUs required by model (8), number of experiment setups (5) and estimated number of hours per experiment (10).",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Speakers and signers were paid a fair rate. Our recorded data reports self-identified gender by participant. Each of the speakers and signers signed a consent form agreeing on the dataset and its usage that they were participating in.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Acknowledgments",
+        "text_level": 1,
+        "bbox": [
+            114,
+            711,
+            278,
+            728
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "This paper is part of the LCM project and authors would like to thank the entire LCM team for the fruitful discussions. Authors want to thank Eduardo Sánchez for early discussions on the project.",
+        "bbox": [
+            112,
+            738,
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "References",
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+            847
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+        "page_idx": 5
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+        ],
+        "bbox": [
+            510,
+            85,
+            882,
+            847
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "A Languages",
+        "text_level": 1,
+        "bbox": [
+            512,
+            863,
+            640,
+            879
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Table 3 reports details on languages covered by FLEURS, TTS and ASR.",
+        "bbox": [
+            510,
+            889,
+            880,
+            919
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "10899",
+        "bbox": [
+            477,
+            928,
+            524,
+            940
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/6a7ea56c53003017b5533d8973fae52421c83e1e9a0d16bf66b41827cffded49.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Language</td><td>Code</td><td>Script</td><td>Family</td><td>FLEURS</td><td>SeamlessM4T</td><td>Whisper</td><td>2M-BELEBELE</td></tr><tr><td>Mesopotamian Arabic</td><td>acm_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Afrikaans</td><td>afr_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Tosk Albanian</td><td>als_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Amharic</td><td>amh_Ethi</td><td>Ethi</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>North Levantine Arabic</td><td>apc_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Najdi Arabic</td><td>ars_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Moroccan Arabic</td><td>ary_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Egyptian Arabic</td><td>arz_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Assamese</td><td>asm_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>North Azerbaijani</td><td>azj_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Bambara</td><td>bam_Latn</td><td>Latn</td><td>Niger-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Bengali</td><td>ben_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Bengali</td><td>ben_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td>✓(1)</td></tr><tr><td>Standard Tibetan</td><td>bod_Tibt</td><td>Tibt</td><td>Sino-Tibetan</td><td></td><td></td><td></td><td></td></tr><tr><td>Bulgarian</td><td>bul_Cyr1</td><td>Cyr1</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Catalan</td><td>cat_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Cebuano</td><td>ceb_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Czech</td><td>ces_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Central Kurdish</td><td>ckb_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Danish</td><td>dan_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>German</td><td>deu_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Greek</td><td>ell_Grek</td><td>Grek</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>English</td><td>eng_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Estonian</td><td>est_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Basque</td><td>eus_Latn</td><td>Latn</td><td>Basque</td><td></td><td></td><td></td><td></td></tr><tr><td>Finnish</td><td>fin_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>French</td><td>fra_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Fulfulde (Nigerian)</td><td>fuv_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td>✓(2)</td></tr><tr><td>Oromo (West Central)</td><td>gaz_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>(✓)</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Guarani</td><td>grn_Latn</td><td>Latn</td><td>Tupian</td><td></td><td></td><td></td><td></td></tr><tr><td>Gujarati</td><td>guj_Gujr</td><td>Gujr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Haitian Creole</td><td>hat_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Hausa</td><td>hau_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td>(✓)</td><td></td><td>✓(2)</td></tr><tr><td>Hebrew</td><td>heb_Hebr</td><td>Hebr</td><td>Afro-Asiatic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Hindi</td><td>hin_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Hindi</td><td>hin_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Croatian</td><td>hrv_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Hungarian</td><td>hun_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Armenian</td><td>hye_Armn</td><td>Armn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Igbo</td><td>ibo_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Ilocano</td><td>ilo_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Indonesian</td><td>ind_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Icelandic</td><td>isl_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Italian</td><td>ita_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Javanese</td><td>jav_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Japanese</td><td>jpn_Jpan</td><td>Jpan</td><td>Japonie</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Jingpho</td><td>kac_Latn</td><td>Latn</td><td>Sino-Tibetan</td><td></td><td></td><td></td><td></td></tr><tr><td>Kannada</td><td>kan_Knda</td><td>Knda</td><td>Dravidian</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Georgian</td><td>kat_Geor</td><td>Geor</td><td>Kartvelian</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Kazakh</td><td>kaz_Cyrl</td><td>Cyr1</td><td>Turkic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Kabuverdianu</td><td>kea_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Mongolian</td><td>khk_Cyr</td><td>Cyr</td><td>Mongolic</td><td>(✓)</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Khmer</td><td>khm_Khmr</td><td>Khmr</td><td>Austroasiatic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Kinyarwanda</td><td>kin_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Kyrgyz</td><td>kir_Cyr</td><td>Cyr</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Korean</td><td>kor_Hang</td><td>Hang</td><td>Koreanic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Lao</td><td>lao_Laoo</td><td>Laoo</td><td>Kra-Dai</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lingala</td><td>lin_Latn</td><td>Latn</td><td>Niger-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lithuanian</td><td>lit_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Ganda</td><td>lug_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Luo</td><td>luo_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Standard 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+    },
+    {
+        "type": "page_number",
+        "text": "10900",
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+        "table_caption": [],
+        "table_footnote": [],
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+    },
+    {
+        "type": "page_number",
+        "text": "10901",
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+    },
+    {
+        "type": "table",
+        "img_path": "",
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+        "table_footnote": [],
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+    },
+    {
+        "type": "text",
+        "text": "Table 3: Languages details. Column FLEURS reports the languages covered by Speech BELEBELE v1. Column ASR shows the languages reported in the experiment section, note that Hausa is covered by WHISPER-LARGE-V3 but not for SEAMLESSM4T. The number in brackets shows the number of annotations per language.",
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "B Annotation Guidelines",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Recording process. Find a quiet place free from distractions and noises, and choose a headphone that is comfortable to wear and a good quality microphone that will not distort or break your voice. Read aloud and record the scripts in a pleasant tone and at a constant and even pace, as if you were reading a formal document. Try not to speak too quickly or slowly and aim for a natural pace that is easy to follow. The audio files below provide examples of paces that are expected, too fast, or too slow, for the sentence. The hearing also marks the date for the suspect's right to a rapid trial.",
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+    {
+        "type": "text",
+        "text": "To achieve the best sound quality when recording, position the microphone close to your mouth so that the voice will sound clear and present, but not too close that it sounds muddy or you can hear a puff of air. Clearly enunciate the words and avoid mumbling. Be sure to provide a 2-second pause between sentences to add clarity and keep the overall pace down. When dealing with long, complicated sentences that contain multiple clauses or phrases, there are several approaches to ensure clarity and a natural flow as follows. Break it down: Separate the sentence into smaller parts or clauses. Practice reading aloud several times before starting the recording. This can help you get a feel for the rhythm and pacing of the sentence. Pace yourself: Try to maintain a steady, even pace. If the sentence is particularly long, it is possible to take a brief pause at a natural breakpoint to catch your breath.",
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+    {
+        "type": "text",
+        "text": "You should read the provided passages aloud without repairs (a repair is the repetition of a word that was incorrectly pronounced to correct its pronunciation).",
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+        "type": "text",
+        "text": "To achieve this, familiarize yourself beforehand with the correct pronunciation of difficult words, proper nouns, and transliterated words, as well as signs and symbols, dates and times, numbers, abbreviations, and punctuation marks. Some elements may have more than one correct pronunciation. In this case, use the one that comes the more naturally to you, as long as it is an accepted pronunciation (i.e., it is acknowledged in your language's dictionaries). Practice reading the passages aloud several times to become more comfortable with the material. Please pay particular attention to the following items:",
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+        "text": "Numbers. Number formats can vary from language to language; it is important to follow the pronunciation rules in your language. Here are some general guidelines and examples: Decimal numbers: Read the whole part of the number as a whole number and then individually read every number after the decimal point. For example, in English, the decimal number 3.14 should be read as \"three point one four.\" Different languages may have different rules, and you should follow the rules that are appropriate for your language. Cardinal numbers represent quantities or amounts. Ordinal numbers represent positions or ranks in sequential order and should be read with the appropriate suffix.",
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+    },
+    {
+        "type": "page_number",
+        "text": "10902",
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+    },
+    {
+        "type": "text",
+        "text": "For example, in English, the ordinal number 1st is read \"first\" (not \"onest\") and 5th is read \"fifth\" (not \"fiveth\"). Different languages may have different rules, and you should follow the rule that is appropriate for your language.",
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+    },
+    {
+        "type": "text",
+        "text": "Roman numerals are a collection of seven symbols that each represent a value:  $\\mathrm{I} = 1$ ,  $\\mathrm{V} = 5$ ,  $\\mathrm{X} = 10$ ,  $\\mathrm{L} = 50$ ,  $\\mathrm{C} = 100$ ,  $\\mathrm{D} = 500$ , and  $\\mathrm{M} = 1,000$ . The can be pronounced in slightly different ways depending on the context, but they are never pronounced as individual letters. For example, in English, VIII in Henry VIII is pronounced \"Henry the eighth\", while Superbowl LVIII is pronounced \"Superbowl fifty-eight\", but they are never pronounced \"Henry vi i i\" or \"Superbowl I v i i\". Different languages may have different rules, and you should follow the rules that are appropriate for your language. Punctuation marks: As a general rule, punctuation marks should not be pronounced, except quotation marks.",
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+    {
+        "type": "text",
+        "text": "For example, in English, punctuation marks such as periods, commas, colons, semicolons, question marks, and exclamation points are typically not pronounced. For example, the sentence. As a result of this, a big scandal arose. will be pronounced \"As a result of this a big scandal arose\" - not \"As a result of this comma a big scandal arose period\". However, in formal-register English (in the news, for example), a difference is made between content created by the news team and content that should be attributed to someone else by explicitly pronouncing quotation marks. For example, the news transcript The fighter said: \"I am here to try to win this.\" will be pronounced: \"The fighter said, quote, I am here to try to win this. End of quote.\" In this case, different languages may have different rules, and you should follow the rules that are appropriate for your language. Signs and symbols. Signs and symbols need to be pronounced as they would be heard in a speech-only setting. Attention should be paid: (a) to potential number or gender agreement (for example, in English, \"40%\" should be read as \"forty percent\" — not \"forty percents\") (b) to potential differences between the place of the sign or symbol in writing and in speech (for example, in English, the \"$\" sign should be read as \"dollar\" and should be read after the number it precedes; i.e. \"$22\" should be read as \"twenty-two dollars\" — not \"dollars twenty-two\") (c) to the way the sign or symbol gets expanded in speech (for example, in English, \"Platform 9 3/4\" should be read \"platform nine and three quarters\" — not \"platform nine",
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "three quarters\"). Similarly,  $50\\mathrm{km / h}$  would be pronounced \"fifty kilometers per hour\" — not \"fifty kilometers hour\"). Different languages may have different rules, and you should follow the rules that are appropriate for your language.",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Proper nouns and foreign expressions. Even the same language may have at least 2 different ways to pronounce foreign expressions of proper nouns: (a) one way is to try to approach the way they would sound in the foreign language from which they come (for example, in English, Louis in Louis XIV is pronounced \"leewee\" as it would be in French); (b) the other way is to pronounce them according to the rules of the adopting language (for example, in English, Louis in the City of St Louis is pronounced as in the English proper noun \"Lewis\")",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Abbreviations. Abbreviations should be expanded as much as possible. However, it is suggested to refrain from expanding them if their expansion results in unnatural speech. For example, in English, abbreviations such as Dr. or etc. are pronounced \"doctor\" and \"et cetera\", respectively (not \"d r\" nor \"e t c\"). However, abbreviations such as AM or PhD are pronounced as a sequence of letters without being expanded (\"a m\" and \"p h d\", respectively - not \"ante meridiem\" nor \"philosophy doctorate\"). Different languages may have different conventions, and you should follow the conventions that are appropriate for your language.",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "C Ablation study: Synthetic extension in speech evaluation datasets",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 10
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+    {
+        "type": "text",
+        "text": "In this part of our work, we aim to analyze the feasibility of synthetically extending text benchmarks to speech using TTS systems, thereby creating multimodal datasets. Our goal is to understand if it would have been feasible to obtain the speech version of BELEBELE by using state of the art TTS systems, instead of human recordings.",
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+    {
+        "type": "text",
+        "text": "For this study we use FLEURS dataset, that contains ASR data in the same domain as BELEBELE. We chose to perform this study in the ASR task because it is simpler compared to other speech tasks, due to its monotonic alignment process and minimal need for reasoning. This ensures that the overall model performance and the complexity of the task are less likely to influence the results.",
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+    {
+        "type": "text",
+        "text": "For our experiments, we generate a synthetic copy of the FLEURS dataset using the MMS TTS (Pratap et al., 2024) system on the FLEURS tran",
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+    },
+    {
+        "type": "page_number",
+        "text": "10903",
+        "bbox": [
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "scripts. Then, we benchmark state-of-the-art models (WHISPER, SEAMLESSM4T and MMS ASR) on both the original and synthetic datasets and analyze whether the conclusions remain consistent across both datasets.",
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "It is important to note that a decrease in system performance is expected when using synthetic data. However, if this decrease occurs proportionally across all models, the synthetic data could still be useful to benchmark models. Conversely, if the model performance ranking changes, we can conclude that synthetic data is not reliable when benchmarking models.",
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+    },
+    {
+        "type": "text",
+        "text": "To measure the variability in model rankings between the original and the synthetic data, we track the inversions that occur in the order of the models in the two settings. We define an inversion as a swap between two models that appear in adjacent positions on the list. We count how many swaps are needed in the ranking obtained using synthetic data to match the ranking from the original dataset.",
+        "bbox": [
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+        "page_idx": 11
+    },
+    {
+        "type": "table",
+        "img_path": "images/396031df020c32d50fbb8d92605944f995cd4c1966ee3f1af02d3de43e739640.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\"></td><td colspan=\"2\">SEAMLESSM4T</td><td colspan=\"2\">WHISPER</td><td colspan=\"2\">MMS</td><td rowspan=\"2\">Inv</td></tr><tr><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td></tr><tr><td>Bengali</td><td>14.1</td><td>21.1</td><td>114.7</td><td>105.8</td><td>14.6</td><td>25.0</td><td></td></tr><tr><td>Catalan</td><td>8.2</td><td>13.2</td><td>6.7</td><td>16.4</td><td>10.3</td><td>21.8</td><td>✓</td></tr><tr><td>Dutch</td><td>9.9</td><td>20.0</td><td>8.5</td><td>19.7</td><td>12.4</td><td>28.3</td><td></td></tr><tr><td>English</td><td>6.0</td><td>11.7</td><td>4.5</td><td>9.8</td><td>12.3</td><td>19.2</td><td></td></tr><tr><td>Finnish</td><td>20.1</td><td>20.8</td><td>12.5</td><td>18.9</td><td>13.1</td><td>18.4</td><td>✓</td></tr><tr><td>French</td><td>9.5</td><td>10.8</td><td>6.7</td><td>11.3</td><td>12.4</td><td>16.6</td><td>✓</td></tr><tr><td>German</td><td>8.5</td><td>13.9</td><td>5.2</td><td>12.3</td><td>10.5</td><td>20.8</td><td></td></tr><tr><td>Hindi</td><td>11.9</td><td>13.4</td><td>33.5</td><td>28.7</td><td>11.1</td><td>18.3</td><td>✓</td></tr><tr><td>Indonesian</td><td>12.1</td><td>12.8</td><td>8.7</td><td>14.2</td><td>13.2</td><td>21.9</td><td>✓</td></tr><tr><td>Korean</td><td>25.7</td><td>40.3</td><td>15.4</td><td>29.9</td><td>47.8</td><td>61.2</td><td></td></tr><tr><td>Polish</td><td>13.0</td><td>14.7</td><td>8.1</td><td>13.3</td><td>11.6</td><td>18.1</td><td>✓</td></tr><tr><td>Portuguese</td><td>9.0</td><td>8.0</td><td>4.1</td><td>6.9</td><td>8.7</td><td>10.4</td><td>✓</td></tr><tr><td>Romanian</td><td>12.6</td><td>11.7</td><td>13.5</td><td>25.4</td><td>12.0</td><td>15.4</td><td>✓</td></tr><tr><td>Russian</td><td>10.2</td><td>18.6</td><td>5.6</td><td>17.4</td><td>18.8</td><td>34.3</td><td></td></tr><tr><td>Spanish</td><td>6.3</td><td>9.1</td><td>3.4</td><td>10.0</td><td>6.4</td><td>10.8</td><td>✓</td></tr><tr><td>Swahili</td><td>19.5</td><td>19.0</td><td>64.2</td><td>58.4</td><td>14.2</td><td>19.0</td><td>✓</td></tr><tr><td>Swedish</td><td>15.4</td><td>20.1</td><td>11.3</td><td>19.1</td><td>21.0</td><td>27.8</td><td></td></tr><tr><td>Telugu</td><td>27.4</td><td>28.0</td><td>132.2</td><td>133.9</td><td>24.2</td><td>27.8</td><td></td></tr><tr><td>Thai</td><td>127.8</td><td>135.5</td><td>104.0</td><td>121.3</td><td>99.8</td><td>99.9</td><td></td></tr><tr><td>Turkish</td><td>18.6</td><td>23.0</td><td>8.4</td><td>16.5</td><td>19.2</td><td>30.3</td><td></td></tr><tr><td>Ukrainian</td><td>15.0</td><td>23.5</td><td>9.8</td><td>21.8</td><td>18.1</td><td>34.7</td><td></td></tr><tr><td>Vietnamese</td><td>16.0</td><td>20.1</td><td>10.2</td><td>14.2</td><td>25.8</td><td>25.3</td><td></td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "Table 4:  $\\mathrm{{WER}}\\left(  \\downarrow  \\right)$  results on the ASR task. Last column marks if the language has at least 1 inversion in ASR performance ranking comparing human vs TTS inputs.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "In Table 4 we see that in the ASR setting, conclusions regarding model performance can vary depending on whether human or synthetic data is used. Although these conclusions are specific to the evaluated tasks and datasets, we demonstrate that even with the outstanding performance of current TTS methods, this does not guarantee the reliability of the data they generate when it comes to evaluation purposes. This is true not only for low-resource languages, but also for high-resource languages such as French or Spanish. These findings show that speech benchmarks might not be reliable if synthetically generated even in widely researched areas, further supporting the creation of evaluation datasets by humans.",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "Note that we perform the study on the FLEURS languages that are covered by all MMS, WHISPER and SEAMLESSM4T.",
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+    },
+    {
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+        "text": "10904",
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+        "page_idx": 11
+    }
+]

2025/2M-BELEBELE_ Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF/01f08187-e16e-4ec5-be1b-7fef9667f6ed_model.json

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+    [
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset"
+        },
+        {
+            "type": "text",
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+            "content": "Marta R. Costa-jussà, Bokai Yu, Pierre Andrews, Belen Alastruey, Necati Cihan Camgoz Joe Chuang, Jean Maillard, Christophe Ropers, Arina Turkantenko, Carleigh Wood FAIR, Meta"
+        },
+        {
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+            "content": "{costajussa,bokai, mortimer,alastruey,neccam,"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "joechuang, jeanmm, chrisropers, arinatur, carleighwood}@meta.com"
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "Abstract"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.142,
+                0.283,
+                0.461,
+                0.397
+            ],
+            "angle": 0,
+            "content": "We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 91 spoken languages at the intersection of BELEBELE and FLEURS, and one sign language (ASL). As a by-product we also extend the Automatic Speech Recognition Benchmark, FLEURS, by \\(20\\%\\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "We evaluate 2M-BELEBELE dataset for both 5-shot and zero-shot settings and across languages, the speech comprehension accuracy is \\(\\approx 10\\%\\) average lower compared to reading comprehension."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+                0.262,
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+            ],
+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.504,
+                0.49,
+                0.824
+            ],
+            "angle": 0,
+            "content": "From an AI perspective, text understanding and generation services are used globally in more than a hundred languages, but the scarcity of labeled data poses a significant challenge to developing functional systems in most languages. Although natural language processing (NLP) datasets with extensive language coverage, such as FLORES-200 (NLLBTeam, 2024), are available, they mainly concentrate on machine translation (MT). Multilingual evaluation benchmarks such as those for multilingual question answering (Lewis et al., 2020; Clark et al., 2020), natural language inference (Conneau et al., 2018), summarization (Hasan et al., 2021; Ladhak et al., 2020), and reasoning datasets (Ponti et al., 2020; Lin et al., 2021) collectively cover only about 30 languages. Furthermore, the extension of such datasets to speech or American Sign Language (ASL) is lacking, with the exception of FLEURS (Conneau et al., 2022; Tanzer, 2024), which is based on FLORES-200."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "The recent BELEBELE benchmark is the first corpus that addresses text reading comprehension for a large number of languages following a multi-way parallel approach (Bandarkar et al., 2023). The entire BELEBELE text statistics are summarized in Table 1 in Appendix A."
+        },
+        {
+            "type": "image",
+            "bbox": [
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+        },
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+            "bbox": [
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+            "angle": 0,
+            "content": "Figure 1: 2M-BELEBELE entry: beyond passage, question and multiple choice answers in text from BELEBELE, we extend to ASL and 91 speech languages."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "In this work, we extend the BELEBELE dataset to speech and sign (Section 3). By doing so, we create the first highly multilingual speech and sign comprehension dataset: 2M-BELEBELE, which is composed of human speech recordings covering 91 languages and human sign recordings for ASL. This dataset will enable researchers conducting experiments on multilingual speech and ASL understanding."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "As a by-product of 2M-BELEBELE, we also extend the FLEURS dataset (which is widely used to benchmark language identification and ASR) by providing recordings for more FLORES-200 sentences than were previously available and adding sign language, creating a new 2M-FLORES. This 2M-FLORES extends FLEURS by \\(20\\%\\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "Finally, we provide a very basic set of experiments that evaluate 2M-BELEBELE and provide some reference results. We use direct and/or cascaded systems to evaluate 2M-BELEBELE dataset (Section 4). We also list several further experimentation that 2M-BELEBELE unblocks. Note that the main contribution of this paper is the creation of the first highly multilingual speech and sign comprehension dataset. The complete set of experiments"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+            "angle": 0,
+            "content": "10893"
+        },
+        {
+            "type": "footer",
+            "bbox": [
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+            "angle": 0,
+            "content": "Findings of the Association for Computational Linguistics: ACL 2025, pages 10893-10904"
+        },
+        {
+            "type": "footer",
+            "bbox": [
+                0.269,
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+            "angle": 0,
+            "content": "July 27 - August 1, 2025 ©2025 Association for Computational Linguistics"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.49,
+                0.135
+            ],
+            "angle": 0,
+            "content": "is out of the scope of this paper (Limitations). By open-sourcing our dataset, we encourage the scientific community to pursue such experimentation."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
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+            ],
+            "angle": 0,
+            "content": "2 Related Work"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.169,
+                0.493,
+                0.476
+            ],
+            "angle": 0,
+            "content": "Speech Comprehension The outstanding performance of some MT and text-to-speech (TTS) models has enabled a rise in the number of works using synthetically generated training data. Furthermore, some recent works propose to also use synthetic data for evaluation; e.g., (Ustun et al., 2024; SEAM-LESSCommunicationTeam, 2025; Nguyen et al., 2024; Nachmani et al., 2023). This strategy allows researchers to extend datasets to low-resource languages and to other modalities, such as speech. However, we prove that using synthetic data for evaluation does not provide comparable conclusions as relying on human speech for the particular task of automatic speech recognition (ASR) and the FLEURS domain (Appendix C). The evaluation dataset that is closest to the speech comprehension evaluation dataset presented in this paper is the generative QA dataset proposed in (Nachmani et al., 2023). The dataset covers 300 questions in English."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.483,
+                0.492,
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+            ],
+            "angle": 0,
+            "content": "ASL Comprehension Compared to spoken languages, sign languages are considered low-resource languages for natural language processing (Yin et al., 2021). Most popular datasets cover small domains discourse; e.g., weather broadcasts (Camgoz et al., 2018), which has limited real world applications. There have been previous releases of large scale open domain sign language datasets; e.g., (Albanie et al., 2021; Shi et al., 2022; Uthus et al., 2024). However, the results and challenges on such datasets suggest that computational sign language research still requires additional datasets to reach the performance of their spoken language counterparts (Müller et al., 2022, 2023). With the release of the ASL extension of the BELEBELE dataset, we aim to provide additional, high quality sign language data with gloss annotations to underpin further computational sign language research. Furthermore, due to the paragraph-level nature of the BELEBELE dataset, we enable paragraph-context sign language translation, which has been reported to improve translation performance (Sincan et al., 2023)."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "3 2M-BELEBELE"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.89,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "FLEURS and BELEBELE passage alignment. Since BELEBELE uses passages constructed from"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.887,
+                0.423
+            ],
+            "angle": 0,
+            "content": "sentences in the FLORES-200 dataset, and FLEURS (Conneau et al., 2022) is a human speech version of FLORES-200 for a subset of its languages, we create a speech version of BELEBELE by aligning its passages with the speech segments available in FLEURS. This extension can be done without extra human annotation, just by computing the alignment between FLEURS and BELEBELE passages. However, such alignment does not cover the entire BELEBELE corpus because FLEURS does not cover the entirety of FLORES-200. There are 91 languages shared between FLEURS and BELEBELE. FLEURS does not cover the same passages as BELEBELE in all those 91 languages, which means that some languages have more speech passages than others. In general, we are able to match almost \\(\\approx 80\\%\\) of the passages. Figure 2 shows the number of FLEURS paragraphs we can match, thus obtaining the number of paragraphs that must be recorded in order to cover all passages BELEBELE."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            ],
+            "angle": 0,
+            "content": "Speech recordings. We commission human recordings for the part of the BELEBELE dataset that is not covered by existing FLEURS recordings, as well as for elements of BELEBELE that do not exist in FLEURS (i.e. questions and answers). Recording participants must be native speakers of the languages they record. They must have an impeccable grasp of the conventions used in their respective languages for the narration of texts. The three tasks that participants are asked to perform are: (1) Read aloud and record the text passages provided (from FLORES-200); (2) Read aloud and record the provided written questions; (3) Read aloud and record the provided written answers. For the task, we provide the participants with (a) the text of the sentences to be recorded in TSV format (the number of passages may differ from language to language), (b) the written questions (900 per language, and (c) the written answer options (3,600 per language). Additional details on the recording guidelines provided to annotators are reported in the appendix B. We verify the quality of the recordings by randomly selecting 270 recordings (30% of sample size) and ensuring that the recordings do not contain background or ambient noise and that the voices of the participants are clearly audible."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.858,
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+            "angle": 0,
+            "content": "Sign recordings. To obtain ASL sign recordings, we provide translators of ASL and native signers with the English text version of the sentences to be recorded. The interpreters are then asked to"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10894"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.15,
+                0.085,
+                0.852,
+                0.234
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.196,
+                0.246,
+                0.794,
+                0.262
+            ],
+            "angle": 0,
+            "content": "Figure 2: FLEURS vs New Recordings from 2M-BELEBELE for sentences in passages."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.287,
+                0.49,
+                0.513
+            ],
+            "angle": 0,
+            "content": "translate these sentences into ASL, create glosses for all sentences, and record their interpretations into ASL one sentence at a time. The glosses are subjected to an additional quality check by expert annotators to harmonize the glossing format. To harmonize the recording conditions and eliminate visual bias, the videos are recorded against plain monochrome backgrounds (e.g., white or green), and signers are requested to wear monochrome upper body clothing (e.g., black). All videos are captured in 1920x1080p resolution with all of the signing space covered in FOV. The recordings are done in 60 frames per second to address most of the motion blur that happens during signing."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.545,
+                0.49,
+                0.771
+            ],
+            "angle": 0,
+            "content": "2M-BELEBELE Statistics. The final dataset is composed of 91 in speech plus 1 in sign. Each of the languages' respective subsets includes 2,000 utterances organized in 488 distinct passages, 900 questions, and 4 multiple choice answers per question. For our recorded data (the red portion of Figure 2 plus questions and answers), we have one audio file or two per sentence, depending on the number of available participants (one participant only in 23 languages, and two participants in 51 languages). When two speakers are available, we request that one should represent a higher-pitch range, and the other a lower-pitch range for each passage. More details are available in Appendix A."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.777,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "In addition, the data set includes video recordings in ASL for 2,000 FLORES sentences (not including the test partition) and is similarly organized in 488 distinct passages, as well as 900 questions and 4 multiple-choice answers for each question (see summary table 1). The ASL dataset was recorded by two interpreters, but, contrary to what was possible in other languages, each interpreter could only cover one-half of the dataset each."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.51,
+                0.284,
+                0.887,
+                0.35
+            ],
+            "angle": 0,
+            "content": "<table><tr><td colspan=\"2\">Passages</td><td colspan=\"2\">Questions/Answers</td></tr><tr><td>Distinct Passages</td><td>488</td><td>Distinct Q</td><td>900</td></tr><tr><td>Questions per passage</td><td>1-2</td><td>Multiple-choice A</td><td>4</td></tr><tr><td>Avg words (std)</td><td>79.1 (26.2)</td><td>Avg words Q (std)</td><td>12.9 (4.0)</td></tr><tr><td>Avg sentences (std)</td><td>4.1 (1.4)</td><td>Avg words A (std)</td><td>4.2 (2.9)</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.508,
+                0.358,
+                0.885,
+                0.402
+            ],
+            "angle": 0,
+            "content": "Table 1: Statistics for 2M-BELEBELE, which covers 91 spoken languages plus ASL. Average words are computed for English."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.427,
+                0.657,
+                0.444
+            ],
+            "angle": 0,
+            "content": "4 Experiments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.454,
+                0.885,
+                0.648
+            ],
+            "angle": 0,
+            "content": "We evaluate 2M-BELEBELE, and compare performance across modalities. Our comparison is limited in number of systems and combination of modalities. 2M-BELEBELE offers the opportunity to check multimodal comprehension by combining speech/text/sign passages; questions and answers. In our case, we only provide results for entire text passages, questions and answers and speech passages, text questions and answers. A more comprehensive set of experiments is out of the scope of this paper, which aims at unblocking such experimentation by open-sourcing the dataset itself."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.658,
+                0.886,
+                0.899
+            ],
+            "angle": 0,
+            "content": "Systems. We use the speech section of the 2M-BELEBELE dataset to evaluate the speech comprehension task with a cascaded system consisting of first speech recognition (ASR) using the WHISPER-LARGE-V3 model (Radford et al., 2022) (hereinafter, WHISPER) and SEAMLESSM4T (corresponding to SEAMLESSM4T-LARGE V2) model (SEAMLESSCommunicationTeam, 2025) feeding into LLAMA-3<sup>1</sup>. We also provide results with a unified system SPIRITLM (Nguyen et al., 2024), which is a multimodal language model that freely mixes text and speech. Since the size of this model is 7B and is based on LLAMA-2, we also add a comparison to the LLAMA-2 model. We compare these results with LLAMA-3 and LLAMA-3-CHAT"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.532,
+                0.907,
+                0.831,
+                0.92
+            ],
+            "angle": 0,
+            "content": "1https://ai.meta.com/blog/meta-llama-3/"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10895"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.134,
+                0.082,
+                0.864,
+                0.273
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Dataset</td><td>Model</td><td>Size</td><td>Vocab</td><td>#Lang</td><td>AVG</td><td>% ≥ 50</td><td>% ≥ 70</td><td>Eng</td><td>non-Eng AVG</td></tr><tr><td colspan=\"10\">5-Shot In-Context Learning (examples in English)</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>85.4</td><td>96.6</td><td>94.9</td><td>94.8</td><td>85.2</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>77.4</td><td>88.1</td><td>72.9</td><td>94.4</td><td>77.1</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>84.9</td><td>97.4</td><td>94.9</td><td>94.8</td><td>84.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>77.1</td><td>89.7</td><td>71.8</td><td>94.4</td><td>76.6</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>81.7</td><td>94.9</td><td>92.7</td><td>93.5</td><td>81.4</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-2</td><td>7B</td><td>32K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>49.9</td><td>-</td></tr><tr><td>2M-BELEBELE</td><td>SPIRITLM</td><td>7B</td><td>37K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>25.9</td><td>-</td></tr><tr><td colspan=\"10\">Zero-Shot</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>87.5</td><td>98.3</td><td>96.6</td><td>95.8</td><td>87.3</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>79.4</td><td>93.2</td><td>78.0</td><td>95.7</td><td>79.2</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>87.0</td><td>97.4</td><td>94.9</td><td>95.8</td><td>86.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>79.1</td><td>92.3</td><td>76.9</td><td>95.7</td><td>78.7</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>84.8</td><td>94.9</td><td>94.9</td><td>95.5</td><td>84.5</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.284,
+                0.883,
+                0.327
+            ],
+            "angle": 0,
+            "content": "Table 2: Summary of accuracy results on 2M-BELEBELE compared to BELEBELE across models and evaluation settings. AVG and non-Eng AVG refers to QA accuracy; and \\(\\geq 50 / 70\\) refers to the proportion of languages for which a given model performs above \\(50 / 70\\%\\) for question and answer in text and passage in speech."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.353,
+                0.435,
+                0.369
+            ],
+            "angle": 0,
+            "content": "using the BELEBELE text passage as input."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.377,
+                0.489,
+                0.506
+            ],
+            "angle": 0,
+            "content": "Languages For the mentioned systems, we report the results in 5-shot in-context learning and zero-shot on 59 languages at the intersection of language coverage between WHISPER and 2M-BELEBELE and 39 languages at the intersection of WHISPER, SEAMLESSM4T and 2M-BELEBELE (see Table 3 in Appendix A with the detailed list of languages per system)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.513,
+                0.49,
+                0.56
+            ],
+            "angle": 0,
+            "content": "Zero-shot Evaluation. We use the same evaluation strategy as in (Bandarkar et al., 2023). SPIRITLM is not available in chat mode."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.57,
+                0.49,
+                0.78
+            ],
+            "angle": 0,
+            "content": "5-shot In-Context Learning. The few-shot examples are taken randomly from the English training set and they are prompted as text format to the model. Different from (Bandarkar et al., 2023), we do not pick the answer with the highest probability but directly assess the predicted letter of the answer. For 5-shot and zero-shot settings, our instruction prompt is as follows \"Given the following passage, query, and answer choices, output the letter corresponding to the correct answer. Do not write any explanation. Only output the letter within A, B, C, or D that corresponds to the correct answer.\" and we report the averaged accuracy over 3 runs<sup>2</sup>."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.787,
+                0.49,
+                0.9
+            ],
+            "angle": 0,
+            "content": "Results. Table 2 reports the summary of the results at the intersection of languages between system availability (either 59 or 39 as reported in detail in Table 3). The English drop from direct text to speech task does not vary much between 5-shot and zero-shot strategies, being slightly higher in the zero-shot setting (coherently with previous"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.352,
+                0.885,
+                0.561
+            ],
+            "angle": 0,
+            "content": "LLAMA-3 results that show better performance in zero-shot in other tasks\\(^{3}\\)). When comparing speech and text comprehension, we observe that speech decreases performance in about \\(10\\%\\) when comparing for 59 languages (using WHISPER for ASR). However, this decrease shortens (to about \\(2 - 3\\%\\) average) when comparing for 39 languages (using SEAMLESSM4T for ASR). 2M-BELEBELE accuracy results per language compared to BELEBELE are shown in Figure 3 in the 59 languages at the intersection of WHISPER and 2M-BELEBELE languages for LLAMA-3 (reading comprehension) and WHISPER + LLAMA-3 (speech comprehension)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.562,
+                0.885,
+                0.674
+            ],
+            "angle": 0,
+            "content": "Differences in speech and text vary slightly depending on the languages. Low-resource languages have a greater variation between text and speech BELEBELE. The ten languages with the largest gap are: Burmese, Maltese, Assamese, Mongolian, Southern Pashto, Sindhi, Telugu, Javanese, Tajik, Georgian."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.676,
+                0.884,
+                0.789
+            ],
+            "angle": 0,
+            "content": "Additionally, Table 2 reports English results for SPIRITLM, a direct multimodal model. One of the reasons SPIRITLM may be performing worse is that 5-shot examples are in text, while the passage on the asked question is in speech. Best results in average for speech comprehension are achieved with the SEAMLESSM4T + LLAMA-3 cascade."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.802,
+                0.885,
+                0.883
+            ],
+            "angle": 0,
+            "content": "ASL We know from previous large-scale translation attempts (Albanie et al., 2021; Müller et al., 2022) that models struggle to generalize over both individuals/appearance and large domain of discourse. Compared to speech and text models, sign"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.136,
+                0.907,
+                0.285,
+                0.92
+            ],
+            "angle": 0,
+            "content": "2Random seeds: 0, 1, 2."
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.509,
+                0.895,
+                0.883,
+                0.92
+            ],
+            "angle": 0,
+            "content": "3https://ai.meta.com/blog/meta-llama-3-1/ and https://ai.meta.com/blog/meta-llama-3/"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10896"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.172,
+                0.085,
+                0.831,
+                0.288
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.301,
+                0.884,
+                0.331
+            ],
+            "angle": 0,
+            "content": "Figure 3: Speech and Text BELEBELE accuracy results in 59 languages. We compare text performance with LLAMA-3-CHAT (zero-shot) and speech performance with WHISPER +LLAMA-3-CHAT (asr+zero-shot)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.356,
+                0.49,
+                0.661
+            ],
+            "angle": 0,
+            "content": "language models suffer from having to learn generalized representations from high-dimensional inputs, i.e. videos, without overfitting to limited training dataset. Previous attempts have been made to create a more generalizable abstraction layer in the form of subunits (Camgoz et al., 2020), similar to phonemes for speech, which achieved promising results on a translation task with a small discourse domain. However, this work is yet to be applied to large discourse domain translation tasks. The best results in the FLORES domain have been achieved with close models that are not available (Zhang et al., 2024). Trying (Rust et al., 2024) as an open model did not perform above chance in the final reading comprehension dataset. However, we believe that the release of this new dataset with the additional gloss annotation will help in training models that generalize over individuals better and improve large-scale sign language translation."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.682,
+                0.254,
+                0.699
+            ],
+            "angle": 0,
+            "content": "5 Conclusions"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.715,
+                0.49,
+                0.828
+            ],
+            "angle": 0,
+            "content": "The 2M-BELEBELE data set<sup>4</sup> allow evaluating natural language comprehension in a large number of languages, including ASL. 2M-BELEBELE is purely human-made and covers BELEBELE passages, questions, and answers for 91 languages in the speech modality and ASL. As a by-product, 2M-FLORES extends FLEURS by \\(20\\%\\)"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.355,
+                0.844,
+                0.371
+            ],
+            "angle": 0,
+            "content": "Limitations and ethical considerations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.388,
+                0.885,
+                0.726
+            ],
+            "angle": 0,
+            "content": "Our speech annotations do not have the entire set completed with two annotators. Due to the high volume of the dataset, not every recording has been thoroughly verified. Some of the languages in 2M-BELEBELE are low-resource languages, which pose a challenge in sourcing professionals to record. Therefore, some of the audios were recorded in home settings and may contain minor background noise, static noise, echoes, and, occasionally, the speech could be slightly muffled or soft. All annotators are native speakers of the target language, but they may have regional accents in their speech, and their personal speech styles may be present in the audio as well. However, these are minor limitations since the mentioned imperfections should not affect intelligibility; all the recordings can be clearly understood by human standards. Regarding regional accents, from a linguistic perspective, they do not imply \"incorrectness.\" We have collected data from several speakers to ensure that the dataset reflects the diversity present in the languages."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.729,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "We can group the ASL limitations under two categories, namely visual and linguistic. For visual limitations, ASL sequences are recorded in what can be considered laboratory environments with few signer variance. This makes it harder for models trained on them to generalize to unseen environments and signers. However, it is a justified and minor limitation. Using controlled environments allows us to break down the task into two parts: translating sign language from videos and generalizing to new environments and signers. Since sign language translation is a low-resource task,"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.113,
+                0.847,
+                0.488,
+                0.894
+            ],
+            "angle": 0,
+            "content": "42M-BELEBELE dataset is freely available in Github https://github.com/facebookresearch/belebele and in HuggingFace https://huggingface.co/datasets/facebook/2M-Belebele"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.114,
+                0.895,
+                0.488,
+                0.92
+            ],
+            "angle": 0,
+            "content": "52M-FLORES is freely available in HuggingFace https: //huggingface.co/datasets/facebook/2M-Flores-ASL"
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.113,
+                0.847,
+                0.488,
+                0.92
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10897"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.085,
+                0.493,
+                0.325
+            ],
+            "angle": 0,
+            "content": "we prioritize improving translation from controlled videos, while acknowledging the need for future work on generalizing to new settings. For linguistic limitations, ASL sequences are collected one sentence at a time. Although this enables pairwise training and evaluation, such as classical text-based NMT, the generated sequences may not be fully realistic in terms of real-world signing. An example would be the use of placement. In sentence-per-sentence sequence generation, a signer would refer to an entity with their sign each sentence, whereas in long-form conversation, a signer would place the entity in their signing space after first reference and refer them in via use of placement in the following sentences."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.327,
+                0.492,
+                0.502
+            ],
+            "angle": 0,
+            "content": "Our benchmarking is limited compared to the potential capabilities of the dataset. For example, since we have spoken questions, passages and responses, instead of just using a fix modality (spoken passages, text questions and responses), we could explore the performance when using all combinations among modalities (e.g., question in speech, answer in speech, passage in speech; or question in speech, answer in text, passage in speech; or question in speech, answer in speech and passage in text.)"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.112,
+                0.505,
+                0.49,
+                0.616
+            ],
+            "angle": 0,
+            "content": "In terms of compute budget, we estimate it as 47K Nvidia A100 hours by taking into account the product of following factors: number of languages (59 / 39), number of random seeds (3), number of GPUs required by model (8), number of experiment setups (5) and estimated number of hours per experiment (10)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.618,
+                0.49,
+                0.699
+            ],
+            "angle": 0,
+            "content": "Speakers and signers were paid a fair rate. Our recorded data reports self-identified gender by participant. Each of the speakers and signers signed a consent form agreeing on the dataset and its usage that they were participating in."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.712,
+                0.279,
+                0.729
+            ],
+            "angle": 0,
+            "content": "Acknowledgments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
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+            "angle": 0,
+            "content": "This paper is part of the LCM project and authors would like to thank the entire LCM team for the fruitful discussions. Authors want to thank Eduardo Sánchez for early discussions on the project."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "References"
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+            "bbox": [
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+            "angle": 0,
+            "content": "A Languages"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.512,
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+                0.882,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Table 3 reports details on languages covered by FLEURS, TTS and ASR."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+                0.929,
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+            "angle": 0,
+            "content": "10899"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.116,
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+            "angle": 0,
+            "content": "<table><tr><td>Language</td><td>Code</td><td>Script</td><td>Family</td><td>FLEURS</td><td>SeamlessM4T</td><td>Whisper</td><td>2M-BELEBELE</td></tr><tr><td>Mesopotamian Arabic</td><td>acm_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Afrikaans</td><td>afr_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Tosk Albanian</td><td>als_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Amharic</td><td>amh_Ethi</td><td>Ethi</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>North Levantine Arabic</td><td>apc_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Najdi Arabic</td><td>ars_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Moroccan Arabic</td><td>ary_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Egyptian Arabic</td><td>arz_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Assamese</td><td>asm_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>North Azerbaijani</td><td>azj_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Bambara</td><td>bam_Latn</td><td>Latn</td><td>Niger-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Bengali</td><td>ben_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Bengali</td><td>ben_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td>✓(1)</td></tr><tr><td>Standard Tibetan</td><td>bod_Tibt</td><td>Tibt</td><td>Sino-Tibetan</td><td></td><td></td><td></td><td></td></tr><tr><td>Bulgarian</td><td>bul_Cyr1</td><td>Cyr1</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Catalan</td><td>cat_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Cebuano</td><td>ceb_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Czech</td><td>ces_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Central Kurdish</td><td>ckb_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Danish</td><td>dan_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>German</td><td>deu_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Greek</td><td>ell_Grek</td><td>Grek</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>English</td><td>eng_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Estonian</td><td>est_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Basque</td><td>eus_Latn</td><td>Latn</td><td>Basque</td><td></td><td></td><td></td><td></td></tr><tr><td>Finnish</td><td>fin_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>French</td><td>fra_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Fulfulde (Nigerian)</td><td>fuv_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td>✓(2)</td></tr><tr><td>Oromo (West Central)</td><td>gaz_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>(✓)</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Guarani</td><td>grn_Latn</td><td>Latn</td><td>Tupian</td><td></td><td></td><td></td><td></td></tr><tr><td>Gujarati</td><td>guj_Gujr</td><td>Gujr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Haitian 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+            "content": "<table><tr><td>Language</td><td>Code</td><td>Script</td><td>Family</td><td>FLEURS</td><td>SeamlessM4T</td><td>Whisper</td><td>2M-BELEBELE</td></tr><tr><td>Mongolian</td><td>khk_Cyr</td><td>Cyr</td><td>Mongolic</td><td>(✓)</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Khmer</td><td>khm_Khmr</td><td>Khmr</td><td>Austroasiatic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Kinyarwanda</td><td>kin_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Kyrgyz</td><td>kir_Cyr</td><td>Cyr</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Korean</td><td>kor_Hang</td><td>Hang</td><td>Koreanic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Lao</td><td>lao_Laoo</td><td>Laoo</td><td>Kra-Dai</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lingala</td><td>lin_Latn</td><td>Latn</td><td>Niger-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lithuanian</td><td>lit_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Ganda</td><td>lug_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Luo</td><td>luo_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Standard Latvian</td><td>lvs_Latn</td><td>Latn</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Malayam</td><td>mal_Mlym</td><td>Mlym</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Marathi</td><td>mar_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Macedonian</td><td>mkd_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Maltese</td><td>mlt_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Maori</td><td>mri_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Burmese</td><td>mya_Mymr</td><td>Mymr</td><td>Sino-Tibetan</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Dutch</td><td>nld_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Norwegian Bokmål</td><td>nob_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Nepali</td><td>npi_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Nepali</td><td>npi_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Northern Sotho</td><td>nso_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Nyanja</td><td>nya_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Odia</td><td>ory_Orya</td><td>Orya</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Eastern Panjabi</td><td>pan_Guru</td><td>Guru</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Southern Pashto</td><td>pbt_Arab</td><td>Arab</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Western Persian</td><td>pes_Arab</td><td>Arab</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Plateau Malagasy</td><td>plt_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Polish</td><td>pol_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Portuguese</td><td>por_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Romanian</td><td>ron_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Russian</td><td>rus_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Shan</td><td>shn_Mymr</td><td>Mymr</td><td>Tai-Kadai</td><td></td><td></td><td></td><td></td></tr><tr><td>Sinhala</td><td>sin_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Sinhala</td><td>sin_Sinh</td><td>Sinh</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Slovak</td><td>slk_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Slovenian</td><td>slv_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Shona</td><td>sna_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Sindhi</td><td>snd_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Somali</td><td>som_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Southern Sotho</td><td>sot_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Spanish</td><td>spa_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Serbian</td><td>srp_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Swati</td><td>ssw_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Sundanese</td><td>sun_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Swedish</td><td>swe_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Swahili</td><td>swh_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Tamil</td><td>tam_Taml</td><td>Taml</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Telugu</td><td>tel_Telu</td><td>Telu</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Tajik</td><td>tgk_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Tagalog</td><td>tgl_Latn</td><td>Latn</td><td>Austronesian</td><td>(✓)</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Thai</td><td>thaThai</td><td>Thai</td><td>Tai-Kadai</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Tigrinya</td><td>tir_Ethi</td><td>Ethi</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Tswana</td><td>tsn_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr></table>"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
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+                0.524,
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+            "angle": 0,
+            "content": "10901"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.115,
+                0.081,
+                0.923,
+                0.336
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Language</td><td>Code</td><td>Script</td><td>Family</td><td>FLEURS</td><td>SeamlessM4T</td><td>Whisper</td><td>2M-BELEBELE</td></tr><tr><td>Tsonga</td><td>tso_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Tsonga</td><td>tso_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Turkish</td><td>tur_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Ukranean</td><td>ukr_Cyrl</td><td>Cyrl</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Urdu</td><td>urd_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Urdu</td><td>urd_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Northen Uzbek</td><td>uzn_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Vietnamese</td><td>vie_Latn</td><td>Latn</td><td>Austroasiatic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Waray</td><td>war_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Wolof</td><td>wol_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Xhosa</td><td>xho_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Yoruba</td><td>yor_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Chinese</td><td>zho_Hans</td><td>Hans</td><td>Sino-Tibetan</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Chinese</td><td>zho_Hant</td><td>Hant</td><td>Sino-Tibetan</td><td>(✓)</td><td></td><td></td><td></td></tr><tr><td>Standard Malay</td><td>zsm_Latn</td><td>Latn</td><td>Austronesian</td><td>(✓)</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Zulu</td><td>zul_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>American Sign Language</td><td>ase</td><td>-</td><td>Sign Language</td><td></td><td></td><td></td><td>✓(2)</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.343,
+                0.884,
+                0.388
+            ],
+            "angle": 0,
+            "content": "Table 3: Languages details. Column FLEURS reports the languages covered by Speech BELEBELE v1. Column ASR shows the languages reported in the experiment section, note that Hausa is covered by WHISPER-LARGE-V3 but not for SEAMLESSM4T. The number in brackets shows the number of annotations per language."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.411,
+                0.348,
+                0.427
+            ],
+            "angle": 0,
+            "content": "B Annotation Guidelines"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.438,
+                0.489,
+                0.631
+            ],
+            "angle": 0,
+            "content": "Recording process. Find a quiet place free from distractions and noises, and choose a headphone that is comfortable to wear and a good quality microphone that will not distort or break your voice. Read aloud and record the scripts in a pleasant tone and at a constant and even pace, as if you were reading a formal document. Try not to speak too quickly or slowly and aim for a natural pace that is easy to follow. The audio files below provide examples of paces that are expected, too fast, or too slow, for the sentence. The hearing also marks the date for the suspect's right to a rapid trial."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.632,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "To achieve the best sound quality when recording, position the microphone close to your mouth so that the voice will sound clear and present, but not too close that it sounds muddy or you can hear a puff of air. Clearly enunciate the words and avoid mumbling. Be sure to provide a 2-second pause between sentences to add clarity and keep the overall pace down. When dealing with long, complicated sentences that contain multiple clauses or phrases, there are several approaches to ensure clarity and a natural flow as follows. Break it down: Separate the sentence into smaller parts or clauses. Practice reading aloud several times before starting the recording. This can help you get a feel for the rhythm and pacing of the sentence. Pace yourself: Try to maintain a steady, even pace. If the sentence is particularly long, it is possible to take a brief pause at a natural breakpoint to catch your breath."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.412,
+                0.885,
+                0.476
+            ],
+            "angle": 0,
+            "content": "You should read the provided passages aloud without repairs (a repair is the repetition of a word that was incorrectly pronounced to correct its pronunciation)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.477,
+                0.885,
+                0.686
+            ],
+            "angle": 0,
+            "content": "To achieve this, familiarize yourself beforehand with the correct pronunciation of difficult words, proper nouns, and transliterated words, as well as signs and symbols, dates and times, numbers, abbreviations, and punctuation marks. Some elements may have more than one correct pronunciation. In this case, use the one that comes the more naturally to you, as long as it is an accepted pronunciation (i.e., it is acknowledged in your language's dictionaries). Practice reading the passages aloud several times to become more comfortable with the material. Please pay particular attention to the following items:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.697,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Numbers. Number formats can vary from language to language; it is important to follow the pronunciation rules in your language. Here are some general guidelines and examples: Decimal numbers: Read the whole part of the number as a whole number and then individually read every number after the decimal point. For example, in English, the decimal number 3.14 should be read as \"three point one four.\" Different languages may have different rules, and you should follow the rules that are appropriate for your language. Cardinal numbers represent quantities or amounts. Ordinal numbers represent positions or ranks in sequential order and should be read with the appropriate suffix."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10902"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.49,
+                0.166
+            ],
+            "angle": 0,
+            "content": "For example, in English, the ordinal number 1st is read \"first\" (not \"onest\") and 5th is read \"fifth\" (not \"fiveth\"). Different languages may have different rules, and you should follow the rule that is appropriate for your language."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.167,
+                0.49,
+                0.405
+            ],
+            "angle": 0,
+            "content": "Roman numerals are a collection of seven symbols that each represent a value: \\( \\mathrm{I} = 1 \\), \\( \\mathrm{V} = 5 \\), \\( \\mathrm{X} = 10 \\), \\( \\mathrm{L} = 50 \\), \\( \\mathrm{C} = 100 \\), \\( \\mathrm{D} = 500 \\), and \\( \\mathrm{M} = 1,000 \\). The can be pronounced in slightly different ways depending on the context, but they are never pronounced as individual letters. For example, in English, VIII in Henry VIII is pronounced \"Henry the eighth\", while Superbowl LVIII is pronounced \"Superbowl fifty-eight\", but they are never pronounced \"Henry vi i i\" or \"Superbowl I v i i\". Different languages may have different rules, and you should follow the rules that are appropriate for your language. Punctuation marks: As a general rule, punctuation marks should not be pronounced, except quotation marks."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.116,
+                0.408,
+                0.49,
+                0.921
+            ],
+            "angle": 0,
+            "content": "For example, in English, punctuation marks such as periods, commas, colons, semicolons, question marks, and exclamation points are typically not pronounced. For example, the sentence. As a result of this, a big scandal arose. will be pronounced \"As a result of this a big scandal arose\" - not \"As a result of this comma a big scandal arose period\". However, in formal-register English (in the news, for example), a difference is made between content created by the news team and content that should be attributed to someone else by explicitly pronouncing quotation marks. For example, the news transcript The fighter said: \"I am here to try to win this.\" will be pronounced: \"The fighter said, quote, I am here to try to win this. End of quote.\" In this case, different languages may have different rules, and you should follow the rules that are appropriate for your language. Signs and symbols. Signs and symbols need to be pronounced as they would be heard in a speech-only setting. Attention should be paid: (a) to potential number or gender agreement (for example, in English, \"40%\" should be read as \"forty percent\" — not \"forty percents\") (b) to potential differences between the place of the sign or symbol in writing and in speech (for example, in English, the \"$\" sign should be read as \"dollar\" and should be read after the number it precedes; i.e. \"$22\" should be read as \"twenty-two dollars\" — not \"dollars twenty-two\") (c) to the way the sign or symbol gets expanded in speech (for example, in English, \"Platform 9 3/4\" should be read \"platform nine and three quarters\" — not \"platform nine"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.167
+            ],
+            "angle": 0,
+            "content": "three quarters\"). Similarly, \\(50\\mathrm{km / h}\\) would be pronounced \"fifty kilometers per hour\" — not \"fifty kilometers hour\"). Different languages may have different rules, and you should follow the rules that are appropriate for your language."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.177,
+                0.885,
+                0.354
+            ],
+            "angle": 0,
+            "content": "Proper nouns and foreign expressions. Even the same language may have at least 2 different ways to pronounce foreign expressions of proper nouns: (a) one way is to try to approach the way they would sound in the foreign language from which they come (for example, in English, Louis in Louis XIV is pronounced \"leewee\" as it would be in French); (b) the other way is to pronounce them according to the rules of the adopting language (for example, in English, Louis in the City of St Louis is pronounced as in the English proper noun \"Lewis\")"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.365,
+                0.887,
+                0.575
+            ],
+            "angle": 0,
+            "content": "Abbreviations. Abbreviations should be expanded as much as possible. However, it is suggested to refrain from expanding them if their expansion results in unnatural speech. For example, in English, abbreviations such as Dr. or etc. are pronounced \"doctor\" and \"et cetera\", respectively (not \"d r\" nor \"e t c\"). However, abbreviations such as AM or PhD are pronounced as a sequence of letters without being expanded (\"a m\" and \"p h d\", respectively - not \"ante meridiem\" nor \"philosophy doctorate\"). Different languages may have different conventions, and you should follow the conventions that are appropriate for your language."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.588,
+                0.878,
+                0.621
+            ],
+            "angle": 0,
+            "content": "C Ablation study: Synthetic extension in speech evaluation datasets"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.631,
+                0.885,
+                0.744
+            ],
+            "angle": 0,
+            "content": "In this part of our work, we aim to analyze the feasibility of synthetically extending text benchmarks to speech using TTS systems, thereby creating multimodal datasets. Our goal is to understand if it would have been feasible to obtain the speech version of BELEBELE by using state of the art TTS systems, instead of human recordings."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.745,
+                0.885,
+                0.873
+            ],
+            "angle": 0,
+            "content": "For this study we use FLEURS dataset, that contains ASR data in the same domain as BELEBELE. We chose to perform this study in the ASR task because it is simpler compared to other speech tasks, due to its monotonic alignment process and minimal need for reasoning. This ensures that the overall model performance and the complexity of the task are less likely to influence the results."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.874,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "For our experiments, we generate a synthetic copy of the FLEURS dataset using the MMS TTS (Pratap et al., 2024) system on the FLEURS tran"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10903"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.489,
+                0.163
+            ],
+            "angle": 0,
+            "content": "scripts. Then, we benchmark state-of-the-art models (WHISPER, SEAMLESSM4T and MMS ASR) on both the original and synthetic datasets and analyze whether the conclusions remain consistent across both datasets."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.166,
+                0.489,
+                0.293
+            ],
+            "angle": 0,
+            "content": "It is important to note that a decrease in system performance is expected when using synthetic data. However, if this decrease occurs proportionally across all models, the synthetic data could still be useful to benchmark models. Conversely, if the model performance ranking changes, we can conclude that synthetic data is not reliable when benchmarking models."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.295,
+                0.489,
+                0.422
+            ],
+            "angle": 0,
+            "content": "To measure the variability in model rankings between the original and the synthetic data, we track the inversions that occur in the order of the models in the two settings. We define an inversion as a swap between two models that appear in adjacent positions on the list. We count how many swaps are needed in the ranking obtained using synthetic data to match the ranking from the original dataset."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.116,
+                0.433,
+                0.52,
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+            ],
+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\"></td><td colspan=\"2\">SEAMLESSM4T</td><td colspan=\"2\">WHISPER</td><td colspan=\"2\">MMS</td><td rowspan=\"2\">Inv</td></tr><tr><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td></tr><tr><td>Bengali</td><td>14.1</td><td>21.1</td><td>114.7</td><td>105.8</td><td>14.6</td><td>25.0</td><td></td></tr><tr><td>Catalan</td><td>8.2</td><td>13.2</td><td>6.7</td><td>16.4</td><td>10.3</td><td>21.8</td><td>✓</td></tr><tr><td>Dutch</td><td>9.9</td><td>20.0</td><td>8.5</td><td>19.7</td><td>12.4</td><td>28.3</td><td></td></tr><tr><td>English</td><td>6.0</td><td>11.7</td><td>4.5</td><td>9.8</td><td>12.3</td><td>19.2</td><td></td></tr><tr><td>Finnish</td><td>20.1</td><td>20.8</td><td>12.5</td><td>18.9</td><td>13.1</td><td>18.4</td><td>✓</td></tr><tr><td>French</td><td>9.5</td><td>10.8</td><td>6.7</td><td>11.3</td><td>12.4</td><td>16.6</td><td>✓</td></tr><tr><td>German</td><td>8.5</td><td>13.9</td><td>5.2</td><td>12.3</td><td>10.5</td><td>20.8</td><td></td></tr><tr><td>Hindi</td><td>11.9</td><td>13.4</td><td>33.5</td><td>28.7</td><td>11.1</td><td>18.3</td><td>✓</td></tr><tr><td>Indonesian</td><td>12.1</td><td>12.8</td><td>8.7</td><td>14.2</td><td>13.2</td><td>21.9</td><td>✓</td></tr><tr><td>Korean</td><td>25.7</td><td>40.3</td><td>15.4</td><td>29.9</td><td>47.8</td><td>61.2</td><td></td></tr><tr><td>Polish</td><td>13.0</td><td>14.7</td><td>8.1</td><td>13.3</td><td>11.6</td><td>18.1</td><td>✓</td></tr><tr><td>Portuguese</td><td>9.0</td><td>8.0</td><td>4.1</td><td>6.9</td><td>8.7</td><td>10.4</td><td>✓</td></tr><tr><td>Romanian</td><td>12.6</td><td>11.7</td><td>13.5</td><td>25.4</td><td>12.0</td><td>15.4</td><td>✓</td></tr><tr><td>Russian</td><td>10.2</td><td>18.6</td><td>5.6</td><td>17.4</td><td>18.8</td><td>34.3</td><td></td></tr><tr><td>Spanish</td><td>6.3</td><td>9.1</td><td>3.4</td><td>10.0</td><td>6.4</td><td>10.8</td><td>✓</td></tr><tr><td>Swahili</td><td>19.5</td><td>19.0</td><td>64.2</td><td>58.4</td><td>14.2</td><td>19.0</td><td>✓</td></tr><tr><td>Swedish</td><td>15.4</td><td>20.1</td><td>11.3</td><td>19.1</td><td>21.0</td><td>27.8</td><td></td></tr><tr><td>Telugu</td><td>27.4</td><td>28.0</td><td>132.2</td><td>133.9</td><td>24.2</td><td>27.8</td><td></td></tr><tr><td>Thai</td><td>127.8</td><td>135.5</td><td>104.0</td><td>121.3</td><td>99.8</td><td>99.9</td><td></td></tr><tr><td>Turkish</td><td>18.6</td><td>23.0</td><td>8.4</td><td>16.5</td><td>19.2</td><td>30.3</td><td></td></tr><tr><td>Ukrainian</td><td>15.0</td><td>23.5</td><td>9.8</td><td>21.8</td><td>18.1</td><td>34.7</td><td></td></tr><tr><td>Vietnamese</td><td>16.0</td><td>20.1</td><td>10.2</td><td>14.2</td><td>25.8</td><td>25.3</td><td></td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.114,
+                0.705,
+                0.488,
+                0.749
+            ],
+            "angle": 0,
+            "content": "Table 4: \\( \\mathrm{{WER}}\\left(  \\downarrow  \\right) \\) results on the ASR task. Last column marks if the language has at least 1 inversion in ASR performance ranking comparing human vs TTS inputs."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.883,
+                0.326
+            ],
+            "angle": 0,
+            "content": "In Table 4 we see that in the ASR setting, conclusions regarding model performance can vary depending on whether human or synthetic data is used. Although these conclusions are specific to the evaluated tasks and datasets, we demonstrate that even with the outstanding performance of current TTS methods, this does not guarantee the reliability of the data they generate when it comes to evaluation purposes. This is true not only for low-resource languages, but also for high-resource languages such as French or Spanish. These findings show that speech benchmarks might not be reliable if synthetically generated even in widely researched areas, further supporting the creation of evaluation datasets by humans."
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.114,
+                0.76,
+                0.489,
+                0.797
+            ],
+            "angle": 0,
+            "content": "Note that we perform the study on the FLEURS languages that are covered by all MMS, WHISPER and SEAMLESSM4T."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
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+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "10904"
+        }
+    ]
+]

2025/2M-BELEBELE_ Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF/01f08187-e16e-4ec5-be1b-7fef9667f6ed_origin.pdf

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2025/2M-BELEBELE_ Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF/full.md

@@ -0,0 +1,202 @@
+# 2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset
+
+Marta R. Costa-jussà, Bokai Yu, Pierre Andrews, Belen Alastruey, Necati Cihan Camgoz Joe Chuang, Jean Maillard, Christophe Ropers, Arina Turkantenko, Carleigh Wood FAIR, Meta
+
+{costajussa,bokai, mortimer,alastruey,neccam,
+
+joechuang, jeanmm, chrisropers, arinatur, carleighwood}@meta.com
+
+# Abstract
+
+We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 91 spoken languages at the intersection of BELEBELE and FLEURS, and one sign language (ASL). As a by-product we also extend the Automatic Speech Recognition Benchmark, FLEURS, by  $20\%$ .
+
+We evaluate 2M-BELEBELE dataset for both 5-shot and zero-shot settings and across languages, the speech comprehension accuracy is  $\approx 10\%$  average lower compared to reading comprehension.
+
+# 1 Introduction
+
+From an AI perspective, text understanding and generation services are used globally in more than a hundred languages, but the scarcity of labeled data poses a significant challenge to developing functional systems in most languages. Although natural language processing (NLP) datasets with extensive language coverage, such as FLORES-200 (NLLBTeam, 2024), are available, they mainly concentrate on machine translation (MT). Multilingual evaluation benchmarks such as those for multilingual question answering (Lewis et al., 2020; Clark et al., 2020), natural language inference (Conneau et al., 2018), summarization (Hasan et al., 2021; Ladhak et al., 2020), and reasoning datasets (Ponti et al., 2020; Lin et al., 2021) collectively cover only about 30 languages. Furthermore, the extension of such datasets to speech or American Sign Language (ASL) is lacking, with the exception of FLEURS (Conneau et al., 2022; Tanzer, 2024), which is based on FLORES-200.
+
+The recent BELEBELE benchmark is the first corpus that addresses text reading comprehension for a large number of languages following a multi-way parallel approach (Bandarkar et al., 2023). The entire BELEBELE text statistics are summarized in Table 1 in Appendix A.
+
+![](images/67dbaae73cd2450c2d6eabface2782ad4809e7c00fa90fdd0131e9a82d9b718c.jpg)  
+Figure 1: 2M-BELEBELE entry: beyond passage, question and multiple choice answers in text from BELEBELE, we extend to ASL and 91 speech languages.
+
+In this work, we extend the BELEBELE dataset to speech and sign (Section 3). By doing so, we create the first highly multilingual speech and sign comprehension dataset: 2M-BELEBELE, which is composed of human speech recordings covering 91 languages and human sign recordings for ASL. This dataset will enable researchers conducting experiments on multilingual speech and ASL understanding.
+
+As a by-product of 2M-BELEBELE, we also extend the FLEURS dataset (which is widely used to benchmark language identification and ASR) by providing recordings for more FLORES-200 sentences than were previously available and adding sign language, creating a new 2M-FLORES. This 2M-FLORES extends FLEURS by  $20\%$ .
+
+Finally, we provide a very basic set of experiments that evaluate 2M-BELEBELE and provide some reference results. We use direct and/or cascaded systems to evaluate 2M-BELEBELE dataset (Section 4). We also list several further experimentation that 2M-BELEBELE unblocks. Note that the main contribution of this paper is the creation of the first highly multilingual speech and sign comprehension dataset. The complete set of experiments
+
+is out of the scope of this paper (Limitations). By open-sourcing our dataset, we encourage the scientific community to pursue such experimentation.
+
+# 2 Related Work
+
+Speech Comprehension The outstanding performance of some MT and text-to-speech (TTS) models has enabled a rise in the number of works using synthetically generated training data. Furthermore, some recent works propose to also use synthetic data for evaluation; e.g., (Ustun et al., 2024; SEAM-LESSCommunicationTeam, 2025; Nguyen et al., 2024; Nachmani et al., 2023). This strategy allows researchers to extend datasets to low-resource languages and to other modalities, such as speech. However, we prove that using synthetic data for evaluation does not provide comparable conclusions as relying on human speech for the particular task of automatic speech recognition (ASR) and the FLEURS domain (Appendix C). The evaluation dataset that is closest to the speech comprehension evaluation dataset presented in this paper is the generative QA dataset proposed in (Nachmani et al., 2023). The dataset covers 300 questions in English.
+
+ASL Comprehension Compared to spoken languages, sign languages are considered low-resource languages for natural language processing (Yin et al., 2021). Most popular datasets cover small domains discourse; e.g., weather broadcasts (Camgoz et al., 2018), which has limited real world applications. There have been previous releases of large scale open domain sign language datasets; e.g., (Albanie et al., 2021; Shi et al., 2022; Uthus et al., 2024). However, the results and challenges on such datasets suggest that computational sign language research still requires additional datasets to reach the performance of their spoken language counterparts (Müller et al., 2022, 2023). With the release of the ASL extension of the BELEBELE dataset, we aim to provide additional, high quality sign language data with gloss annotations to underpin further computational sign language research. Furthermore, due to the paragraph-level nature of the BELEBELE dataset, we enable paragraph-context sign language translation, which has been reported to improve translation performance (Sincan et al., 2023).
+
+# 3 2M-BELEBELE
+
+FLEURS and BELEBELE passage alignment. Since BELEBELE uses passages constructed from
+
+sentences in the FLORES-200 dataset, and FLEURS (Conneau et al., 2022) is a human speech version of FLORES-200 for a subset of its languages, we create a speech version of BELEBELE by aligning its passages with the speech segments available in FLEURS. This extension can be done without extra human annotation, just by computing the alignment between FLEURS and BELEBELE passages. However, such alignment does not cover the entire BELEBELE corpus because FLEURS does not cover the entirety of FLORES-200. There are 91 languages shared between FLEURS and BELEBELE. FLEURS does not cover the same passages as BELEBELE in all those 91 languages, which means that some languages have more speech passages than others. In general, we are able to match almost  $\approx 80\%$  of the passages. Figure 2 shows the number of FLEURS paragraphs we can match, thus obtaining the number of paragraphs that must be recorded in order to cover all passages BELEBELE.
+
+Speech recordings. We commission human recordings for the part of the BELEBELE dataset that is not covered by existing FLEURS recordings, as well as for elements of BELEBELE that do not exist in FLEURS (i.e. questions and answers). Recording participants must be native speakers of the languages they record. They must have an impeccable grasp of the conventions used in their respective languages for the narration of texts. The three tasks that participants are asked to perform are: (1) Read aloud and record the text passages provided (from FLORES-200); (2) Read aloud and record the provided written questions; (3) Read aloud and record the provided written answers. For the task, we provide the participants with (a) the text of the sentences to be recorded in TSV format (the number of passages may differ from language to language), (b) the written questions (900 per language, and (c) the written answer options (3,600 per language). Additional details on the recording guidelines provided to annotators are reported in the appendix B. We verify the quality of the recordings by randomly selecting 270 recordings (30% of sample size) and ensuring that the recordings do not contain background or ambient noise and that the voices of the participants are clearly audible.
+
+Sign recordings. To obtain ASL sign recordings, we provide translators of ASL and native signers with the English text version of the sentences to be recorded. The interpreters are then asked to
+
+![](images/637e03ba16e7d6f02ccec028a4e76f28b4bd0c2b59140524a0171fa03239b4b1.jpg)  
+Figure 2: FLEURS vs New Recordings from 2M-BELEBELE for sentences in passages.
+
+translate these sentences into ASL, create glosses for all sentences, and record their interpretations into ASL one sentence at a time. The glosses are subjected to an additional quality check by expert annotators to harmonize the glossing format. To harmonize the recording conditions and eliminate visual bias, the videos are recorded against plain monochrome backgrounds (e.g., white or green), and signers are requested to wear monochrome upper body clothing (e.g., black). All videos are captured in 1920x1080p resolution with all of the signing space covered in FOV. The recordings are done in 60 frames per second to address most of the motion blur that happens during signing.
+
+2M-BELEBELE Statistics. The final dataset is composed of 91 in speech plus 1 in sign. Each of the languages' respective subsets includes 2,000 utterances organized in 488 distinct passages, 900 questions, and 4 multiple choice answers per question. For our recorded data (the red portion of Figure 2 plus questions and answers), we have one audio file or two per sentence, depending on the number of available participants (one participant only in 23 languages, and two participants in 51 languages). When two speakers are available, we request that one should represent a higher-pitch range, and the other a lower-pitch range for each passage. More details are available in Appendix A.
+
+In addition, the data set includes video recordings in ASL for 2,000 FLORES sentences (not including the test partition) and is similarly organized in 488 distinct passages, as well as 900 questions and 4 multiple-choice answers for each question (see summary table 1). The ASL dataset was recorded by two interpreters, but, contrary to what was possible in other languages, each interpreter could only cover one-half of the dataset each.
+
+<table><tr><td colspan="2">Passages</td><td colspan="2">Questions/Answers</td></tr><tr><td>Distinct Passages</td><td>488</td><td>Distinct Q</td><td>900</td></tr><tr><td>Questions per passage</td><td>1-2</td><td>Multiple-choice A</td><td>4</td></tr><tr><td>Avg words (std)</td><td>79.1 (26.2)</td><td>Avg words Q (std)</td><td>12.9 (4.0)</td></tr><tr><td>Avg sentences (std)</td><td>4.1 (1.4)</td><td>Avg words A (std)</td><td>4.2 (2.9)</td></tr></table>
+
+Table 1: Statistics for 2M-BELEBELE, which covers 91 spoken languages plus ASL. Average words are computed for English.
+
+# 4 Experiments
+
+We evaluate 2M-BELEBELE, and compare performance across modalities. Our comparison is limited in number of systems and combination of modalities. 2M-BELEBELE offers the opportunity to check multimodal comprehension by combining speech/text/sign passages; questions and answers. In our case, we only provide results for entire text passages, questions and answers and speech passages, text questions and answers. A more comprehensive set of experiments is out of the scope of this paper, which aims at unblocking such experimentation by open-sourcing the dataset itself.
+
+Systems. We use the speech section of the 2M-BELEBELE dataset to evaluate the speech comprehension task with a cascaded system consisting of first speech recognition (ASR) using the WHISPER-LARGE-V3 model (Radford et al., 2022) (hereinafter, WHISPER) and SEAMLESSM4T (corresponding to SEAMLESSM4T-LARGE V2) model (SEAMLESSCommunicationTeam, 2025) feeding into LLAMA-3<sup>1</sup>. We also provide results with a unified system SPIRITLM (Nguyen et al., 2024), which is a multimodal language model that freely mixes text and speech. Since the size of this model is 7B and is based on LLAMA-2, we also add a comparison to the LLAMA-2 model. We compare these results with LLAMA-3 and LLAMA-3-CHAT
+
+<table><tr><td>Dataset</td><td>Model</td><td>Size</td><td>Vocab</td><td>#Lang</td><td>AVG</td><td>% ≥ 50</td><td>% ≥ 70</td><td>Eng</td><td>non-Eng AVG</td></tr><tr><td colspan="10">5-Shot In-Context Learning (examples in English)</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>85.4</td><td>96.6</td><td>94.9</td><td>94.8</td><td>85.2</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>59</td><td>77.4</td><td>88.1</td><td>72.9</td><td>94.4</td><td>77.1</td></tr><tr><td>BELEBELE</td><td>LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>84.9</td><td>97.4</td><td>94.9</td><td>94.8</td><td>84.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>77.1</td><td>89.7</td><td>71.8</td><td>94.4</td><td>76.6</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3</td><td>70B</td><td>128K</td><td>39</td><td>81.7</td><td>94.9</td><td>92.7</td><td>93.5</td><td>81.4</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-2</td><td>7B</td><td>32K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>49.9</td><td>-</td></tr><tr><td>2M-BELEBELE</td><td>SPIRITLM</td><td>7B</td><td>37K</td><td>1</td><td>-</td><td>-</td><td>-</td><td>25.9</td><td>-</td></tr><tr><td colspan="10">Zero-Shot</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>87.5</td><td>98.3</td><td>96.6</td><td>95.8</td><td>87.3</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>59</td><td>79.4</td><td>93.2</td><td>78.0</td><td>95.7</td><td>79.2</td></tr><tr><td>BELEBELE</td><td>LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>87.0</td><td>97.4</td><td>94.9</td><td>95.8</td><td>86.7</td></tr><tr><td>2M-BELEBELE</td><td>WHISPER + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>79.1</td><td>92.3</td><td>76.9</td><td>95.7</td><td>78.7</td></tr><tr><td>2M-BELEBELE</td><td>SEAMLESSM4T + LLAMA-3-CHAT</td><td>70B</td><td>128K</td><td>39</td><td>84.8</td><td>94.9</td><td>94.9</td><td>95.5</td><td>84.5</td></tr></table>
+
+Table 2: Summary of accuracy results on 2M-BELEBELE compared to BELEBELE across models and evaluation settings. AVG and non-Eng AVG refers to QA accuracy; and  $\geq 50 / 70$  refers to the proportion of languages for which a given model performs above  $50 / 70\%$  for question and answer in text and passage in speech.
+
+using the BELEBELE text passage as input.
+
+Languages For the mentioned systems, we report the results in 5-shot in-context learning and zero-shot on 59 languages at the intersection of language coverage between WHISPER and 2M-BELEBELE and 39 languages at the intersection of WHISPER, SEAMLESSM4T and 2M-BELEBELE (see Table 3 in Appendix A with the detailed list of languages per system).
+
+Zero-shot Evaluation. We use the same evaluation strategy as in (Bandarkar et al., 2023). SPIRITLM is not available in chat mode.
+
+5-shot In-Context Learning. The few-shot examples are taken randomly from the English training set and they are prompted as text format to the model. Different from (Bandarkar et al., 2023), we do not pick the answer with the highest probability but directly assess the predicted letter of the answer. For 5-shot and zero-shot settings, our instruction prompt is as follows "Given the following passage, query, and answer choices, output the letter corresponding to the correct answer. Do not write any explanation. Only output the letter within A, B, C, or D that corresponds to the correct answer." and we report the averaged accuracy over 3 runs<sup>2</sup>.
+
+Results. Table 2 reports the summary of the results at the intersection of languages between system availability (either 59 or 39 as reported in detail in Table 3). The English drop from direct text to speech task does not vary much between 5-shot and zero-shot strategies, being slightly higher in the zero-shot setting (coherently with previous
+
+LLAMA-3 results that show better performance in zero-shot in other tasks $^{3}$ ). When comparing speech and text comprehension, we observe that speech decreases performance in about  $10\%$  when comparing for 59 languages (using WHISPER for ASR). However, this decrease shortens (to about  $2 - 3\%$  average) when comparing for 39 languages (using SEAMLESSM4T for ASR). 2M-BELEBELE accuracy results per language compared to BELEBELE are shown in Figure 3 in the 59 languages at the intersection of WHISPER and 2M-BELEBELE languages for LLAMA-3 (reading comprehension) and WHISPER + LLAMA-3 (speech comprehension).
+
+Differences in speech and text vary slightly depending on the languages. Low-resource languages have a greater variation between text and speech BELEBELE. The ten languages with the largest gap are: Burmese, Maltese, Assamese, Mongolian, Southern Pashto, Sindhi, Telugu, Javanese, Tajik, Georgian.
+
+Additionally, Table 2 reports English results for SPIRITLM, a direct multimodal model. One of the reasons SPIRITLM may be performing worse is that 5-shot examples are in text, while the passage on the asked question is in speech. Best results in average for speech comprehension are achieved with the SEAMLESSM4T + LLAMA-3 cascade.
+
+ASL We know from previous large-scale translation attempts (Albanie et al., 2021; Müller et al., 2022) that models struggle to generalize over both individuals/appearance and large domain of discourse. Compared to speech and text models, sign
+
+![](images/052ecf45a644ca967eb01b6420c8c9542c5844298696cd0ba3a8507f0f617d4c.jpg)  
+Figure 3: Speech and Text BELEBELE accuracy results in 59 languages. We compare text performance with LLAMA-3-CHAT (zero-shot) and speech performance with WHISPER +LLAMA-3-CHAT (asr+zero-shot).
+
+language models suffer from having to learn generalized representations from high-dimensional inputs, i.e. videos, without overfitting to limited training dataset. Previous attempts have been made to create a more generalizable abstraction layer in the form of subunits (Camgoz et al., 2020), similar to phonemes for speech, which achieved promising results on a translation task with a small discourse domain. However, this work is yet to be applied to large discourse domain translation tasks. The best results in the FLORES domain have been achieved with close models that are not available (Zhang et al., 2024). Trying (Rust et al., 2024) as an open model did not perform above chance in the final reading comprehension dataset. However, we believe that the release of this new dataset with the additional gloss annotation will help in training models that generalize over individuals better and improve large-scale sign language translation.
+
+# 5 Conclusions
+
+The 2M-BELEBELE data set<sup>4</sup> allow evaluating natural language comprehension in a large number of languages, including ASL. 2M-BELEBELE is purely human-made and covers BELEBELE passages, questions, and answers for 91 languages in the speech modality and ASL. As a by-product, 2M-FLORES extends FLEURS by  $20\%$
+
+# Limitations and ethical considerations
+
+Our speech annotations do not have the entire set completed with two annotators. Due to the high volume of the dataset, not every recording has been thoroughly verified. Some of the languages in 2M-BELEBELE are low-resource languages, which pose a challenge in sourcing professionals to record. Therefore, some of the audios were recorded in home settings and may contain minor background noise, static noise, echoes, and, occasionally, the speech could be slightly muffled or soft. All annotators are native speakers of the target language, but they may have regional accents in their speech, and their personal speech styles may be present in the audio as well. However, these are minor limitations since the mentioned imperfections should not affect intelligibility; all the recordings can be clearly understood by human standards. Regarding regional accents, from a linguistic perspective, they do not imply "incorrectness." We have collected data from several speakers to ensure that the dataset reflects the diversity present in the languages.
+
+We can group the ASL limitations under two categories, namely visual and linguistic. For visual limitations, ASL sequences are recorded in what can be considered laboratory environments with few signer variance. This makes it harder for models trained on them to generalize to unseen environments and signers. However, it is a justified and minor limitation. Using controlled environments allows us to break down the task into two parts: translating sign language from videos and generalizing to new environments and signers. Since sign language translation is a low-resource task,
+
+we prioritize improving translation from controlled videos, while acknowledging the need for future work on generalizing to new settings. For linguistic limitations, ASL sequences are collected one sentence at a time. Although this enables pairwise training and evaluation, such as classical text-based NMT, the generated sequences may not be fully realistic in terms of real-world signing. An example would be the use of placement. In sentence-per-sentence sequence generation, a signer would refer to an entity with their sign each sentence, whereas in long-form conversation, a signer would place the entity in their signing space after first reference and refer them in via use of placement in the following sentences.
+
+Our benchmarking is limited compared to the potential capabilities of the dataset. For example, since we have spoken questions, passages and responses, instead of just using a fix modality (spoken passages, text questions and responses), we could explore the performance when using all combinations among modalities (e.g., question in speech, answer in speech, passage in speech; or question in speech, answer in text, passage in speech; or question in speech, answer in speech and passage in text.)
+
+In terms of compute budget, we estimate it as 47K Nvidia A100 hours by taking into account the product of following factors: number of languages (59 / 39), number of random seeds (3), number of GPUs required by model (8), number of experiment setups (5) and estimated number of hours per experiment (10).
+
+Speakers and signers were paid a fair rate. Our recorded data reports self-identified gender by participant. Each of the speakers and signers signed a consent form agreeing on the dataset and its usage that they were participating in.
+
+# Acknowledgments
+
+This paper is part of the LCM project and authors would like to thank the entire LCM team for the fruitful discussions. Authors want to thank Eduardo Sánchez for early discussions on the project.
+
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+
+# A Languages
+
+Table 3 reports details on languages covered by FLEURS, TTS and ASR.
+
+<table><tr><td>Language</td><td>Code</td><td>Script</td><td>Family</td><td>FLEURS</td><td>SeamlessM4T</td><td>Whisper</td><td>2M-BELEBELE</td></tr><tr><td>Mesopotamian Arabic</td><td>acm_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Afrikaans</td><td>afr_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Tosk Albanian</td><td>als_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Amharic</td><td>amh_Ethi</td><td>Ethi</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>North Levantine Arabic</td><td>apc_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Modern Standard Arabic</td><td>arb_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Najdi Arabic</td><td>ars_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Moroccan Arabic</td><td>ary_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Egyptian Arabic</td><td>arz_Arab</td><td>Arab</td><td>Afro-Asiatic</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Assamese</td><td>asm_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>North Azerbaijani</td><td>azj_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Bambara</td><td>bam_Latn</td><td>Latn</td><td>Niger-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Bengali</td><td>ben_Beng</td><td>Beng</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Bengali</td><td>ben_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td>✓(1)</td></tr><tr><td>Standard Tibetan</td><td>bod_Tibt</td><td>Tibt</td><td>Sino-Tibetan</td><td></td><td></td><td></td><td></td></tr><tr><td>Bulgarian</td><td>bul_Cyr1</td><td>Cyr1</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Catalan</td><td>cat_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Cebuano</td><td>ceb_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Czech</td><td>ces_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Central Kurdish</td><td>ckb_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Danish</td><td>dan_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>German</td><td>deu_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Greek</td><td>ell_Grek</td><td>Grek</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>English</td><td>eng_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Estonian</td><td>est_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Basque</td><td>eus_Latn</td><td>Latn</td><td>Basque</td><td></td><td></td><td></td><td></td></tr><tr><td>Finnish</td><td>fin_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>French</td><td>fra_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Fulfulde (Nigerian)</td><td>fuv_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td>✓(2)</td></tr><tr><td>Oromo (West Central)</td><td>gaz_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>(✓)</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Guarani</td><td>grn_Latn</td><td>Latn</td><td>Tupian</td><td></td><td></td><td></td><td></td></tr><tr><td>Gujarati</td><td>guj_Gujr</td><td>Gujr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Haitian Creole</td><td>hat_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Hausa</td><td>hau_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td>(✓)</td><td></td><td>✓(2)</td></tr><tr><td>Hebrew</td><td>heb_Hebr</td><td>Hebr</td><td>Afro-Asiatic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Hindi</td><td>hin_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Hindi</td><td>hin_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Croatian</td><td>hrv_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Hungarian</td><td>hun_Latn</td><td>Latn</td><td>Uralic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Armenian</td><td>hye_Armn</td><td>Armn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Igbo</td><td>ibo_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Ilocano</td><td>ilo_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Indonesian</td><td>ind_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Icelandic</td><td>isl_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Italian</td><td>ita_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Javanese</td><td>jav_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Japanese</td><td>jpn_Jpan</td><td>Jpan</td><td>Japonie</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Jingpho</td><td>kac_Latn</td><td>Latn</td><td>Sino-Tibetan</td><td></td><td></td><td></td><td></td></tr><tr><td>Kannada</td><td>kan_Knda</td><td>Knda</td><td>Dravidian</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Georgian</td><td>kat_Geor</td><td>Geor</td><td>Kartvelian</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Kazakh</td><td>kaz_Cyrl</td><td>Cyr1</td><td>Turkic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Kabuverdianu</td><td>kea_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Mongolian</td><td>khk_Cyr</td><td>Cyr</td><td>Mongolic</td><td>(✓)</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Khmer</td><td>khm_Khmr</td><td>Khmr</td><td>Austroasiatic</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Kinyarwanda</td><td>kin_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Kyrgyz</td><td>kir_Cyr</td><td>Cyr</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Korean</td><td>kor_Hang</td><td>Hang</td><td>Koreanic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Lao</td><td>lao_Laoo</td><td>Laoo</td><td>Kra-Dai</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lingala</td><td>lin_Latn</td><td>Latn</td><td>Niger-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Lithuanian</td><td>lit_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Ganda</td><td>lug_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Luo</td><td>luo_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Standard Latvian</td><td>lvs_Latn</td><td>Latn</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Malayam</td><td>mal_Mlym</td><td>Mlym</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Marathi</td><td>mar_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Macedonian</td><td>mkd_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Maltese</td><td>mlt_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Maori</td><td>mri_Latn</td><td>Latn</td><td>Austronesian</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Burmese</td><td>mya_Mymr</td><td>Mymr</td><td>Sino-Tibetan</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Dutch</td><td>nld_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Norwegian Bokmål</td><td>nob_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Nepali</td><td>npi_Deva</td><td>Deva</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Nepali</td><td>npi_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Northern Sotho</td><td>nso_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Nyanja</td><td>nya_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Odia</td><td>ory_Orya</td><td>Orya</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Eastern Panjabi</td><td>pan_Guru</td><td>Guru</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Southern Pashto</td><td>pbt_Arab</td><td>Arab</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Western Persian</td><td>pes_Arab</td><td>Arab</td><td>Indo-European</td><td>(✓)</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Plateau Malagasy</td><td>plt_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Polish</td><td>pol_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Portuguese</td><td>por_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Romanian</td><td>ron_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Russian</td><td>rus_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Shan</td><td>shn_Mymr</td><td>Mymr</td><td>Tai-Kadai</td><td></td><td></td><td></td><td></td></tr><tr><td>Sinhala</td><td>sin_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Sinhala</td><td>sin_Sinh</td><td>Sinh</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Slovak</td><td>slk_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(1)</td></tr><tr><td>Slovenian</td><td>slv_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Shona</td><td>sna_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Sindhi</td><td>snd_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Somali</td><td>som_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Southern Sotho</td><td>sot_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Spanish</td><td>spa_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Serbian</td><td>srp_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td></td><td>✓</td><td>✓(2)</td></tr><tr><td>Swati</td><td>ssw_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Sundanese</td><td>sun_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Swedish</td><td>swe_Latn</td><td>Latn</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Swahili</td><td>swh_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Tamil</td><td>tam_Taml</td><td>Taml</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Telugu</td><td>tel_Telu</td><td>Telu</td><td>Dravidian</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Tajik</td><td>tgk_Cyr</td><td>Cyr</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Tagalog</td><td>tgl_Latn</td><td>Latn</td><td>Austronesian</td><td>(✓)</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Thai</td><td>thaThai</td><td>Thai</td><td>Tai-Kadai</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Tigrinya</td><td>tir_Ethi</td><td>Ethi</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Tswana</td><td>tsn_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td></td><td></td><td></td><td></td></tr><tr><td>Tsonga</td><td>tso_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Tsonga</td><td>tso_Latn</td><td>Latn</td><td>Afro-Asiatic</td><td></td><td></td><td></td><td></td></tr><tr><td>Turkish</td><td>tur_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(1)</td></tr><tr><td>Ukranean</td><td>ukr_Cyrl</td><td>Cyrl</td><td>Indo-European</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Urdu</td><td>urd_Arab</td><td>Arab</td><td>Indo-European</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Urdu</td><td>urd_Latn</td><td>Latn</td><td>Indo-European</td><td></td><td></td><td></td><td></td></tr><tr><td>Northen Uzbek</td><td>uzn_Latn</td><td>Latn</td><td>Turkic</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Vietnamese</td><td>vie_Latn</td><td>Latn</td><td>Austroasiatic</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Waray</td><td>war_Latn</td><td>Latn</td><td>Austronesian</td><td></td><td></td><td></td><td></td></tr><tr><td>Wolof</td><td>wol_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Xhosa</td><td>xho_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(1)</td></tr><tr><td>Yoruba</td><td>yor_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td>✓</td><td>✓</td><td>✓(2)</td></tr><tr><td>Chinese</td><td>zho_Hans</td><td>Hans</td><td>Sino-Tibetan</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Chinese</td><td>zho_Hant</td><td>Hant</td><td>Sino-Tibetan</td><td>(✓)</td><td></td><td></td><td></td></tr><tr><td>Standard Malay</td><td>zsm_Latn</td><td>Latn</td><td>Austronesian</td><td>(✓)</td><td></td><td></td><td>✓(2)</td></tr><tr><td>Zulu</td><td>zul_Latn</td><td>Latn</td><td>Atlantic-Congo</td><td>✓</td><td></td><td></td><td>✓(2)</td></tr><tr><td>American Sign Language</td><td>ase</td><td>-</td><td>Sign Language</td><td></td><td></td><td></td><td>✓(2)</td></tr></table>
+
+Table 3: Languages details. Column FLEURS reports the languages covered by Speech BELEBELE v1. Column ASR shows the languages reported in the experiment section, note that Hausa is covered by WHISPER-LARGE-V3 but not for SEAMLESSM4T. The number in brackets shows the number of annotations per language.
+
+# B Annotation Guidelines
+
+Recording process. Find a quiet place free from distractions and noises, and choose a headphone that is comfortable to wear and a good quality microphone that will not distort or break your voice. Read aloud and record the scripts in a pleasant tone and at a constant and even pace, as if you were reading a formal document. Try not to speak too quickly or slowly and aim for a natural pace that is easy to follow. The audio files below provide examples of paces that are expected, too fast, or too slow, for the sentence. The hearing also marks the date for the suspect's right to a rapid trial.
+
+To achieve the best sound quality when recording, position the microphone close to your mouth so that the voice will sound clear and present, but not too close that it sounds muddy or you can hear a puff of air. Clearly enunciate the words and avoid mumbling. Be sure to provide a 2-second pause between sentences to add clarity and keep the overall pace down. When dealing with long, complicated sentences that contain multiple clauses or phrases, there are several approaches to ensure clarity and a natural flow as follows. Break it down: Separate the sentence into smaller parts or clauses. Practice reading aloud several times before starting the recording. This can help you get a feel for the rhythm and pacing of the sentence. Pace yourself: Try to maintain a steady, even pace. If the sentence is particularly long, it is possible to take a brief pause at a natural breakpoint to catch your breath.
+
+You should read the provided passages aloud without repairs (a repair is the repetition of a word that was incorrectly pronounced to correct its pronunciation).
+
+To achieve this, familiarize yourself beforehand with the correct pronunciation of difficult words, proper nouns, and transliterated words, as well as signs and symbols, dates and times, numbers, abbreviations, and punctuation marks. Some elements may have more than one correct pronunciation. In this case, use the one that comes the more naturally to you, as long as it is an accepted pronunciation (i.e., it is acknowledged in your language's dictionaries). Practice reading the passages aloud several times to become more comfortable with the material. Please pay particular attention to the following items:
+
+Numbers. Number formats can vary from language to language; it is important to follow the pronunciation rules in your language. Here are some general guidelines and examples: Decimal numbers: Read the whole part of the number as a whole number and then individually read every number after the decimal point. For example, in English, the decimal number 3.14 should be read as "three point one four." Different languages may have different rules, and you should follow the rules that are appropriate for your language. Cardinal numbers represent quantities or amounts. Ordinal numbers represent positions or ranks in sequential order and should be read with the appropriate suffix.
+
+For example, in English, the ordinal number 1st is read "first" (not "onest") and 5th is read "fifth" (not "fiveth"). Different languages may have different rules, and you should follow the rule that is appropriate for your language.
+
+Roman numerals are a collection of seven symbols that each represent a value:  $\mathrm{I} = 1$ ,  $\mathrm{V} = 5$ ,  $\mathrm{X} = 10$ ,  $\mathrm{L} = 50$ ,  $\mathrm{C} = 100$ ,  $\mathrm{D} = 500$ , and  $\mathrm{M} = 1,000$ . The can be pronounced in slightly different ways depending on the context, but they are never pronounced as individual letters. For example, in English, VIII in Henry VIII is pronounced "Henry the eighth", while Superbowl LVIII is pronounced "Superbowl fifty-eight", but they are never pronounced "Henry vi i i" or "Superbowl I v i i". Different languages may have different rules, and you should follow the rules that are appropriate for your language. Punctuation marks: As a general rule, punctuation marks should not be pronounced, except quotation marks.
+
+For example, in English, punctuation marks such as periods, commas, colons, semicolons, question marks, and exclamation points are typically not pronounced. For example, the sentence. As a result of this, a big scandal arose. will be pronounced "As a result of this a big scandal arose" - not "As a result of this comma a big scandal arose period". However, in formal-register English (in the news, for example), a difference is made between content created by the news team and content that should be attributed to someone else by explicitly pronouncing quotation marks. For example, the news transcript The fighter said: "I am here to try to win this." will be pronounced: "The fighter said, quote, I am here to try to win this. End of quote." In this case, different languages may have different rules, and you should follow the rules that are appropriate for your language. Signs and symbols. Signs and symbols need to be pronounced as they would be heard in a speech-only setting. Attention should be paid: (a) to potential number or gender agreement (for example, in English, "40%" should be read as "forty percent" — not "forty percents") (b) to potential differences between the place of the sign or symbol in writing and in speech (for example, in English, the "$" sign should be read as "dollar" and should be read after the number it precedes; i.e. "$22" should be read as "twenty-two dollars" — not "dollars twenty-two") (c) to the way the sign or symbol gets expanded in speech (for example, in English, "Platform 9 3/4" should be read "platform nine and three quarters" — not "platform nine
+
+three quarters"). Similarly,  $50\mathrm{km / h}$  would be pronounced "fifty kilometers per hour" — not "fifty kilometers hour"). Different languages may have different rules, and you should follow the rules that are appropriate for your language.
+
+Proper nouns and foreign expressions. Even the same language may have at least 2 different ways to pronounce foreign expressions of proper nouns: (a) one way is to try to approach the way they would sound in the foreign language from which they come (for example, in English, Louis in Louis XIV is pronounced "leewee" as it would be in French); (b) the other way is to pronounce them according to the rules of the adopting language (for example, in English, Louis in the City of St Louis is pronounced as in the English proper noun "Lewis")
+
+Abbreviations. Abbreviations should be expanded as much as possible. However, it is suggested to refrain from expanding them if their expansion results in unnatural speech. For example, in English, abbreviations such as Dr. or etc. are pronounced "doctor" and "et cetera", respectively (not "d r" nor "e t c"). However, abbreviations such as AM or PhD are pronounced as a sequence of letters without being expanded ("a m" and "p h d", respectively - not "ante meridiem" nor "philosophy doctorate"). Different languages may have different conventions, and you should follow the conventions that are appropriate for your language.
+
+# C Ablation study: Synthetic extension in speech evaluation datasets
+
+In this part of our work, we aim to analyze the feasibility of synthetically extending text benchmarks to speech using TTS systems, thereby creating multimodal datasets. Our goal is to understand if it would have been feasible to obtain the speech version of BELEBELE by using state of the art TTS systems, instead of human recordings.
+
+For this study we use FLEURS dataset, that contains ASR data in the same domain as BELEBELE. We chose to perform this study in the ASR task because it is simpler compared to other speech tasks, due to its monotonic alignment process and minimal need for reasoning. This ensures that the overall model performance and the complexity of the task are less likely to influence the results.
+
+For our experiments, we generate a synthetic copy of the FLEURS dataset using the MMS TTS (Pratap et al., 2024) system on the FLEURS tran
+
+scripts. Then, we benchmark state-of-the-art models (WHISPER, SEAMLESSM4T and MMS ASR) on both the original and synthetic datasets and analyze whether the conclusions remain consistent across both datasets.
+
+It is important to note that a decrease in system performance is expected when using synthetic data. However, if this decrease occurs proportionally across all models, the synthetic data could still be useful to benchmark models. Conversely, if the model performance ranking changes, we can conclude that synthetic data is not reliable when benchmarking models.
+
+To measure the variability in model rankings between the original and the synthetic data, we track the inversions that occur in the order of the models in the two settings. We define an inversion as a swap between two models that appear in adjacent positions on the list. We count how many swaps are needed in the ranking obtained using synthetic data to match the ranking from the original dataset.
+
+<table><tr><td rowspan="2"></td><td colspan="2">SEAMLESSM4T</td><td colspan="2">WHISPER</td><td colspan="2">MMS</td><td rowspan="2">Inv</td></tr><tr><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td><td>Hum</td><td>Syn</td></tr><tr><td>Bengali</td><td>14.1</td><td>21.1</td><td>114.7</td><td>105.8</td><td>14.6</td><td>25.0</td><td></td></tr><tr><td>Catalan</td><td>8.2</td><td>13.2</td><td>6.7</td><td>16.4</td><td>10.3</td><td>21.8</td><td>✓</td></tr><tr><td>Dutch</td><td>9.9</td><td>20.0</td><td>8.5</td><td>19.7</td><td>12.4</td><td>28.3</td><td></td></tr><tr><td>English</td><td>6.0</td><td>11.7</td><td>4.5</td><td>9.8</td><td>12.3</td><td>19.2</td><td></td></tr><tr><td>Finnish</td><td>20.1</td><td>20.8</td><td>12.5</td><td>18.9</td><td>13.1</td><td>18.4</td><td>✓</td></tr><tr><td>French</td><td>9.5</td><td>10.8</td><td>6.7</td><td>11.3</td><td>12.4</td><td>16.6</td><td>✓</td></tr><tr><td>German</td><td>8.5</td><td>13.9</td><td>5.2</td><td>12.3</td><td>10.5</td><td>20.8</td><td></td></tr><tr><td>Hindi</td><td>11.9</td><td>13.4</td><td>33.5</td><td>28.7</td><td>11.1</td><td>18.3</td><td>✓</td></tr><tr><td>Indonesian</td><td>12.1</td><td>12.8</td><td>8.7</td><td>14.2</td><td>13.2</td><td>21.9</td><td>✓</td></tr><tr><td>Korean</td><td>25.7</td><td>40.3</td><td>15.4</td><td>29.9</td><td>47.8</td><td>61.2</td><td></td></tr><tr><td>Polish</td><td>13.0</td><td>14.7</td><td>8.1</td><td>13.3</td><td>11.6</td><td>18.1</td><td>✓</td></tr><tr><td>Portuguese</td><td>9.0</td><td>8.0</td><td>4.1</td><td>6.9</td><td>8.7</td><td>10.4</td><td>✓</td></tr><tr><td>Romanian</td><td>12.6</td><td>11.7</td><td>13.5</td><td>25.4</td><td>12.0</td><td>15.4</td><td>✓</td></tr><tr><td>Russian</td><td>10.2</td><td>18.6</td><td>5.6</td><td>17.4</td><td>18.8</td><td>34.3</td><td></td></tr><tr><td>Spanish</td><td>6.3</td><td>9.1</td><td>3.4</td><td>10.0</td><td>6.4</td><td>10.8</td><td>✓</td></tr><tr><td>Swahili</td><td>19.5</td><td>19.0</td><td>64.2</td><td>58.4</td><td>14.2</td><td>19.0</td><td>✓</td></tr><tr><td>Swedish</td><td>15.4</td><td>20.1</td><td>11.3</td><td>19.1</td><td>21.0</td><td>27.8</td><td></td></tr><tr><td>Telugu</td><td>27.4</td><td>28.0</td><td>132.2</td><td>133.9</td><td>24.2</td><td>27.8</td><td></td></tr><tr><td>Thai</td><td>127.8</td><td>135.5</td><td>104.0</td><td>121.3</td><td>99.8</td><td>99.9</td><td></td></tr><tr><td>Turkish</td><td>18.6</td><td>23.0</td><td>8.4</td><td>16.5</td><td>19.2</td><td>30.3</td><td></td></tr><tr><td>Ukrainian</td><td>15.0</td><td>23.5</td><td>9.8</td><td>21.8</td><td>18.1</td><td>34.7</td><td></td></tr><tr><td>Vietnamese</td><td>16.0</td><td>20.1</td><td>10.2</td><td>14.2</td><td>25.8</td><td>25.3</td><td></td></tr></table>
+
+Table 4:  $\mathrm{{WER}}\left(  \downarrow  \right)$  results on the ASR task. Last column marks if the language has at least 1 inversion in ASR performance ranking comparing human vs TTS inputs.
+
+In Table 4 we see that in the ASR setting, conclusions regarding model performance can vary depending on whether human or synthetic data is used. Although these conclusions are specific to the evaluated tasks and datasets, we demonstrate that even with the outstanding performance of current TTS methods, this does not guarantee the reliability of the data they generate when it comes to evaluation purposes. This is true not only for low-resource languages, but also for high-resource languages such as French or Spanish. These findings show that speech benchmarks might not be reliable if synthetically generated even in widely researched areas, further supporting the creation of evaluation datasets by humans.

2025/2M-BELEBELE_ Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF/images.zip

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+        "text": "3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models",
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+        "type": "text",
+        "text": "Zhaoxi Zhang $^{1,2,4}$ , Sanwoo Lee $^{1,2}$ , Zhixiang Wang $^{1,3}$ , Yunfang Wu $^{1,2*}$",
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+        "text": "$^{1}$ National Key Laboratory for Multimedia Information Processing, Peking University  $^{2}$ School of Computer Science, Peking University, Beijing, China",
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+        "type": "text",
+        "text": "$^{3}$ School of Software and Microelectronics, Peking University, Beijing, China",
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+    {
+        "type": "text",
+        "text": "$^{4}$ School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China {1120210536}@bit.edu.cn, {sanwoo}@pku.edu.cn, {ekko}@stu.pku.edu.cn, {wuyf}@pku.edu.cn",
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+    {
+        "type": "text",
+        "text": "Abstract",
+        "text_level": 1,
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+        "text": "The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications. The code of this paper can be found at https://github.com/JesseZZZZZ/3DM.",
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+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Recent advances in MLLMs (Liu et al., 2023; Chen et al., 2024; GLM et al., 2024; Zhu et al., 2024a) have shown remarkable performance in various multimodal tasks, ranging from image captioning (Wang et al., 2024) and visual question answering (Li et al., 2023) to a nuanced multimodal sarcasm",
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+    {
+        "type": "table",
+        "img_path": "images/5ddc12dfce02b68f52922758015bdcd68506051fceea1e9f5a014ec642abe948.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Acc</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>LLaVA-v1.5-7b</td><td>0.516</td><td>0.469</td><td>0.947</td><td>0.628</td></tr><tr><td>ChatGLM4-9b</td><td>0.689</td><td>0.725</td><td>0.450</td><td>0.555</td></tr></table>",
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+        "text": "Table 1: The performance of LLaVA-v1.5-7b with a positive bias, and ChatGLM4-9b with a negative bias.",
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+        "type": "image",
+        "img_path": "images/3963e59728a96649323ff726db501c6f72f603a05a3b066bba93aa9f73192818.jpg",
+        "image_caption": [
+            "Figure 1: Conceptual comparison of model merging with fine-tuning and ensembling in the context of debi-aising. Model merging is training-free and benefits from efficient inference."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "detection (Tang et al., 2024). Despite the progress, MLLMs are prone to biased predictions (Cui et al., 2023; Han et al., 2024). For instance, Table 1 shows that LLaVA (Liu et al., 2023) favors classifying inputs as sarcastic (positive-biased model), whereas ChatGLM (GLM et al., 2024) has the opposite tendency (negative-biased model). This may be a symptom of hallucinating answers from spurious correlations seen in the dataset (Bai et al., 2024). MLLMs' inherent biases compromise their reliability and fairness for deployment in real-world applications. Thus, enhancing MLLMs' accuracy and ensuring minimal bias have significant practical implications.",
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+    {
+        "type": "text",
+        "text": "In this paper, we present the first attempt, to the best of our knowledge, at merging models (Yang et al., 2024; Ramé et al., 2023; Lin et al., 2024) to debias MLLMs and showcasing its general effectiveness. Existing debiasing or dehallucination methods have relied on labeled datasets for finetuning (Chen et al., 2021; Guo et al., 2022; Liu et al., 2024) or repetitive inference for ensembling predictions (Clark et al., 2019), both of which in",
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+    {
+        "type": "page_footnote",
+        "text": "* Corresponding author.",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "14049",
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+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: ACL 2025, pages 14049-14059 July 27 - August 1, 2025 ©2025 Association for Computational Linguistics",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Our approach collects a positive-biased model and a negative-biased model, then merges them in the parameter space without the need for additional training and repeated inference. Through this process, biases in opposite directions are canceled out efficiently. See Fig. 1 for the conceptual comparisons between merging and the traditional approaches.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "However, merging MLLMs for debiasing faces several challenges: (1) Merging models often requires the same architecture across models to allow for parameter-wise operations, a condition rarely satisfied in the rapidly evolving ecosystem of MLLMs (Zhang et al., 2024); (2) Reducing the bias alone does not always translate to improved accuracy—debiased models may struggle with task performance. This highlights the need to refine existing merging methods (Ilharco et al., 2022; Yadav et al., 2024; Yu et al., 2024) through the lens of reducing bias and enhancing accuracy.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "We propose 3DM (Distill, Dynamic Drop and Merge), an architecture-agnostic merging framework designed to address these challenges. First, knowledge distillation (Gou et al., 2021) bridges architectural gaps between models, enabling parameter-level merging even for heterogeneous MLLMs. Second, we introduce a dynamic dropping strategy that assigns parameter-specific drop rates based on their influence on bias and accuracy. This is motivated by a recent merging method—DARE (Yu et al., 2024)—that sparsifies parameters by a uniform chance of dropout and treats all parameters equally.",
+        "bbox": [
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+        "type": "text",
+        "text": "We first conduct experiments on the MMSD2.0 (Qin et al., 2023) sarcasm detection dataset and measure models' bias with our newly proposed metric, Bias Ratio (Sec. 3). The results demonstrate that (1) merging methods are in common effective in reducing bias, and that (2) 3DM significantly outperforms DARE and other baselines in accuracy, F1-score, and Bias Ratio. In addition, experiments on MMSD1.0 (Cai et al., 2019) further validate that 3DM generalizes well across different datasets. Compared with methods requiring hyperparameter search over the validation data, 3DM does not contain such hyperparameters, making it convenient for implementation.",
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+    {
+        "type": "text",
+        "text": "In essence, our contributions are as follows:",
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+    {
+        "type": "text",
+        "text": "1. Architecture Alignment: A distillation pipeline that aligns MLLM architectures, preserving their original bias and accuracy.",
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+        "list_items": [
+            "2. Dynamic Dropping: A merging strategy that adaptively adjusts drop rates to reduce biases and improve accuracy.",
+            "3. Bias Ratio: A metric for quantifying bias direction and magnitude, contributing to ongoing efforts in bias quantification.",
+            "4. Empirical Validation: Extensive experiments demonstrating 3DM's effectiveness in terms of both debiasing and accuracy enhancement."
+        ],
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+    {
+        "type": "text",
+        "text": "2 Related Work",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "2.1 Model Debiasing",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Existing debiasing mechanisms in the literature can be classified into two primary categories (Mehrabi et al., 2021; Pessach and Shmueli, 2022): training-based debiasing and training-free debiasing. Training-based debiasing approaches necessitate modifications to the training dataset (Li and Vasconcelos, 2019), demonstrating notable effectiveness while requiring extensively annotated training data. Conversely, training-free debiasing methodologies primarily focus on altering the output distribution (Kamiran et al., 2012), with assembling emerging as a crucial technique in this domain (Clark et al., 2019).",
+        "bbox": [
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+        "page_idx": 1
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+    {
+        "type": "text",
+        "text": "A notable example of ensembling is the blindfolding strategy proposed by Zhu et al. (2024b), which involves masking specific portions of the input and computing the final output score as the difference between traditional inference, fully blindfolded inference, and partially blindfolded inference. Although ensembling methods eliminate the need for training processes, they incur substantial computational overhead due to the requirement for multiple inference operations. In light of these considerations, we propose our merging strategy as an effective compromise between these two approaches, offering the dual advantages of eliminating excessive inference requirements while maintaining a label-free training process.",
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+    {
+        "type": "text",
+        "text": "2.2 Model Merging",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Garipov et al. (2018); Draxler et al. (2018) demonstrated that two models trained from different initializations can be connected by a path of nonincreasing loss in the loss landscape, referred to as model connectivity. If the two models share a significant part of the optimization trajectory (e.g., pre-trained model), they are often connected by a",
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+        "page_idx": 1
+    },
+    {
+        "type": "page_number",
+        "text": "14050",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "linear path (Frankle et al., 2020; Neyshabur et al., 2020; Mirzadeh et al., 2021), where interpolating along the path potentially leads to better accuracy and generalization (Izmailov et al., 2018). This property has been exploited as simply averaging the weights of numerous models fine-tuned from different hyperparameters to improve accuracy (Wortsman et al., 2022), popularizing model merging as an efficient alternative to ensemble in combining models without additional instruction tuning.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "The success of averaging fine-tuned models has led to a surge of merging methods, aimed at steering models' behavior in desired way. A prominent example is multi-task learning via merging, where accounting for parameter importance (Matena and Raffel, 2022) and minimizing prediction differences to the fine-tuned models (Jin et al., 2022) are shown to be effective. While these methods relies on statistics that are expensive to compute, Task Arithmetic (Ilharco et al., 2022) presents a cost-effective and scalable method of adding the weighted average of task vectors (i.e., fine-tuned part of parameters) to the pre-trained model. Subsequent studies are dedicated to pre-processing task vectors to reduce interference across models (Yadav et al., 2024; Yu et al., 2024; Deep et al., 2024). Moreover, distillation is proposed for architecture alignment by FUSECHAT (Wan et al., 2024). Our distill-merge pipeline and dynamic dropping strategy aligns with this line of research, however we are focused on editing task vectors to reduce bias and improve accuracy.",
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+        "type": "text",
+        "text": "3 Bias Ratio",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "The metrics used to evaluate a model's bias (or fairness) remain a subject of ongoing dialogue, with no clear consensus yet (Caton and Haas, 2024). Previous studies have employed various evaluation metrics to assess bias. In this work, we introduce the Bias Ratio (BR) as a measure of a model's bias, which is based on the quantities of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN).",
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+    {
+        "type": "equation",
+        "text": "\n$$\nB R = \\frac {F P}{F P + T N} - \\frac {F N}{F N + T P} \\tag {1}\n$$\n",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "The Bias Ratio (BR) ranges from  $-1$  to  $1$ , where its absolute value indicates the magnitude of bias, and its sign denotes the direction. For instance, a BR value of 0.8 reflects a relatively high degree of positive bias, whereas a BR of  $-0.1$  suggests a relatively low degree of negative bias. While",
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "previous studies have primarily conducted qualitative analyses of bias based on TP, TN, FP, and FN, we propose a quantitative metric to systematically assess both the degree and direction of bias.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "4 Method",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Focusing on a two-way classification task (e.g., sarcasm detection), suppose we are given two MLLMs, a positive-biased model and a negative-biased model: A positive-biased model tends to classify an input as positive sample, represented by high recall and low precision (Table 1). Likewise, a negative-biased model is inclined to classify an input as negative sample, represented by low recall and high precision (Table 1).",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Then we apply our proposed 3DM framework following three steps, as illustrated in Fig. 2: (1) knowledge distillation for architecture alignment; (2) dynamic dropping strategy that filters out delta parameters based on the contribution to accuracy and bias; (3) merging the positive-biased delta parameters and negative-biased delta parameters to cancel out predictive bias.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "4.1 Architecture Alignment via Distillation",
+        "text_level": 1,
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "An intuitive way to mitigate bias is to merge a positive-biased model and a negative-biased model to cancel out the bias. However, the diverse ecosystem of MLLMs makes it challenging to guarantee those two models to share the same architecture, blocking them from being merged through parameter-wise operations. Knowledge distillation provides a viable solution by reshaping the two models into the same architecture, while preserving the predictive accuracy and bias of each model. Hence we start by distilling the two types of models and proceed to model merging (Sec. 4.2, 4.3) on the basis of compatible architecture.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Knowledge distillation (Gou et al., 2021) typically follows a teacher-student structure, where the teacher model's output (generated by the prompt proposed in Sec. 5.1.2) supervises the student model such that the student model inherits the behavior of the teacher model. Note that the student model is not required to be smaller than the teacher model in our case, as our goal of knowledge distillation does not lie in compression.",
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Specially, we fine-tune the pre-trained model using pseudo labels generated by a teacher model (i.e., either positive-biased model or negative-biased model). We minimize cross-entropy loss evaluated",
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "14051",
+        "bbox": [
+            477,
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+            522,
+            940
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "image",
+        "img_path": "images/2694ac8aad3d7ee53af74cf3df96215755daa62e5cb4dd3f0659367959d9a1f4.jpg",
+        "image_caption": [
+            "Figure 2: Overview of 3DM framework. First, positive-biased model and negative-biased model are distilled to a base student model to share an identical architecture. Second, dynamic dropping assigns a drop rate to each delta parameter based on the discrepancy between the positive-biased model and the negative-biased model. Then, sparse task vectors after dropping are added to the base model to build a debiased model."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            122,
+            86,
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+            312
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "on the pseudo labels:",
+        "bbox": [
+            112,
+            404,
+            273,
+            420
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {L} _ {c e} = - \\sum_ {t = 1} ^ {m} \\log P \\left(\\hat {y} _ {t} \\mid x, \\hat {y} _ {<   t}\\right) \\tag {2}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            183,
+            432,
+            487,
+            472
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $\\{\\hat{y}_i\\}_{i=1}^m$  is the pseudo label of length  $m$  generated by teacher model. In the context of sarcasm detection,  $x$  is a pair of input text and image and  $\\{\\hat{y}_i\\}_{i=1}^m$  is an answer sequence indicating whether the input pair contains sarcasm.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "4.2 Dynamic Dropping",
+        "text_level": 1,
+        "bbox": [
+            112,
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+            593
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Merging a positive-biased model and a negative-biased model is in general effective in alleviating the bias. In this section, we further propose dynamic dropping, aiming to improve accuracy and F1-score while simultaneously reducing bias.",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "In model merging, delta parameters are defined as the subtraction of parameters of base model from the fine-tuned model, and they can be understood as task vectors (Ilharco et al., 2022). Findings by Yu et al. (2024) suggest that one could randomly zero-out delta parameters of an LLM with a drop rate of  $p$  and re-scale the remaining ones by  $1 / (1 - p)$  without impacting the model's performance. This sparsification strategy—coined as DARE—has been shown to be helpful in reducing parameter interference among the models to be merged. However, DARE assigns the same drop rate for all delta parameters.",
+        "bbox": [
+            112,
+            678,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Conversely, the drop rate of a delta parameter should ideally be determined by its contribution",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "to improving accuracy and reducing bias. That is, \"important\" delta parameters should be preserved by a higher probability.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Delta Parameters We merge the distilled positive-biased model and negative-biased model by editing their respective delta parameters and combining those to the base student model. Delta parameters are defined as:",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nd _ {i j} ^ {P} = W _ {i j} ^ {P} - W _ {i j} ^ {\\text {b a s e}} \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            616,
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nd _ {i j} ^ {N} = W _ {i j} ^ {N} - W _ {i j} ^ {\\text {b a s e}} \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            616,
+            583,
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+            605
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $W^{base} \\in \\mathbb{R}^{m \\times n}$  is a parameter matrix of the base model and  $W^{P}$  and  $W^{N}$  are the ones distilled from positive-biased model and negative-biased model, respectively.  $i$  and  $j$  denotes position  $(i,j)$  of the parameter in  $W$ .",
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Classification of Delta Parameters. In terms of which delta parameters are more responsible for boosting model's accuracy and suppressing bias, we suggest the following criteria for classifying delta parameters into three categories:",
+        "bbox": [
+            507,
+            701,
+            884,
+            782
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1. Bias-free Delta (Fig. 3(a)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the same sign, i.e.  $d_{ij}^{P}d_{ij}^{N} > 0$ .",
+            "2. Unidirectional Delta (Fig. 3(b)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the opposite sign, and the magnitude of one dominates the magnitude of the other, i.e.  $d_{ij}^{P}d_{ij}^{N} < 0$  and  $|d_{ij}^{P} + d_{ij}^{N}| > c$  where  $c$  is a threshold."
+        ],
+        "bbox": [
+            522,
+            790,
+            882,
+            919
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "14052",
+        "bbox": [
+            477,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/b93e841a60b7e67b2fded2b2ad89cd1eb4055c16cbd8a6a716e28a6fed1952d2.jpg",
+        "image_caption": [
+            "(a)"
+        ],
+        "image_footnote": [],
+        "bbox": [
+            159,
+            86,
+            230,
+            149
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/e506c9ebf32a42ce2bd94e860e2d85adc0f62d204f97541d86bbc517bc8b0c46.jpg",
+        "image_caption": [
+            "(b)"
+        ],
+        "image_footnote": [],
+        "bbox": [
+            263,
+            87,
+            337,
+            162
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/109791fa0a075079673853b6033687fcbd6a395cb56ed47edaffe44a85d45342.jpg",
+        "image_caption": [
+            "(c)",
+            "Figure 3: Configurations of delta parameters under different conditions. The delta parameter from the positive-biased model (blue) and the negative-biased model (pink) can exhibit (a) the same sign, (b) opposite signs with a large magnitude difference, or (c) opposite signs with comparable magnitudes (dashed)."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            371,
+            86,
+            443,
+            177
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "3. Bidirectional Delta (Fig. 3(c)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the opposite signs, and the magnitudes of both are comparable, i.e.  $d_{ij}^{P}d_{ij}^{N} < 0$  and  $|d_{ij}^{P} + d_{ij}^{N}| < c$ .",
+        "bbox": [
+            127,
+            322,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "The above criteria follows from our hypothesis about the roles of delta parameters: (1) Delta parameters with the same sign indicates a consistent direction in parameter updates by the positive-biased model and negative-biased model, potentially implying salient deltas that are associated with accuracy; (2) Given that positive-biased model and negative-biased model are best distinguished by their bias, those delta parameters with the opposite sign have greater contribution to bias, in which bidirectional delta may lead to severer interference while merging than unidirectional delta.",
+        "bbox": [
+            112,
+            401,
+            489,
+            594
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Towards Adaptive Drop Rate via Dynamic Dropping. Our classification of delta parameters motivates us to assign increasing drop rates for bias-free delta, unidirectional delta, and bidirectional delta. In light of this, we present dynamic dropping, a strategy of applying adaptive drop rate  $p_{ij}$  at a parameter-level:",
+        "bbox": [
+            112,
+            602,
+            489,
+            715
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\np _ {i j} = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} \\geq 0 \\\\ 1 - \\frac {\\left| d _ {i j} ^ {P} + d _ {i j} ^ {N} \\right|}{\\left| d _ {i j} ^ {P} \\right| + \\left| d _ {i j} ^ {N} \\right|} & \\text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} <   0 \\end{array} \\right. \\tag {5}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            157,
+            736,
+            487,
+            785
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Here,  $p_{ij}$  is the drop rate between 0 and 1. Intuitively, Eq. 5 excludes bias-free delta from dropout operation, and for  $d_{ij}^{P}d_{ij}^{N} < 0$ , Eq. 5 imposes higher drop rate on bidirectional delta than on unidirectional delta. Noted, we implement a synchronized dropping mechanism where delta parameters at the same position are either dropped or retained simultaneously.",
+        "bbox": [
+            112,
+            791,
+            489,
+            921
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "table",
+        "img_path": "images/69a77ae98082e489f67382000487ed340cbbf33855ab116ca48a62de01894dc4.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>MMSD1.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>8642</td><td>11174</td></tr><tr><td>Validation</td><td>2410</td><td>959</td><td>1451</td></tr><tr><td>Test</td><td>2409</td><td>959</td><td>1450</td></tr></table>",
+        "bbox": [
+            547,
+            80,
+            843,
+            146
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "table",
+        "img_path": "images/ccde7d358f92a75a61cfec20f55d4cecf9c60d50c871704cac5c8b1c1ba5f383.jpg",
+        "table_caption": [
+            "Table 2: Composition of MMSD1.0 dataset."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>MMSD2.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>9572</td><td>10240</td></tr><tr><td>Validation</td><td>2410</td><td>1042</td><td>1368</td></tr><tr><td>Test</td><td>2409</td><td>1037</td><td>1372</td></tr></table>",
+        "bbox": [
+            549,
+            183,
+            843,
+            248
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Table 3: Composition of MMSD2.0 dataset.",
+        "bbox": [
+            544,
+            256,
+            843,
+            272
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "After dynamic dropping, each remaining delta parameter is rescaled by  $1 / (1 - p_{ij})$  to preserve the expectation of input embeddings, as elaborated in Yu et al. (2024).",
+        "bbox": [
+            507,
+            299,
+            880,
+            362
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.3 Parameter Merging",
+        "text_level": 1,
+        "bbox": [
+            507,
+            375,
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+            390
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Let the delta parameters after dynamic dropping and re-scaling be  $\\hat{d}_{ij}^{P}$  and  $\\hat{d}_{ij}^{N}$ . Then the average of  $\\hat{d}_{ij}^{P}$  and  $\\hat{d}_{ij}^{N}$  is added to the base model parameter to derive the merged parameter  $W_{ij}^{*}$ :",
+        "bbox": [
+            507,
+            395,
+            882,
+            464
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nW _ {i j} ^ {*} = 0. 5 \\hat {d} _ {i j} ^ {P} + 0. 5 \\hat {d} _ {i j} ^ {N} + W _ {i j} ^ {\\text {b a s e}} \\tag {6}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            573,
+            478,
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+            500
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "$W^{*}$  is the final model weights of our 3DM method, where bias is reduced and the overall performance is boosted.",
+        "bbox": [
+            507,
+            510,
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+            557
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "5 Experiments",
+        "text_level": 1,
+        "bbox": [
+            507,
+            571,
+            655,
+            588
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "In this section, we first introduce the experimental setup, including the datasets, prompts, base models, and baselines. Then, we design experiments to validate our method. Distillation (5.2), merging (5.3), ensembling (5.5), and generalizability (5.6) are analyzed in this section.",
+        "bbox": [
+            507,
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+            694
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "5.1 Experimental Setup",
+        "text_level": 1,
+        "bbox": [
+            507,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "5.1.1 Dataset",
+        "text_level": 1,
+        "bbox": [
+            507,
+            727,
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+            740
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "We validate our approach on MMSD2.0 (Qin et al., 2023), a multi-modal sarcasm detection dataset whose test set contains 2409 sentences along with images, and we test the generalizability on MMSD1.0 (Cai et al., 2019). See Table 3 for dataset statistics.",
+        "bbox": [
+            507,
+            746,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "5.1.2 Implementation Details",
+        "text_level": 1,
+        "bbox": [
+            507,
+            853,
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+            868
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Prompt Template. We use a fixed template to format the prompt. The template is carefully designed to ensure consistency across all samples",
+        "bbox": [
+            507,
+            873,
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+            921
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "14053",
+        "bbox": [
+            477,
+            927,
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+            940
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/b7b7a36d002d95b163640f871d6f1861398247304ec2a4a9413c1d7a94d163f9.jpg",
+        "image_caption": [
+            "Figure 4: Visualization of the percentage of different types of parameters we encounter when merging. Blue and red represent the sign of  $d_{ij}^{P}$  and  $d_{ij}^{N}$  being same and different. The  $y$  axis represents the last  $y$  layers in the model."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            117,
+            84,
+            482,
+            235
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "and to minimize any potential bias introduced by expression. The following prompt template is used:",
+        "bbox": [
+            112,
+            346,
+            487,
+            378
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "\"This is an image with: \" as the caption. Is the image-text pair sarcastic?First answer the question with yes or no, then explain your reasons.\"",
+        "bbox": [
+            112,
+            378,
+            487,
+            441
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Knowledge Distillation. To examine the validity of knowledge distillation in transferring both accuracy and bias from the teacher models, we choose LLaVA-1.5-7b (Liu et al., 2023) and InternVL-2.5-8b (Chen et al., 2024) as student models (base models), and select LLaVA-1.5-7b and ChatGLM4-9b (GLM et al., 2024) as teacher models. Our choices of small-sized MLLMs are intended to show that 3DM does not necessitate any pre-existing sarcasm detection capabilities in the student models.",
+        "bbox": [
+            112,
+            453,
+            489,
+            613
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Dynamic Dropping. To assess the effectiveness of dynamic dropping, we fix InternVL as the base model and obtain positive and negative delta parameters distilled from LLaVA and ChatGLM, respectively. Choosing InternVL is informed by empirical observations (See Table 4), indicating that the pretrained InternVL exhibits weak bias  $(BR = 0.185)$  relatively, and has no pre-existing knowledge of sarcasm detection  $(Acc \\approx 0.5)$ , making it an appropriate candidate for our experiment.",
+        "bbox": [
+            112,
+            624,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Hyperparameter Searching The fixed drop rate of DARE and our ablation study (unused in 3DM) is set to 0.7, which is the result of tuning on the validation set of MMSD2.0 (Table 8).",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "5.1.3 Baselines",
+        "text_level": 1,
+        "bbox": [
+            112,
+            869,
+            247,
+            883
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "We compare 3DM with merging baselines including Average Merging (Wortsman et al., 2022),",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "TIES (Yadav et al., 2024) and DARE (Yu et al., 2024), in addition to ensembling. TIES merges models by drop-elect-merge operations, where the \"drop\" step mitigates interference by removing redundant delta parameters based on their magnitudes.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "5.2 Distillation Experiments",
+        "text_level": 1,
+        "bbox": [
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+            192,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Table 4 and Table 5 present the performance and bias of both teacher models and student models after distillation. As observed, the student models effectively inherit the bias direction of their respective teacher models, while also achieving improved performance, except for the F1-score of LLaVA base model distilled from ChatGLM4. These results demonstrate that we can successfully prepare models for merging-based debiasing through distillation, without the need for elaborate training labels.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "For the subsequent merging experiments, we apply our proposed distill-merge pipeline for debiasing when LLaVA serves as the student model. For InternVL as the student model, we further compare our proposed dynamic dropping method with baseline merging approaches, as InternVL itself exhibits a weak bias and can therefore be used as the base model.",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "5.3 Merging Experiments",
+        "text_level": 1,
+        "bbox": [
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+            530,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "This section analyzes the results on the testing set of MMSD2.0 (Qin et al., 2023).",
+        "bbox": [
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+            551,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "For the LLaVA base model, we compare the performance of merged models against their original counterparts. As shown in Table 5, the average merging strategy fails to surpass the negative-biased model. However, applying DARE (fixed dropping) leads to significant improvements, with both accuracy and F1-score approximating those of the zero-shot inference of teacher models, alongside a substantial reduction in the absolute value of BR. This highlights the potential of our distill- merge pipeline for debiasing tasks when combined with a well-designed merging method.",
+        "bbox": [
+            507,
+            583,
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Similarly, Table 4 implies that all merging strategies significantly reduce the absolute value of BR, compared to student base models distilled from biased models into InternVL, further demonstrating the effectiveness of our distill-merge pipeline. Moreover, 3DM, which introduces a tailored dropping mechanism in the \"merge\" phase, achieves state-of-the-art performance in accuracy, F1-score and BR across all merging approaches. This under",
+        "bbox": [
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+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "14054",
+        "bbox": [
+            477,
+            927,
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+            940
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "table",
+        "img_path": "images/0d2cc8f5dcc973d3c500ff092b355eded8cca65f1b00922d2bf2c371955c55ce.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.689</td><td>0.555</td><td>466</td><td>177</td><td>1195</td><td>571</td><td>-</td><td>-0.422</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.499</td><td>0.509</td><td>625</td><td>796</td><td>576</td><td>412</td><td>weak</td><td>0.183</td></tr><tr><td rowspan=\"7\">InternVL-2.5-8b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.543</td><td>0.629</td><td>934</td><td>998</td><td>374</td><td>103</td><td>+</td><td>0.628</td></tr><tr><td>negative learning</td><td>0.644</td><td>0.428</td><td>321</td><td>141</td><td>1231</td><td>716</td><td>-</td><td>-0.588</td></tr><tr><td rowspan=\"4\">Merging</td><td>average merging</td><td>0.688</td><td>0.614</td><td>599</td><td>314</td><td>1058</td><td>438</td><td>weak</td><td>-0.194</td></tr><tr><td>TIES</td><td>0.648</td><td>0.484</td><td>397</td><td>208</td><td>1164</td><td>640</td><td>-</td><td>-0.466</td></tr><tr><td>DARE</td><td>0.684</td><td>0.609</td><td>592</td><td>316</td><td>1056</td><td>445</td><td>weak</td><td>-0.199</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.663</td><td>0.516</td><td>431</td><td>205</td><td>1159</td><td>605</td><td>-</td><td>-0.434</td></tr></table>",
+        "bbox": [
+            114,
+            80,
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+            250
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/405c3203691937cc9021f61bd4f1b0b92c59b6ccfd99b4789f3553b53ca28cb0.jpg",
+        "table_caption": [
+            "Table 4: Results of applying multiple debiasing methods, including average merging, fixed dropping (DARE), our proposed 3DM and ensembling methods. \"+\" and \"-\" indicate that the model tends to give positive and negative answers."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"5\">LLaVA-v1.5-7b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>negative learning</td><td>0.710</td><td>0.666</td><td>572</td><td>233</td><td>1139</td><td>465</td><td>-</td><td>-0.279</td></tr><tr><td rowspan=\"2\">Merging</td><td>average merging</td><td>0.671</td><td>0.474</td><td>357</td><td>113</td><td>1259</td><td>680</td><td>-</td><td>-0.573</td></tr><tr><td>DARE</td><td>0.714</td><td>0.649</td><td>617</td><td>290</td><td>1082</td><td>400</td><td>weak</td><td>-0.189</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.716</td><td>0.693</td><td>773</td><td>421</td><td>951</td><td>264</td><td>weak</td><td>0.05</td></tr></table>",
+        "bbox": [
+            114,
+            303,
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+            397
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/26a23594f718a549d515d25a7ba0907a7bd57d517ab1cffdb38dfd9f5672eb74.jpg",
+        "table_caption": [
+            "Table 5: Results of applying debiasing methods on LLaVA-based models. Because LLaVA itself has a positive bias, we apply the original model to the \"positive learning\" row."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Ablation type</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"4\">InternVL-2.5-8b</td><td>Bias-free</td><td>0.663</td><td>0.661</td><td>790</td><td>564</td><td>808</td><td>247</td><td>weak</td><td>0.173</td></tr><tr><td>Uni. &amp; Bi.</td><td>0.649</td><td>0.477</td><td>386</td><td>195</td><td>1177</td><td>651</td><td>-</td><td>-0.486</td></tr><tr><td>Async.</td><td>0.691</td><td>0.637</td><td>654</td><td>362</td><td>1010</td><td>383</td><td>weak</td><td>-0.105</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr></table>",
+        "bbox": [
+            114,
+            448,
+            878,
+            529
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Table 6: Ablation study. In 3DM, we classify each position into two categories based on their signs. In this part, we remove one of them, and test the method's performance. We also examine the performance of the synchronized dropping mechanism.",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "scores the effectiveness and superiority of dynamic dropping.",
+        "bbox": [
+            112,
+            607,
+            485,
+            639
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "We provide insights into why 3DM outperforms other merging approaches. While TIES mitigates interference between delta parameters through sign selection, it struggles in cases like Fig. 3(c), where it may remain on the wrong side. DARE, on the other hand, applies a uniform drop rate to all delta parameters, disregarding their distinct roles. However, as illustrated in Fig. 4, the proportion of bias-free delta parameters (blue) is comparable to that of unidirectional and bidirectional delta parameters (red), highlighting the necessity of dynamically assigning drop rates based on their roles (Sec. 4.2) and merging conditions (Fig. 3).",
+        "bbox": [
+            112,
+            640,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "5.4 Ablation Study",
+        "text_level": 1,
+        "bbox": [
+            112,
+            865,
+            280,
+            881
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "To better understand the role of dynamic dropping in 3DM, we conduct an ablation study by modify-",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "ing key components of the mechanism.",
+        "bbox": [
+            507,
+            607,
+            800,
+            621
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "As shown in Table 6, Bias-free, which replaces the adaptive drop rates of unidirectional and bidirectional deltas in Eq. 5 with a fixed rate, results in lower accuracy, along with a higher absolute value of BR. This suggests that a fixed drop rate fails to effectively leverage the variations in  $d_{ij}^{P}$  and  $d_{ij}^{N}$ . Similarly, Uni. & Bi., which follows DARE by applying a fixed drop rate to bias-free deltas instead of fully preserving them, also performs suboptimally compared to 3DM.",
+        "bbox": [
+            505,
+            624,
+            882,
+            784
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Additionally, we evaluate a less aggressive strategy than 3DM (synchronized dropping), called Async., which drops delta parameters individually based on Eq. 5. This reduces the likelihood of simultaneously eliminating delta parameters<sup>1</sup> in the scenario shown in Fig. 3(c). While this approach",
+        "bbox": [
+            507,
+            785,
+            884,
+            883
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_footnote",
+        "text": "1The final parameter at that position defaults to the base model's.",
+        "bbox": [
+            507,
+            894,
+            882,
+            919
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "14055",
+        "bbox": [
+            477,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/6febb6f5e46d2f1bed03decbfc09cabca4b93242cdefd92b9cfb056c0c0e18ba.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.445</td><td>0.587</td><td>952</td><td>1331</td><td>119</td><td>7</td><td>+</td><td>0.911</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.713</td><td>0.587</td><td>492</td><td>225</td><td>1225</td><td>467</td><td>-</td><td>-0.332</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.483</td><td>0.473</td><td>559</td><td>846</td><td>604</td><td>400</td><td>weak</td><td>0.166</td></tr><tr><td rowspan=\"7\">InternVL-2.5-8b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.501</td><td>0.592</td><td>871</td><td>1113</td><td>337</td><td>88</td><td>+</td><td>0.676</td></tr><tr><td>negative learning</td><td>0.667</td><td>0.466</td><td>350</td><td>193</td><td>1257</td><td>609</td><td>-</td><td>-0.502</td></tr><tr><td rowspan=\"4\">Merging</td><td>average merging</td><td>0.691</td><td>0.619</td><td>605</td><td>390</td><td>1060</td><td>354</td><td>weak</td><td>-0.100</td></tr><tr><td>TIES</td><td>0.676</td><td>0.519</td><td>422</td><td>244</td><td>1206</td><td>537</td><td>-</td><td>-0.392</td></tr><tr><td>DARE</td><td>0.686</td><td>0.613</td><td>600</td><td>397</td><td>1053</td><td>359</td><td>weak</td><td>-0.101</td></tr><tr><td>3DM</td><td>0.691</td><td>0.636</td><td>651</td><td>436</td><td>1014</td><td>308</td><td>weak</td><td>-0.020</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.680</td><td>0.530</td><td>433</td><td>241</td><td>1200</td><td>526</td><td>-</td><td>-0.381</td></tr></table>",
+        "bbox": [
+            115,
+            80,
+            880,
+            252
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Table 7: Performance of methods on MMSD1.0 dataset.",
+        "bbox": [
+            305,
+            260,
+            687,
+            274
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "achieves a slightly lower BR, it suffers a small drop in accuracy and F1-score. This could be due to it tends to retain a delta parameter in a single wrong direction, thus degenerating into TIES. This reinforces the effectiveness of the synchronized dropping mechanism, which not only preserves flexibility in handling unidirectional deltas but also forces the dropping of delta parameters in the bidirectional delta condition, where they may introduce greater bias or interference.",
+        "bbox": [
+            112,
+            300,
+            489,
+            461
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "5.5 Comparison with Ensemble",
+        "text_level": 1,
+        "bbox": [
+            112,
+            475,
+            378,
+            491
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "We conduct a systematic comparison between our 3DM method and ensemble approaches. For sarcasm detection, ensemble methods generate individual probability distributions and aggregate them for final predictions. While achieving acceptable performance, these methods incur substantial computational overhead, with inference costs scaling as  $O(n)$ , compared to  $O(1)$  for merging methods. This establishes a fundamental advantage for merging approaches.",
+        "bbox": [
+            112,
+            498,
+            489,
+            657
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "In our experiments, we implement basic averaging ensemble, where model distributions are arithmetically averaged. As shown in Table 4, Table 5, and Table 7, this approach demonstrates limited effectiveness on the testing dataset. Although more sophisticated ensemble techniques might surpass 3DM's performance, they cannot overcome the inherent computational limitations of all ensemble methods, which remain a fundamental constraint compared to merging approaches.",
+        "bbox": [
+            112,
+            659,
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+            820
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "5.6 Generalizability Analysis",
+        "text_level": 1,
+        "bbox": [
+            112,
+            834,
+            359,
+            850
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "In order to test the generalizability of our method, we validate our method on the testing set of MMSD1.0 (Cai et al., 2019). We retain the checkpoints in Sec. 5.2, and apply average merging,",
+        "bbox": [
+            112,
+            857,
+            489,
+            921
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "TIES, DARE and 3DM in exactly the same way as Sec. 5.3, but on the MMSD1.0 dataset. Table 7 presents the results of multiple methods, where 3DM exhibits the highest accuracy, the highest F1-score, and the lowest absolute value of BR. Moreover, all merging-based methods reduce the absolute value of BR. The results in Table 7 imply comparable tendency with Table 4, demonstrating the advanced generalizability of 3DM.",
+        "bbox": [
+            507,
+            300,
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+            445
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "5.7 Hyperparameter Tuning for DARE",
+        "text_level": 1,
+        "bbox": [
+            507,
+            457,
+            833,
+            473
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "We search the hyperparameter on the validation set of MMSD2.0, and report the result in Table 8. Based on the result, we select 0.7 as the drop rate for DARE in our experiment.",
+        "bbox": [
+            507,
+            479,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
+        "bbox": [
+            507,
+            556,
+            640,
+            571
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "In this study, we present a comprehensive analysis of biases in MLLMs, empirically demonstrating that the majority of existing MLLMs exhibit significant biases in sarcasm detection tasks, with varying directional tendencies. Our work represents the first systematic effort to develop an architecture-agnostic merging framework specifically designed to address and mitigate biases in models with divergent bias orientations, particularly in debiasing tasks.",
+        "bbox": [
+            507,
+            583,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "The core contributions of our research include: (1) a generalized distill-merge pipeline applicable to both black-box and white-box MLLMs, and (2) a novel dynamic dropping mechanism that assigns individualized drop rates to delta parameters based on each parameter's functional role in the model. Notably, our distill-merge pipeline serves as a general, plug-and-play component that can be seamlessly integrated into various merging methodologies.",
+        "bbox": [
+            507,
+            744,
+            884,
+            888
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "This research establishes a new paradigm for bias mitigation in MLLMs through advanced merge",
+        "bbox": [
+            507,
+            889,
+            882,
+            921
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "14056",
+        "bbox": [
+            477,
+            928,
+            524,
+            940
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "table",
+        "img_path": "images/7f73b555dcff0080bead19e7e557aa89bc2723b0ca5ce0706674459058e34538.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Hyperparameter</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"4\">DARE</td><td>drop rate = 0.1</td><td>0.676</td><td>0.592</td><td>566</td><td>304</td><td>1064</td><td>476</td><td>weak</td><td>-0.235</td></tr><tr><td>drop rate = 0.3</td><td>0.682</td><td>0.588</td><td>547</td><td>272</td><td>1096</td><td>495</td><td>weak</td><td>-0.276</td></tr><tr><td>drop rate = 0.5</td><td>0.686</td><td>0.610</td><td>591</td><td>306</td><td>1062</td><td>451</td><td>weak</td><td>-0.209</td></tr><tr><td>drop rate = 0.7</td><td>0.694</td><td>0.613</td><td>584</td><td>279</td><td>1089</td><td>458</td><td>weak</td><td>-0.236</td></tr></table>",
+        "bbox": [
+            154,
+            80,
+            840,
+            152
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Table 8: Hyperparameter sensitivity of DARE on the validation set of MMSD2.0",
+        "bbox": [
+            223,
+            161,
+            771,
+            175
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "ing techniques, while simultaneously introducing a parameter-specific analytical framework for understanding and utilizing delta parameters. We anticipate that our findings will stimulate further research in this emerging area of MLLM optimization and bias reduction.",
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+    {
+        "type": "text",
+        "text": "Limitations",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "In this study, we introduce a distill-merge pipeline designed for architectural alignment, alongside a dynamic merging mechanism that assigns a unique drop rate for each delta parameter. Nonetheless, the current implementation of assigning drop rates overlooks the intricate interplay of synergistic and antagonistic interactions among multiple delta parameters, which could potentially influence the optimization process and outcomes. For instance, several delta parameters altogether contributes to biases, while any one of them individually can not. This limitation suggests a fertile ground for future research to explore and integrate these complex parameter interactions, thereby refining the mechanism's efficacy and robustness.",
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+    {
+        "type": "text",
+        "text": "Acknowledgments",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "This work is supported by the National Natural Science Foundation of China (62076008) and the Key Project of Natural Science Foundation of China (61936012).",
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+    {
+        "type": "text",
+        "text": "References",
+        "text_level": 1,
+        "bbox": [
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+    {
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2025/3DM_ Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models/0429ce61-4caf-4027-bc76-a381459f26e5_model.json

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+            "content": "3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models"
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+            "content": "Zhaoxi Zhang\\(^{1,2,4}\\), Sanwoo Lee\\(^{1,2}\\), Zhixiang Wang\\(^{1,3}\\), Yunfang Wu\\(^{1,2*}\\)"
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+            "content": "\\(^{1}\\)National Key Laboratory for Multimedia Information Processing, Peking University \\(^{2}\\)School of Computer Science, Peking University, Beijing, China"
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+            "content": "\\(^{3}\\)School of Software and Microelectronics, Peking University, Beijing, China"
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+            "content": "\\(^{4}\\)School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China {1120210536}@bit.edu.cn, {sanwoo}@pku.edu.cn, {ekko}@stu.pku.edu.cn, {wuyf}@pku.edu.cn"
+        },
+        {
+            "type": "title",
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+            "content": "Abstract"
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+            "bbox": [
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+            "content": "The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications. The code of this paper can be found at https://github.com/JesseZZZZZ/3DM."
+        },
+        {
+            "type": "title",
+            "bbox": [
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+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "content": "Recent advances in MLLMs (Liu et al., 2023; Chen et al., 2024; GLM et al., 2024; Zhu et al., 2024a) have shown remarkable performance in various multimodal tasks, ranging from image captioning (Wang et al., 2024) and visual question answering (Li et al., 2023) to a nuanced multimodal sarcasm"
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+            "content": "<table><tr><td>Model</td><td>Acc</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>LLaVA-v1.5-7b</td><td>0.516</td><td>0.469</td><td>0.947</td><td>0.628</td></tr><tr><td>ChatGLM4-9b</td><td>0.689</td><td>0.725</td><td>0.450</td><td>0.555</td></tr></table>"
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+            "content": "Table 1: The performance of LLaVA-v1.5-7b with a positive bias, and ChatGLM4-9b with a negative bias."
+        },
+        {
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+            "bbox": [
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+            "content": "Figure 1: Conceptual comparison of model merging with fine-tuning and ensembling in the context of debi-aising. Model merging is training-free and benefits from efficient inference."
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+            "bbox": [
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+            "content": "detection (Tang et al., 2024). Despite the progress, MLLMs are prone to biased predictions (Cui et al., 2023; Han et al., 2024). For instance, Table 1 shows that LLaVA (Liu et al., 2023) favors classifying inputs as sarcastic (positive-biased model), whereas ChatGLM (GLM et al., 2024) has the opposite tendency (negative-biased model). This may be a symptom of hallucinating answers from spurious correlations seen in the dataset (Bai et al., 2024). MLLMs' inherent biases compromise their reliability and fairness for deployment in real-world applications. Thus, enhancing MLLMs' accuracy and ensuring minimal bias have significant practical implications."
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+            "content": "In this paper, we present the first attempt, to the best of our knowledge, at merging models (Yang et al., 2024; Ramé et al., 2023; Lin et al., 2024) to debias MLLMs and showcasing its general effectiveness. Existing debiasing or dehallucination methods have relied on labeled datasets for finetuning (Chen et al., 2021; Guo et al., 2022; Liu et al., 2024) or repetitive inference for ensembling predictions (Clark et al., 2019), both of which in"
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+            "content": "* Corresponding author."
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+        {
+            "type": "footer",
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+            "angle": 0,
+            "content": "Findings of the Association for Computational Linguistics: ACL 2025, pages 14049-14059 July 27 - August 1, 2025 ©2025 Association for Computational Linguistics"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "Our approach collects a positive-biased model and a negative-biased model, then merges them in the parameter space without the need for additional training and repeated inference. Through this process, biases in opposite directions are canceled out efficiently. See Fig. 1 for the conceptual comparisons between merging and the traditional approaches."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "However, merging MLLMs for debiasing faces several challenges: (1) Merging models often requires the same architecture across models to allow for parameter-wise operations, a condition rarely satisfied in the rapidly evolving ecosystem of MLLMs (Zhang et al., 2024); (2) Reducing the bias alone does not always translate to improved accuracy—debiased models may struggle with task performance. This highlights the need to refine existing merging methods (Ilharco et al., 2022; Yadav et al., 2024; Yu et al., 2024) through the lens of reducing bias and enhancing accuracy."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "We propose 3DM (Distill, Dynamic Drop and Merge), an architecture-agnostic merging framework designed to address these challenges. First, knowledge distillation (Gou et al., 2021) bridges architectural gaps between models, enabling parameter-level merging even for heterogeneous MLLMs. Second, we introduce a dynamic dropping strategy that assigns parameter-specific drop rates based on their influence on bias and accuracy. This is motivated by a recent merging method—DARE (Yu et al., 2024)—that sparsifies parameters by a uniform chance of dropout and treats all parameters equally."
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "content": "We first conduct experiments on the MMSD2.0 (Qin et al., 2023) sarcasm detection dataset and measure models' bias with our newly proposed metric, Bias Ratio (Sec. 3). The results demonstrate that (1) merging methods are in common effective in reducing bias, and that (2) 3DM significantly outperforms DARE and other baselines in accuracy, F1-score, and Bias Ratio. In addition, experiments on MMSD1.0 (Cai et al., 2019) further validate that 3DM generalizes well across different datasets. Compared with methods requiring hyperparameter search over the validation data, 3DM does not contain such hyperparameters, making it convenient for implementation."
+        },
+        {
+            "type": "text",
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+            "content": "In essence, our contributions are as follows:"
+        },
+        {
+            "type": "text",
+            "bbox": [
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+            "angle": 0,
+            "content": "1. Architecture Alignment: A distillation pipeline that aligns MLLM architectures, preserving their original bias and accuracy."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.524,
+                0.085,
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+            "angle": 0,
+            "content": "2. Dynamic Dropping: A merging strategy that adaptively adjusts drop rates to reduce biases and improve accuracy."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.524,
+                0.144,
+                0.885,
+                0.192
+            ],
+            "angle": 0,
+            "content": "3. Bias Ratio: A metric for quantifying bias direction and magnitude, contributing to ongoing efforts in bias quantification."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.524,
+                0.204,
+                0.885,
+                0.266
+            ],
+            "angle": 0,
+            "content": "4. Empirical Validation: Extensive experiments demonstrating 3DM's effectiveness in terms of both debiasing and accuracy enhancement."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.524,
+                0.085,
+                0.885,
+                0.266
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.28,
+                0.666,
+                0.295
+            ],
+            "angle": 0,
+            "content": "2 Related Work"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.306,
+                0.691,
+                0.321
+            ],
+            "angle": 0,
+            "content": "2.1 Model Debiasing"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.327,
+                0.885,
+                0.534
+            ],
+            "angle": 0,
+            "content": "Existing debiasing mechanisms in the literature can be classified into two primary categories (Mehrabi et al., 2021; Pessach and Shmueli, 2022): training-based debiasing and training-free debiasing. Training-based debiasing approaches necessitate modifications to the training dataset (Li and Vasconcelos, 2019), demonstrating notable effectiveness while requiring extensively annotated training data. Conversely, training-free debiasing methodologies primarily focus on altering the output distribution (Kamiran et al., 2012), with assembling emerging as a crucial technique in this domain (Clark et al., 2019)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.536,
+                0.887,
+                0.777
+            ],
+            "angle": 0,
+            "content": "A notable example of ensembling is the blindfolding strategy proposed by Zhu et al. (2024b), which involves masking specific portions of the input and computing the final output score as the difference between traditional inference, fully blindfolded inference, and partially blindfolded inference. Although ensembling methods eliminate the need for training processes, they incur substantial computational overhead due to the requirement for multiple inference operations. In light of these considerations, we propose our merging strategy as an effective compromise between these two approaches, offering the dual advantages of eliminating excessive inference requirements while maintaining a label-free training process."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.789,
+                0.681,
+                0.804
+            ],
+            "angle": 0,
+            "content": "2.2 Model Merging"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.809,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Garipov et al. (2018); Draxler et al. (2018) demonstrated that two models trained from different initializations can be connected by a path of nonincreasing loss in the loss landscape, referred to as model connectivity. If the two models share a significant part of the optimization trajectory (e.g., pre-trained model), they are often connected by a"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14050"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.489,
+                0.244
+            ],
+            "angle": 0,
+            "content": "linear path (Frankle et al., 2020; Neyshabur et al., 2020; Mirzadeh et al., 2021), where interpolating along the path potentially leads to better accuracy and generalization (Izmailov et al., 2018). This property has been exploited as simply averaging the weights of numerous models fine-tuned from different hyperparameters to improve accuracy (Wortsman et al., 2022), popularizing model merging as an efficient alternative to ensemble in combining models without additional instruction tuning."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.246,
+                0.49,
+                0.6
+            ],
+            "angle": 0,
+            "content": "The success of averaging fine-tuned models has led to a surge of merging methods, aimed at steering models' behavior in desired way. A prominent example is multi-task learning via merging, where accounting for parameter importance (Matena and Raffel, 2022) and minimizing prediction differences to the fine-tuned models (Jin et al., 2022) are shown to be effective. While these methods relies on statistics that are expensive to compute, Task Arithmetic (Ilharco et al., 2022) presents a cost-effective and scalable method of adding the weighted average of task vectors (i.e., fine-tuned part of parameters) to the pre-trained model. Subsequent studies are dedicated to pre-processing task vectors to reduce interference across models (Yadav et al., 2024; Yu et al., 2024; Deep et al., 2024). Moreover, distillation is proposed for architecture alignment by FUSECHAT (Wan et al., 2024). Our distill-merge pipeline and dynamic dropping strategy aligns with this line of research, however we are focused on editing task vectors to reduce bias and improve accuracy."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.611,
+                0.24,
+                0.626
+            ],
+            "angle": 0,
+            "content": "3 Bias Ratio"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.636,
+                0.49,
+                0.78
+            ],
+            "angle": 0,
+            "content": "The metrics used to evaluate a model's bias (or fairness) remain a subject of ongoing dialogue, with no clear consensus yet (Caton and Haas, 2024). Previous studies have employed various evaluation metrics to assess bias. In this work, we introduce the Bias Ratio (BR) as a measure of a model's bias, which is based on the quantities of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN)."
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.175,
+                0.787,
+                0.489,
+                0.82
+            ],
+            "angle": 0,
+            "content": "\\[\nB R = \\frac {F P}{F P + T N} - \\frac {F N}{F N + T P} \\tag {1}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.826,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "The Bias Ratio (BR) ranges from \\(-1\\) to \\(1\\), where its absolute value indicates the magnitude of bias, and its sign denotes the direction. For instance, a BR value of 0.8 reflects a relatively high degree of positive bias, whereas a BR of \\(-0.1\\) suggests a relatively low degree of negative bias. While"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.15
+            ],
+            "angle": 0,
+            "content": "previous studies have primarily conducted qualitative analyses of bias based on TP, TN, FP, and FN, we propose a quantitative metric to systematically assess both the degree and direction of bias."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.164,
+                0.614,
+                0.179
+            ],
+            "angle": 0,
+            "content": "4 Method"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.192,
+                0.885,
+                0.336
+            ],
+            "angle": 0,
+            "content": "Focusing on a two-way classification task (e.g., sarcasm detection), suppose we are given two MLLMs, a positive-biased model and a negative-biased model: A positive-biased model tends to classify an input as positive sample, represented by high recall and low precision (Table 1). Likewise, a negative-biased model is inclined to classify an input as negative sample, represented by low recall and high precision (Table 1)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.337,
+                0.885,
+                0.465
+            ],
+            "angle": 0,
+            "content": "Then we apply our proposed 3DM framework following three steps, as illustrated in Fig. 2: (1) knowledge distillation for architecture alignment; (2) dynamic dropping strategy that filters out delta parameters based on the contribution to accuracy and bias; (3) merging the positive-biased delta parameters and negative-biased delta parameters to cancel out predictive bias."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.48,
+                0.863,
+                0.495
+            ],
+            "angle": 0,
+            "content": "4.1 Architecture Alignment via Distillation"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.502,
+                0.885,
+                0.71
+            ],
+            "angle": 0,
+            "content": "An intuitive way to mitigate bias is to merge a positive-biased model and a negative-biased model to cancel out the bias. However, the diverse ecosystem of MLLMs makes it challenging to guarantee those two models to share the same architecture, blocking them from being merged through parameter-wise operations. Knowledge distillation provides a viable solution by reshaping the two models into the same architecture, while preserving the predictive accuracy and bias of each model. Hence we start by distilling the two types of models and proceed to model merging (Sec. 4.2, 4.3) on the basis of compatible architecture."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.712,
+                0.885,
+                0.856
+            ],
+            "angle": 0,
+            "content": "Knowledge distillation (Gou et al., 2021) typically follows a teacher-student structure, where the teacher model's output (generated by the prompt proposed in Sec. 5.1.2) supervises the student model such that the student model inherits the behavior of the teacher model. Note that the student model is not required to be smaller than the teacher model in our case, as our goal of knowledge distillation does not lie in compression."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.858,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Specially, we fine-tune the pre-trained model using pseudo labels generated by a teacher model (i.e., either positive-biased model or negative-biased model). We minimize cross-entropy loss evaluated"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.524,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14051"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.123,
+                0.087,
+                0.885,
+                0.313
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.322,
+                0.884,
+                0.381
+            ],
+            "angle": 0,
+            "content": "Figure 2: Overview of 3DM framework. First, positive-biased model and negative-biased model are distilled to a base student model to share an identical architecture. Second, dynamic dropping assigns a drop rate to each delta parameter based on the discrepancy between the positive-biased model and the negative-biased model. Then, sparse task vectors after dropping are added to the base model to build a debiased model."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.405,
+                0.275,
+                0.421
+            ],
+            "angle": 0,
+            "content": "on the pseudo labels:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.184,
+                0.433,
+                0.488,
+                0.473
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathcal {L} _ {c e} = - \\sum_ {t = 1} ^ {m} \\log P \\left(\\hat {y} _ {t} \\mid x, \\hat {y} _ {<   t}\\right) \\tag {2}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.485,
+                0.49,
+                0.567
+            ],
+            "angle": 0,
+            "content": "where \\(\\{\\hat{y}_i\\}_{i=1}^m\\) is the pseudo label of length \\(m\\) generated by teacher model. In the context of sarcasm detection, \\(x\\) is a pair of input text and image and \\(\\{\\hat{y}_i\\}_{i=1}^m\\) is an answer sequence indicating whether the input pair contains sarcasm."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.578,
+                0.314,
+                0.594
+            ],
+            "angle": 0,
+            "content": "4.2 Dynamic Dropping"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.599,
+                0.489,
+                0.679
+            ],
+            "angle": 0,
+            "content": "Merging a positive-biased model and a negative-biased model is in general effective in alleviating the bias. In this section, we further propose dynamic dropping, aiming to improve accuracy and F1-score while simultaneously reducing bias."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.68,
+                0.49,
+                0.888
+            ],
+            "angle": 0,
+            "content": "In model merging, delta parameters are defined as the subtraction of parameters of base model from the fine-tuned model, and they can be understood as task vectors (Ilharco et al., 2022). Findings by Yu et al. (2024) suggest that one could randomly zero-out delta parameters of an LLM with a drop rate of \\( p \\) and re-scale the remaining ones by \\( 1 / (1 - p) \\) without impacting the model's performance. This sparsification strategy—coined as DARE—has been shown to be helpful in reducing parameter interference among the models to be merged. However, DARE assigns the same drop rate for all delta parameters."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.89,
+                0.489,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Conversely, the drop rate of a delta parameter should ideally be determined by its contribution"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.405,
+                0.884,
+                0.453
+            ],
+            "angle": 0,
+            "content": "to improving accuracy and reducing bias. That is, \"important\" delta parameters should be preserved by a higher probability."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.462,
+                0.884,
+                0.543
+            ],
+            "angle": 0,
+            "content": "Delta Parameters We merge the distilled positive-biased model and negative-biased model by editing their respective delta parameters and combining those to the base student model. Delta parameters are defined as:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.618,
+                0.553,
+                0.884,
+                0.574
+            ],
+            "angle": 0,
+            "content": "\\[\nd _ {i j} ^ {P} = W _ {i j} ^ {P} - W _ {i j} ^ {\\text {b a s e}} \\tag {3}\n\\]"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.618,
+                0.584,
+                0.883,
+                0.606
+            ],
+            "angle": 0,
+            "content": "\\[\nd _ {i j} ^ {N} = W _ {i j} ^ {N} - W _ {i j} ^ {\\text {b a s e}} \\tag {4}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.611,
+                0.884,
+                0.693
+            ],
+            "angle": 0,
+            "content": "where \\( W^{base} \\in \\mathbb{R}^{m \\times n} \\) is a parameter matrix of the base model and \\( W^{P} \\) and \\( W^{N} \\) are the ones distilled from positive-biased model and negative-biased model, respectively. \\( i \\) and \\( j \\) denotes position \\( (i,j) \\) of the parameter in \\( W \\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.702,
+                0.885,
+                0.783
+            ],
+            "angle": 0,
+            "content": "Classification of Delta Parameters. In terms of which delta parameters are more responsible for boosting model's accuracy and suppressing bias, we suggest the following criteria for classifying delta parameters into three categories:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.525,
+                0.791,
+                0.881,
+                0.83
+            ],
+            "angle": 0,
+            "content": "1. Bias-free Delta (Fig. 3(a)), where \\( d_{ij}^{P} \\) and \\( d_{ij}^{N} \\) have the same sign, i.e. \\( d_{ij}^{P}d_{ij}^{N} > 0 \\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.524,
+                0.839,
+                0.884,
+                0.92
+            ],
+            "angle": 0,
+            "content": "2. Unidirectional Delta (Fig. 3(b)), where \\( d_{ij}^{P} \\) and \\( d_{ij}^{N} \\) have the opposite sign, and the magnitude of one dominates the magnitude of the other, i.e. \\( d_{ij}^{P}d_{ij}^{N} < 0 \\) and \\( |d_{ij}^{P} + d_{ij}^{N}| > c \\) where \\( c \\) is a threshold."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.524,
+                0.791,
+                0.884,
+                0.92
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14052"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.16,
+                0.087,
+                0.231,
+                0.15
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.182,
+                0.18,
+                0.207,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(a)"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.264,
+                0.088,
+                0.338,
+                0.164
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.29,
+                0.181,
+                0.315,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(b)"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.372,
+                0.087,
+                0.444,
+                0.178
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.399,
+                0.182,
+                0.421,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(c)"
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.215,
+                0.49,
+                0.302
+            ],
+            "angle": 0,
+            "content": "Figure 3: Configurations of delta parameters under different conditions. The delta parameter from the positive-biased model (blue) and the negative-biased model (pink) can exhibit (a) the same sign, (b) opposite signs with a large magnitude difference, or (c) opposite signs with comparable magnitudes (dashed)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.129,
+                0.323,
+                0.49,
+                0.397
+            ],
+            "angle": 0,
+            "content": "3. Bidirectional Delta (Fig. 3(c)), where \\( d_{ij}^{P} \\) and \\( d_{ij}^{N} \\) have the opposite signs, and the magnitudes of both are comparable, i.e. \\( d_{ij}^{P}d_{ij}^{N} < 0 \\) and \\( |d_{ij}^{P} + d_{ij}^{N}| < c \\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.402,
+                0.49,
+                0.595
+            ],
+            "angle": 0,
+            "content": "The above criteria follows from our hypothesis about the roles of delta parameters: (1) Delta parameters with the same sign indicates a consistent direction in parameter updates by the positive-biased model and negative-biased model, potentially implying salient deltas that are associated with accuracy; (2) Given that positive-biased model and negative-biased model are best distinguished by their bias, those delta parameters with the opposite sign have greater contribution to bias, in which bidirectional delta may lead to severer interference while merging than unidirectional delta."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.603,
+                0.49,
+                0.716
+            ],
+            "angle": 0,
+            "content": "Towards Adaptive Drop Rate via Dynamic Dropping. Our classification of delta parameters motivates us to assign increasing drop rates for bias-free delta, unidirectional delta, and bidirectional delta. In light of this, we present dynamic dropping, a strategy of applying adaptive drop rate \\( p_{ij} \\) at a parameter-level:"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.158,
+                0.737,
+                0.488,
+                0.787
+            ],
+            "angle": 0,
+            "content": "\\[\np _ {i j} = \\left\\{ \\begin{array}{l l} 0 & \\text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} \\geq 0 \\\\ 1 - \\frac {\\left| d _ {i j} ^ {P} + d _ {i j} ^ {N} \\right|}{\\left| d _ {i j} ^ {P} \\right| + \\left| d _ {i j} ^ {N} \\right|} & \\text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} <   0 \\end{array} \\right. \\tag {5}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.793,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Here, \\( p_{ij} \\) is the drop rate between 0 and 1. Intuitively, Eq. 5 excludes bias-free delta from dropout operation, and for \\( d_{ij}^{P}d_{ij}^{N} < 0 \\), Eq. 5 imposes higher drop rate on bidirectional delta than on unidirectional delta. Noted, we implement a synchronized dropping mechanism where delta parameters at the same position are either dropped or retained simultaneously."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.549,
+                0.082,
+                0.845,
+                0.147
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>MMSD1.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>8642</td><td>11174</td></tr><tr><td>Validation</td><td>2410</td><td>959</td><td>1451</td></tr><tr><td>Test</td><td>2409</td><td>959</td><td>1450</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.546,
+                0.156,
+                0.845,
+                0.171
+            ],
+            "angle": 0,
+            "content": "Table 2: Composition of MMSD1.0 dataset."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.55,
+                0.184,
+                0.845,
+                0.249
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>MMSD2.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>9572</td><td>10240</td></tr><tr><td>Validation</td><td>2410</td><td>1042</td><td>1368</td></tr><tr><td>Test</td><td>2409</td><td>1037</td><td>1372</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.546,
+                0.258,
+                0.845,
+                0.273
+            ],
+            "angle": 0,
+            "content": "Table 3: Composition of MMSD2.0 dataset."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.3,
+                0.882,
+                0.363
+            ],
+            "angle": 0,
+            "content": "After dynamic dropping, each remaining delta parameter is rescaled by \\(1 / (1 - p_{ij})\\) to preserve the expectation of input embeddings, as elaborated in Yu et al. (2024)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.376,
+                0.713,
+                0.391
+            ],
+            "angle": 0,
+            "content": "4.3 Parameter Merging"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.397,
+                0.884,
+                0.466
+            ],
+            "angle": 0,
+            "content": "Let the delta parameters after dynamic dropping and re-scaling be \\(\\hat{d}_{ij}^{P}\\) and \\(\\hat{d}_{ij}^{N}\\). Then the average of \\(\\hat{d}_{ij}^{P}\\) and \\(\\hat{d}_{ij}^{N}\\) is added to the base model parameter to derive the merged parameter \\(W_{ij}^{*}\\):"
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.574,
+                0.479,
+                0.883,
+                0.501
+            ],
+            "angle": 0,
+            "content": "\\[\nW _ {i j} ^ {*} = 0. 5 \\hat {d} _ {i j} ^ {P} + 0. 5 \\hat {d} _ {i j} ^ {N} + W _ {i j} ^ {\\text {b a s e}} \\tag {6}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.511,
+                0.884,
+                0.558
+            ],
+            "angle": 0,
+            "content": "\\(W^{*}\\) is the final model weights of our 3DM method, where bias is reduced and the overall performance is boosted."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.572,
+                0.656,
+                0.589
+            ],
+            "angle": 0,
+            "content": "5 Experiments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.598,
+                0.884,
+                0.695
+            ],
+            "angle": 0,
+            "content": "In this section, we first introduce the experimental setup, including the datasets, prompts, base models, and baselines. Then, we design experiments to validate our method. Distillation (5.2), merging (5.3), ensembling (5.5), and generalizability (5.6) are analyzed in this section."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.707,
+                0.714,
+                0.722
+            ],
+            "angle": 0,
+            "content": "5.1 Experimental Setup"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.728,
+                0.631,
+                0.741
+            ],
+            "angle": 0,
+            "content": "5.1.1 Dataset"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.747,
+                0.882,
+                0.843
+            ],
+            "angle": 0,
+            "content": "We validate our approach on MMSD2.0 (Qin et al., 2023), a multi-modal sarcasm detection dataset whose test set contains 2409 sentences along with images, and we test the generalizability on MMSD1.0 (Cai et al., 2019). See Table 3 for dataset statistics."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.854,
+                0.755,
+                0.869
+            ],
+            "angle": 0,
+            "content": "5.1.2 Implementation Details"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.874,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Prompt Template. We use a fixed template to format the prompt. The template is carefully designed to ensure consistency across all samples"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14053"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.118,
+                0.085,
+                0.483,
+                0.236
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.25,
+                0.489,
+                0.32
+            ],
+            "angle": 0,
+            "content": "Figure 4: Visualization of the percentage of different types of parameters we encounter when merging. Blue and red represent the sign of \\( d_{ij}^{P} \\) and \\( d_{ij}^{N} \\) being same and different. The \\( y \\) axis represents the last \\( y \\) layers in the model."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.347,
+                0.489,
+                0.379
+            ],
+            "angle": 0,
+            "content": "and to minimize any potential bias introduced by expression. The following prompt template is used:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.379,
+                0.489,
+                0.442
+            ],
+            "angle": 0,
+            "content": "\"This is an image with: \" as the caption. Is the image-text pair sarcastic?First answer the question with yes or no, then explain your reasons.\""
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.454,
+                0.49,
+                0.614
+            ],
+            "angle": 0,
+            "content": "Knowledge Distillation. To examine the validity of knowledge distillation in transferring both accuracy and bias from the teacher models, we choose LLaVA-1.5-7b (Liu et al., 2023) and InternVL-2.5-8b (Chen et al., 2024) as student models (base models), and select LLaVA-1.5-7b and ChatGLM4-9b (GLM et al., 2024) as teacher models. Our choices of small-sized MLLMs are intended to show that 3DM does not necessitate any pre-existing sarcasm detection capabilities in the student models."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.625,
+                0.49,
+                0.786
+            ],
+            "angle": 0,
+            "content": "Dynamic Dropping. To assess the effectiveness of dynamic dropping, we fix InternVL as the base model and obtain positive and negative delta parameters distilled from LLaVA and ChatGLM, respectively. Choosing InternVL is informed by empirical observations (See Table 4), indicating that the pretrained InternVL exhibits weak bias \\((BR = 0.185)\\) relatively, and has no pre-existing knowledge of sarcasm detection \\((Acc \\approx 0.5)\\), making it an appropriate candidate for our experiment."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.796,
+                0.489,
+                0.859
+            ],
+            "angle": 0,
+            "content": "Hyperparameter Searching The fixed drop rate of DARE and our ablation study (unused in 3DM) is set to 0.7, which is the result of tuning on the validation set of MMSD2.0 (Table 8)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.87,
+                0.248,
+                0.884
+            ],
+            "angle": 0,
+            "content": "5.1.3 Baselines"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.89,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "We compare 3DM with merging baselines including Average Merging (Wortsman et al., 2022),"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.18
+            ],
+            "angle": 0,
+            "content": "TIES (Yadav et al., 2024) and DARE (Yu et al., 2024), in addition to ensembling. TIES merges models by drop-elect-merge operations, where the \"drop\" step mitigates interference by removing redundant delta parameters based on their magnitudes."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.193,
+                0.748,
+                0.208
+            ],
+            "angle": 0,
+            "content": "5.2 Distillation Experiments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.213,
+                0.885,
+                0.388
+            ],
+            "angle": 0,
+            "content": "Table 4 and Table 5 present the performance and bias of both teacher models and student models after distillation. As observed, the student models effectively inherit the bias direction of their respective teacher models, while also achieving improved performance, except for the F1-score of LLaVA base model distilled from ChatGLM4. These results demonstrate that we can successfully prepare models for merging-based debiasing through distillation, without the need for elaborate training labels."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.391,
+                0.885,
+                0.518
+            ],
+            "angle": 0,
+            "content": "For the subsequent merging experiments, we apply our proposed distill-merge pipeline for debiasing when LLaVA serves as the student model. For InternVL as the student model, we further compare our proposed dynamic dropping method with baseline merging approaches, as InternVL itself exhibits a weak bias and can therefore be used as the base model."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.531,
+                0.729,
+                0.546
+            ],
+            "angle": 0,
+            "content": "5.3 Merging Experiments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.552,
+                0.882,
+                0.583
+            ],
+            "angle": 0,
+            "content": "This section analyzes the results on the testing set of MMSD2.0 (Qin et al., 2023)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.584,
+                0.885,
+                0.776
+            ],
+            "angle": 0,
+            "content": "For the LLaVA base model, we compare the performance of merged models against their original counterparts. As shown in Table 5, the average merging strategy fails to surpass the negative-biased model. However, applying DARE (fixed dropping) leads to significant improvements, with both accuracy and F1-score approximating those of the zero-shot inference of teacher models, alongside a substantial reduction in the absolute value of BR. This highlights the potential of our distill- merge pipeline for debiasing tasks when combined with a well-designed merging method."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.778,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Similarly, Table 4 implies that all merging strategies significantly reduce the absolute value of BR, compared to student base models distilled from biased models into InternVL, further demonstrating the effectiveness of our distill-merge pipeline. Moreover, 3DM, which introduces a tailored dropping mechanism in the \"merge\" phase, achieves state-of-the-art performance in accuracy, F1-score and BR across all merging approaches. This under"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14054"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.115,
+                0.082,
+                0.881,
+                0.252
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.689</td><td>0.555</td><td>466</td><td>177</td><td>1195</td><td>571</td><td>-</td><td>-0.422</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.499</td><td>0.509</td><td>625</td><td>796</td><td>576</td><td>412</td><td>weak</td><td>0.183</td></tr><tr><td rowspan=\"7\">InternVL-2.5-8b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.543</td><td>0.629</td><td>934</td><td>998</td><td>374</td><td>103</td><td>+</td><td>0.628</td></tr><tr><td>negative learning</td><td>0.644</td><td>0.428</td><td>321</td><td>141</td><td>1231</td><td>716</td><td>-</td><td>-0.588</td></tr><tr><td rowspan=\"4\">Merging</td><td>average merging</td><td>0.688</td><td>0.614</td><td>599</td><td>314</td><td>1058</td><td>438</td><td>weak</td><td>-0.194</td></tr><tr><td>TIES</td><td>0.648</td><td>0.484</td><td>397</td><td>208</td><td>1164</td><td>640</td><td>-</td><td>-0.466</td></tr><tr><td>DARE</td><td>0.684</td><td>0.609</td><td>592</td><td>316</td><td>1056</td><td>445</td><td>weak</td><td>-0.199</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.663</td><td>0.516</td><td>431</td><td>205</td><td>1159</td><td>605</td><td>-</td><td>-0.434</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.26,
+                0.884,
+                0.304
+            ],
+            "angle": 0,
+            "content": "Table 4: Results of applying multiple debiasing methods, including average merging, fixed dropping (DARE), our proposed 3DM and ensembling methods. \"+\" and \"-\" indicate that the model tends to give positive and negative answers."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.115,
+                0.304,
+                0.881,
+                0.398
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"5\">LLaVA-v1.5-7b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>negative learning</td><td>0.710</td><td>0.666</td><td>572</td><td>233</td><td>1139</td><td>465</td><td>-</td><td>-0.279</td></tr><tr><td rowspan=\"2\">Merging</td><td>average merging</td><td>0.671</td><td>0.474</td><td>357</td><td>113</td><td>1259</td><td>680</td><td>-</td><td>-0.573</td></tr><tr><td>DARE</td><td>0.714</td><td>0.649</td><td>617</td><td>290</td><td>1082</td><td>400</td><td>weak</td><td>-0.189</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.716</td><td>0.693</td><td>773</td><td>421</td><td>951</td><td>264</td><td>weak</td><td>0.05</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.407,
+                0.884,
+                0.437
+            ],
+            "angle": 0,
+            "content": "Table 5: Results of applying debiasing methods on LLaVA-based models. Because LLaVA itself has a positive bias, we apply the original model to the \"positive learning\" row."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.115,
+                0.449,
+                0.88,
+                0.53
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Model</td><td>Ablation type</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"4\">InternVL-2.5-8b</td><td>Bias-free</td><td>0.663</td><td>0.661</td><td>790</td><td>564</td><td>808</td><td>247</td><td>weak</td><td>0.173</td></tr><tr><td>Uni. &amp; Bi.</td><td>0.649</td><td>0.477</td><td>386</td><td>195</td><td>1177</td><td>651</td><td>-</td><td>-0.486</td></tr><tr><td>Async.</td><td>0.691</td><td>0.637</td><td>654</td><td>362</td><td>1010</td><td>383</td><td>weak</td><td>-0.105</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.539,
+                0.884,
+                0.584
+            ],
+            "angle": 0,
+            "content": "Table 6: Ablation study. In 3DM, we classify each position into two categories based on their signs. In this part, we remove one of them, and test the method's performance. We also examine the performance of the synchronized dropping mechanism."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.608,
+                0.486,
+                0.64
+            ],
+            "angle": 0,
+            "content": "scores the effectiveness and superiority of dynamic dropping."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.642,
+                0.489,
+                0.851
+            ],
+            "angle": 0,
+            "content": "We provide insights into why 3DM outperforms other merging approaches. While TIES mitigates interference between delta parameters through sign selection, it struggles in cases like Fig. 3(c), where it may remain on the wrong side. DARE, on the other hand, applies a uniform drop rate to all delta parameters, disregarding their distinct roles. However, as illustrated in Fig. 4, the proportion of bias-free delta parameters (blue) is comparable to that of unidirectional and bidirectional delta parameters (red), highlighting the necessity of dynamically assigning drop rates based on their roles (Sec. 4.2) and merging conditions (Fig. 3)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.866,
+                0.281,
+                0.882
+            ],
+            "angle": 0,
+            "content": "5.4 Ablation Study"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.89,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "To better understand the role of dynamic dropping in 3DM, we conduct an ablation study by modify-"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.608,
+                0.8,
+                0.623
+            ],
+            "angle": 0,
+            "content": "ing key components of the mechanism."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.507,
+                0.625,
+                0.884,
+                0.785
+            ],
+            "angle": 0,
+            "content": "As shown in Table 6, Bias-free, which replaces the adaptive drop rates of unidirectional and bidirectional deltas in Eq. 5 with a fixed rate, results in lower accuracy, along with a higher absolute value of BR. This suggests that a fixed drop rate fails to effectively leverage the variations in \\( d_{ij}^{P} \\) and \\( d_{ij}^{N} \\). Similarly, Uni. & Bi., which follows DARE by applying a fixed drop rate to bias-free deltas instead of fully preserving them, also performs suboptimally compared to 3DM."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.787,
+                0.885,
+                0.884
+            ],
+            "angle": 0,
+            "content": "Additionally, we evaluate a less aggressive strategy than 3DM (synchronized dropping), called Async., which drops delta parameters individually based on Eq. 5. This reduces the likelihood of simultaneously eliminating delta parameters<sup>1</sup> in the scenario shown in Fig. 3(c). While this approach"
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.509,
+                0.895,
+                0.883,
+                0.92
+            ],
+            "angle": 0,
+            "content": "1The final parameter at that position defaults to the base model's."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14055"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.116,
+                0.082,
+                0.881,
+                0.253
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.445</td><td>0.587</td><td>952</td><td>1331</td><td>119</td><td>7</td><td>+</td><td>0.911</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.713</td><td>0.587</td><td>492</td><td>225</td><td>1225</td><td>467</td><td>-</td><td>-0.332</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.483</td><td>0.473</td><td>559</td><td>846</td><td>604</td><td>400</td><td>weak</td><td>0.166</td></tr><tr><td rowspan=\"7\">InternVL-2.5-8b</td><td rowspan=\"2\">Distillation</td><td>positive learning</td><td>0.501</td><td>0.592</td><td>871</td><td>1113</td><td>337</td><td>88</td><td>+</td><td>0.676</td></tr><tr><td>negative learning</td><td>0.667</td><td>0.466</td><td>350</td><td>193</td><td>1257</td><td>609</td><td>-</td><td>-0.502</td></tr><tr><td rowspan=\"4\">Merging</td><td>average merging</td><td>0.691</td><td>0.619</td><td>605</td><td>390</td><td>1060</td><td>354</td><td>weak</td><td>-0.100</td></tr><tr><td>TIES</td><td>0.676</td><td>0.519</td><td>422</td><td>244</td><td>1206</td><td>537</td><td>-</td><td>-0.392</td></tr><tr><td>DARE</td><td>0.686</td><td>0.613</td><td>600</td><td>397</td><td>1053</td><td>359</td><td>weak</td><td>-0.101</td></tr><tr><td>3DM</td><td>0.691</td><td>0.636</td><td>651</td><td>436</td><td>1014</td><td>308</td><td>weak</td><td>-0.020</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.680</td><td>0.530</td><td>433</td><td>241</td><td>1200</td><td>526</td><td>-</td><td>-0.381</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.307,
+                0.261,
+                0.688,
+                0.275
+            ],
+            "angle": 0,
+            "content": "Table 7: Performance of methods on MMSD1.0 dataset."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.302,
+                0.49,
+                0.462
+            ],
+            "angle": 0,
+            "content": "achieves a slightly lower BR, it suffers a small drop in accuracy and F1-score. This could be due to it tends to retain a delta parameter in a single wrong direction, thus degenerating into TIES. This reinforces the effectiveness of the synchronized dropping mechanism, which not only preserves flexibility in handling unidirectional deltas but also forces the dropping of delta parameters in the bidirectional delta condition, where they may introduce greater bias or interference."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.476,
+                0.379,
+                0.492
+            ],
+            "angle": 0,
+            "content": "5.5 Comparison with Ensemble"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.499,
+                0.49,
+                0.658
+            ],
+            "angle": 0,
+            "content": "We conduct a systematic comparison between our 3DM method and ensemble approaches. For sarcasm detection, ensemble methods generate individual probability distributions and aggregate them for final predictions. While achieving acceptable performance, these methods incur substantial computational overhead, with inference costs scaling as \\( O(n) \\), compared to \\( O(1) \\) for merging methods. This establishes a fundamental advantage for merging approaches."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.661,
+                0.49,
+                0.821
+            ],
+            "angle": 0,
+            "content": "In our experiments, we implement basic averaging ensemble, where model distributions are arithmetically averaged. As shown in Table 4, Table 5, and Table 7, this approach demonstrates limited effectiveness on the testing dataset. Although more sophisticated ensemble techniques might surpass 3DM's performance, they cannot overcome the inherent computational limitations of all ensemble methods, which remain a fundamental constraint compared to merging approaches."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.835,
+                0.36,
+                0.851
+            ],
+            "angle": 0,
+            "content": "5.6 Generalizability Analysis"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.858,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "In order to test the generalizability of our method, we validate our method on the testing set of MMSD1.0 (Cai et al., 2019). We retain the checkpoints in Sec. 5.2, and apply average merging,"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.302,
+                0.885,
+                0.446
+            ],
+            "angle": 0,
+            "content": "TIES, DARE and 3DM in exactly the same way as Sec. 5.3, but on the MMSD1.0 dataset. Table 7 presents the results of multiple methods, where 3DM exhibits the highest accuracy, the highest F1-score, and the lowest absolute value of BR. Moreover, all merging-based methods reduce the absolute value of BR. The results in Table 7 imply comparable tendency with Table 4, demonstrating the advanced generalizability of 3DM."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.458,
+                0.834,
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+            ],
+            "angle": 0,
+            "content": "5.7 Hyperparameter Tuning for DARE"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.48,
+                0.884,
+                0.543
+            ],
+            "angle": 0,
+            "content": "We search the hyperparameter on the validation set of MMSD2.0, and report the result in Table 8. Based on the result, we select 0.7 as the drop rate for DARE in our experiment."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.557,
+                0.642,
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+            ],
+            "angle": 0,
+            "content": "6 Conclusion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.584,
+                0.885,
+                0.743
+            ],
+            "angle": 0,
+            "content": "In this study, we present a comprehensive analysis of biases in MLLMs, empirically demonstrating that the majority of existing MLLMs exhibit significant biases in sarcasm detection tasks, with varying directional tendencies. Our work represents the first systematic effort to develop an architecture-agnostic merging framework specifically designed to address and mitigate biases in models with divergent bias orientations, particularly in debiasing tasks."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.745,
+                0.885,
+                0.889
+            ],
+            "angle": 0,
+            "content": "The core contributions of our research include: (1) a generalized distill-merge pipeline applicable to both black-box and white-box MLLMs, and (2) a novel dynamic dropping mechanism that assigns individualized drop rates to delta parameters based on each parameter's functional role in the model. Notably, our distill-merge pipeline serves as a general, plug-and-play component that can be seamlessly integrated into various merging methodologies."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.89,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "This research establishes a new paradigm for bias mitigation in MLLMs through advanced merge"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.478,
+                0.929,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "14056"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.156,
+                0.082,
+                0.842,
+                0.153
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>Method</td><td>Hyperparameter</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan=\"4\">DARE</td><td>drop rate = 0.1</td><td>0.676</td><td>0.592</td><td>566</td><td>304</td><td>1064</td><td>476</td><td>weak</td><td>-0.235</td></tr><tr><td>drop rate = 0.3</td><td>0.682</td><td>0.588</td><td>547</td><td>272</td><td>1096</td><td>495</td><td>weak</td><td>-0.276</td></tr><tr><td>drop rate = 0.5</td><td>0.686</td><td>0.610</td><td>591</td><td>306</td><td>1062</td><td>451</td><td>weak</td><td>-0.209</td></tr><tr><td>drop rate = 0.7</td><td>0.694</td><td>0.613</td><td>584</td><td>279</td><td>1089</td><td>458</td><td>weak</td><td>-0.236</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.224,
+                0.162,
+                0.772,
+                0.177
+            ],
+            "angle": 0,
+            "content": "Table 8: Hyperparameter sensitivity of DARE on the validation set of MMSD2.0"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.203,
+                0.49,
+                0.298
+            ],
+            "angle": 0,
+            "content": "ing techniques, while simultaneously introducing a parameter-specific analytical framework for understanding and utilizing delta parameters. We anticipate that our findings will stimulate further research in this emerging area of MLLM optimization and bias reduction."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.311,
+                0.221,
+                0.325
+            ],
+            "angle": 0,
+            "content": "Limitations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.336,
+                0.49,
+                0.577
+            ],
+            "angle": 0,
+            "content": "In this study, we introduce a distill-merge pipeline designed for architectural alignment, alongside a dynamic merging mechanism that assigns a unique drop rate for each delta parameter. Nonetheless, the current implementation of assigning drop rates overlooks the intricate interplay of synergistic and antagonistic interactions among multiple delta parameters, which could potentially influence the optimization process and outcomes. For instance, several delta parameters altogether contributes to biases, while any one of them individually can not. This limitation suggests a fertile ground for future research to explore and integrate these complex parameter interactions, thereby refining the mechanism's efficacy and robustness."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.59,
+                0.279,
+                0.606
+            ],
+            "angle": 0,
+            "content": "Acknowledgments"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.615,
+                0.49,
+                0.679
+            ],
+            "angle": 0,
+            "content": "This work is supported by the National Natural Science Foundation of China (62076008) and the Key Project of Natural Science Foundation of China (61936012)."
+        },
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2025/3DM_ Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models/0429ce61-4caf-4027-bc76-a381459f26e5_origin.pdf

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2025/3DM_ Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models/full.md

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+# 3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models
+
+Zhaoxi Zhang $^{1,2,4}$ , Sanwoo Lee $^{1,2}$ , Zhixiang Wang $^{1,3}$ , Yunfang Wu $^{1,2*}$
+
+$^{1}$ National Key Laboratory for Multimedia Information Processing, Peking University  $^{2}$ School of Computer Science, Peking University, Beijing, China
+
+$^{3}$ School of Software and Microelectronics, Peking University, Beijing, China
+
+$^{4}$ School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China {1120210536}@bit.edu.cn, {sanwoo}@pku.edu.cn, {ekko}@stu.pku.edu.cn, {wuyf}@pku.edu.cn
+
+# Abstract
+
+The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications. The code of this paper can be found at https://github.com/JesseZZZZZ/3DM.
+
+# 1 Introduction
+
+Recent advances in MLLMs (Liu et al., 2023; Chen et al., 2024; GLM et al., 2024; Zhu et al., 2024a) have shown remarkable performance in various multimodal tasks, ranging from image captioning (Wang et al., 2024) and visual question answering (Li et al., 2023) to a nuanced multimodal sarcasm
+
+<table><tr><td>Model</td><td>Acc</td><td>Precision</td><td>Recall</td><td>F1</td></tr><tr><td>LLaVA-v1.5-7b</td><td>0.516</td><td>0.469</td><td>0.947</td><td>0.628</td></tr><tr><td>ChatGLM4-9b</td><td>0.689</td><td>0.725</td><td>0.450</td><td>0.555</td></tr></table>
+
+Table 1: The performance of LLaVA-v1.5-7b with a positive bias, and ChatGLM4-9b with a negative bias.
+
+![](images/3963e59728a96649323ff726db501c6f72f603a05a3b066bba93aa9f73192818.jpg)  
+Figure 1: Conceptual comparison of model merging with fine-tuning and ensembling in the context of debi-aising. Model merging is training-free and benefits from efficient inference.
+
+detection (Tang et al., 2024). Despite the progress, MLLMs are prone to biased predictions (Cui et al., 2023; Han et al., 2024). For instance, Table 1 shows that LLaVA (Liu et al., 2023) favors classifying inputs as sarcastic (positive-biased model), whereas ChatGLM (GLM et al., 2024) has the opposite tendency (negative-biased model). This may be a symptom of hallucinating answers from spurious correlations seen in the dataset (Bai et al., 2024). MLLMs' inherent biases compromise their reliability and fairness for deployment in real-world applications. Thus, enhancing MLLMs' accuracy and ensuring minimal bias have significant practical implications.
+
+In this paper, we present the first attempt, to the best of our knowledge, at merging models (Yang et al., 2024; Ramé et al., 2023; Lin et al., 2024) to debias MLLMs and showcasing its general effectiveness. Existing debiasing or dehallucination methods have relied on labeled datasets for finetuning (Chen et al., 2021; Guo et al., 2022; Liu et al., 2024) or repetitive inference for ensembling predictions (Clark et al., 2019), both of which in
+
+Our approach collects a positive-biased model and a negative-biased model, then merges them in the parameter space without the need for additional training and repeated inference. Through this process, biases in opposite directions are canceled out efficiently. See Fig. 1 for the conceptual comparisons between merging and the traditional approaches.
+
+However, merging MLLMs for debiasing faces several challenges: (1) Merging models often requires the same architecture across models to allow for parameter-wise operations, a condition rarely satisfied in the rapidly evolving ecosystem of MLLMs (Zhang et al., 2024); (2) Reducing the bias alone does not always translate to improved accuracy—debiased models may struggle with task performance. This highlights the need to refine existing merging methods (Ilharco et al., 2022; Yadav et al., 2024; Yu et al., 2024) through the lens of reducing bias and enhancing accuracy.
+
+We propose 3DM (Distill, Dynamic Drop and Merge), an architecture-agnostic merging framework designed to address these challenges. First, knowledge distillation (Gou et al., 2021) bridges architectural gaps between models, enabling parameter-level merging even for heterogeneous MLLMs. Second, we introduce a dynamic dropping strategy that assigns parameter-specific drop rates based on their influence on bias and accuracy. This is motivated by a recent merging method—DARE (Yu et al., 2024)—that sparsifies parameters by a uniform chance of dropout and treats all parameters equally.
+
+We first conduct experiments on the MMSD2.0 (Qin et al., 2023) sarcasm detection dataset and measure models' bias with our newly proposed metric, Bias Ratio (Sec. 3). The results demonstrate that (1) merging methods are in common effective in reducing bias, and that (2) 3DM significantly outperforms DARE and other baselines in accuracy, F1-score, and Bias Ratio. In addition, experiments on MMSD1.0 (Cai et al., 2019) further validate that 3DM generalizes well across different datasets. Compared with methods requiring hyperparameter search over the validation data, 3DM does not contain such hyperparameters, making it convenient for implementation.
+
+In essence, our contributions are as follows:
+
+1. Architecture Alignment: A distillation pipeline that aligns MLLM architectures, preserving their original bias and accuracy.
+
+2. Dynamic Dropping: A merging strategy that adaptively adjusts drop rates to reduce biases and improve accuracy.  
+3. Bias Ratio: A metric for quantifying bias direction and magnitude, contributing to ongoing efforts in bias quantification.  
+4. Empirical Validation: Extensive experiments demonstrating 3DM's effectiveness in terms of both debiasing and accuracy enhancement.
+
+# 2 Related Work
+
+# 2.1 Model Debiasing
+
+Existing debiasing mechanisms in the literature can be classified into two primary categories (Mehrabi et al., 2021; Pessach and Shmueli, 2022): training-based debiasing and training-free debiasing. Training-based debiasing approaches necessitate modifications to the training dataset (Li and Vasconcelos, 2019), demonstrating notable effectiveness while requiring extensively annotated training data. Conversely, training-free debiasing methodologies primarily focus on altering the output distribution (Kamiran et al., 2012), with assembling emerging as a crucial technique in this domain (Clark et al., 2019).
+
+A notable example of ensembling is the blindfolding strategy proposed by Zhu et al. (2024b), which involves masking specific portions of the input and computing the final output score as the difference between traditional inference, fully blindfolded inference, and partially blindfolded inference. Although ensembling methods eliminate the need for training processes, they incur substantial computational overhead due to the requirement for multiple inference operations. In light of these considerations, we propose our merging strategy as an effective compromise between these two approaches, offering the dual advantages of eliminating excessive inference requirements while maintaining a label-free training process.
+
+# 2.2 Model Merging
+
+Garipov et al. (2018); Draxler et al. (2018) demonstrated that two models trained from different initializations can be connected by a path of nonincreasing loss in the loss landscape, referred to as model connectivity. If the two models share a significant part of the optimization trajectory (e.g., pre-trained model), they are often connected by a
+
+linear path (Frankle et al., 2020; Neyshabur et al., 2020; Mirzadeh et al., 2021), where interpolating along the path potentially leads to better accuracy and generalization (Izmailov et al., 2018). This property has been exploited as simply averaging the weights of numerous models fine-tuned from different hyperparameters to improve accuracy (Wortsman et al., 2022), popularizing model merging as an efficient alternative to ensemble in combining models without additional instruction tuning.
+
+The success of averaging fine-tuned models has led to a surge of merging methods, aimed at steering models' behavior in desired way. A prominent example is multi-task learning via merging, where accounting for parameter importance (Matena and Raffel, 2022) and minimizing prediction differences to the fine-tuned models (Jin et al., 2022) are shown to be effective. While these methods relies on statistics that are expensive to compute, Task Arithmetic (Ilharco et al., 2022) presents a cost-effective and scalable method of adding the weighted average of task vectors (i.e., fine-tuned part of parameters) to the pre-trained model. Subsequent studies are dedicated to pre-processing task vectors to reduce interference across models (Yadav et al., 2024; Yu et al., 2024; Deep et al., 2024). Moreover, distillation is proposed for architecture alignment by FUSECHAT (Wan et al., 2024). Our distill-merge pipeline and dynamic dropping strategy aligns with this line of research, however we are focused on editing task vectors to reduce bias and improve accuracy.
+
+# 3 Bias Ratio
+
+The metrics used to evaluate a model's bias (or fairness) remain a subject of ongoing dialogue, with no clear consensus yet (Caton and Haas, 2024). Previous studies have employed various evaluation metrics to assess bias. In this work, we introduce the Bias Ratio (BR) as a measure of a model's bias, which is based on the quantities of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN).
+
+$$
+B R = \frac {F P}{F P + T N} - \frac {F N}{F N + T P} \tag {1}
+$$
+
+The Bias Ratio (BR) ranges from  $-1$  to  $1$ , where its absolute value indicates the magnitude of bias, and its sign denotes the direction. For instance, a BR value of 0.8 reflects a relatively high degree of positive bias, whereas a BR of  $-0.1$  suggests a relatively low degree of negative bias. While
+
+previous studies have primarily conducted qualitative analyses of bias based on TP, TN, FP, and FN, we propose a quantitative metric to systematically assess both the degree and direction of bias.
+
+# 4 Method
+
+Focusing on a two-way classification task (e.g., sarcasm detection), suppose we are given two MLLMs, a positive-biased model and a negative-biased model: A positive-biased model tends to classify an input as positive sample, represented by high recall and low precision (Table 1). Likewise, a negative-biased model is inclined to classify an input as negative sample, represented by low recall and high precision (Table 1).
+
+Then we apply our proposed 3DM framework following three steps, as illustrated in Fig. 2: (1) knowledge distillation for architecture alignment; (2) dynamic dropping strategy that filters out delta parameters based on the contribution to accuracy and bias; (3) merging the positive-biased delta parameters and negative-biased delta parameters to cancel out predictive bias.
+
+# 4.1 Architecture Alignment via Distillation
+
+An intuitive way to mitigate bias is to merge a positive-biased model and a negative-biased model to cancel out the bias. However, the diverse ecosystem of MLLMs makes it challenging to guarantee those two models to share the same architecture, blocking them from being merged through parameter-wise operations. Knowledge distillation provides a viable solution by reshaping the two models into the same architecture, while preserving the predictive accuracy and bias of each model. Hence we start by distilling the two types of models and proceed to model merging (Sec. 4.2, 4.3) on the basis of compatible architecture.
+
+Knowledge distillation (Gou et al., 2021) typically follows a teacher-student structure, where the teacher model's output (generated by the prompt proposed in Sec. 5.1.2) supervises the student model such that the student model inherits the behavior of the teacher model. Note that the student model is not required to be smaller than the teacher model in our case, as our goal of knowledge distillation does not lie in compression.
+
+Specially, we fine-tune the pre-trained model using pseudo labels generated by a teacher model (i.e., either positive-biased model or negative-biased model). We minimize cross-entropy loss evaluated
+
+![](images/2694ac8aad3d7ee53af74cf3df96215755daa62e5cb4dd3f0659367959d9a1f4.jpg)  
+Figure 2: Overview of 3DM framework. First, positive-biased model and negative-biased model are distilled to a base student model to share an identical architecture. Second, dynamic dropping assigns a drop rate to each delta parameter based on the discrepancy between the positive-biased model and the negative-biased model. Then, sparse task vectors after dropping are added to the base model to build a debiased model.
+
+on the pseudo labels:
+
+$$
+\mathcal {L} _ {c e} = - \sum_ {t = 1} ^ {m} \log P \left(\hat {y} _ {t} \mid x, \hat {y} _ {<   t}\right) \tag {2}
+$$
+
+where  $\{\hat{y}_i\}_{i=1}^m$  is the pseudo label of length  $m$  generated by teacher model. In the context of sarcasm detection,  $x$  is a pair of input text and image and  $\{\hat{y}_i\}_{i=1}^m$  is an answer sequence indicating whether the input pair contains sarcasm.
+
+# 4.2 Dynamic Dropping
+
+Merging a positive-biased model and a negative-biased model is in general effective in alleviating the bias. In this section, we further propose dynamic dropping, aiming to improve accuracy and F1-score while simultaneously reducing bias.
+
+In model merging, delta parameters are defined as the subtraction of parameters of base model from the fine-tuned model, and they can be understood as task vectors (Ilharco et al., 2022). Findings by Yu et al. (2024) suggest that one could randomly zero-out delta parameters of an LLM with a drop rate of  $p$  and re-scale the remaining ones by  $1 / (1 - p)$  without impacting the model's performance. This sparsification strategy—coined as DARE—has been shown to be helpful in reducing parameter interference among the models to be merged. However, DARE assigns the same drop rate for all delta parameters.
+
+Conversely, the drop rate of a delta parameter should ideally be determined by its contribution
+
+to improving accuracy and reducing bias. That is, "important" delta parameters should be preserved by a higher probability.
+
+Delta Parameters We merge the distilled positive-biased model and negative-biased model by editing their respective delta parameters and combining those to the base student model. Delta parameters are defined as:
+
+$$
+d _ {i j} ^ {P} = W _ {i j} ^ {P} - W _ {i j} ^ {\text {b a s e}} \tag {3}
+$$
+
+$$
+d _ {i j} ^ {N} = W _ {i j} ^ {N} - W _ {i j} ^ {\text {b a s e}} \tag {4}
+$$
+
+where  $W^{base} \in \mathbb{R}^{m \times n}$  is a parameter matrix of the base model and  $W^{P}$  and  $W^{N}$  are the ones distilled from positive-biased model and negative-biased model, respectively.  $i$  and  $j$  denotes position  $(i,j)$  of the parameter in  $W$ .
+
+Classification of Delta Parameters. In terms of which delta parameters are more responsible for boosting model's accuracy and suppressing bias, we suggest the following criteria for classifying delta parameters into three categories:
+
+1. Bias-free Delta (Fig. 3(a)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the same sign, i.e.  $d_{ij}^{P}d_{ij}^{N} > 0$ .  
+2. Unidirectional Delta (Fig. 3(b)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the opposite sign, and the magnitude of one dominates the magnitude of the other, i.e.  $d_{ij}^{P}d_{ij}^{N} < 0$  and  $|d_{ij}^{P} + d_{ij}^{N}| > c$  where  $c$  is a threshold.
+
+![](images/b93e841a60b7e67b2fded2b2ad89cd1eb4055c16cbd8a6a716e28a6fed1952d2.jpg)  
+(a)
+
+![](images/e506c9ebf32a42ce2bd94e860e2d85adc0f62d204f97541d86bbc517bc8b0c46.jpg)  
+(b)
+
+![](images/109791fa0a075079673853b6033687fcbd6a395cb56ed47edaffe44a85d45342.jpg)  
+(c)  
+Figure 3: Configurations of delta parameters under different conditions. The delta parameter from the positive-biased model (blue) and the negative-biased model (pink) can exhibit (a) the same sign, (b) opposite signs with a large magnitude difference, or (c) opposite signs with comparable magnitudes (dashed).
+
+3. Bidirectional Delta (Fig. 3(c)), where  $d_{ij}^{P}$  and  $d_{ij}^{N}$  have the opposite signs, and the magnitudes of both are comparable, i.e.  $d_{ij}^{P}d_{ij}^{N} < 0$  and  $|d_{ij}^{P} + d_{ij}^{N}| < c$ .
+
+The above criteria follows from our hypothesis about the roles of delta parameters: (1) Delta parameters with the same sign indicates a consistent direction in parameter updates by the positive-biased model and negative-biased model, potentially implying salient deltas that are associated with accuracy; (2) Given that positive-biased model and negative-biased model are best distinguished by their bias, those delta parameters with the opposite sign have greater contribution to bias, in which bidirectional delta may lead to severer interference while merging than unidirectional delta.
+
+Towards Adaptive Drop Rate via Dynamic Dropping. Our classification of delta parameters motivates us to assign increasing drop rates for bias-free delta, unidirectional delta, and bidirectional delta. In light of this, we present dynamic dropping, a strategy of applying adaptive drop rate  $p_{ij}$  at a parameter-level:
+
+$$
+p _ {i j} = \left\{ \begin{array}{l l} 0 & \text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} \geq 0 \\ 1 - \frac {\left| d _ {i j} ^ {P} + d _ {i j} ^ {N} \right|}{\left| d _ {i j} ^ {P} \right| + \left| d _ {i j} ^ {N} \right|} & \text {i f} d _ {i j} ^ {P} d _ {i j} ^ {N} <   0 \end{array} \right. \tag {5}
+$$
+
+Here,  $p_{ij}$  is the drop rate between 0 and 1. Intuitively, Eq. 5 excludes bias-free delta from dropout operation, and for  $d_{ij}^{P}d_{ij}^{N} < 0$ , Eq. 5 imposes higher drop rate on bidirectional delta than on unidirectional delta. Noted, we implement a synchronized dropping mechanism where delta parameters at the same position are either dropped or retained simultaneously.
+
+<table><tr><td>MMSD1.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>8642</td><td>11174</td></tr><tr><td>Validation</td><td>2410</td><td>959</td><td>1451</td></tr><tr><td>Test</td><td>2409</td><td>959</td><td>1450</td></tr></table>
+
+Table 2: Composition of MMSD1.0 dataset.  
+
+<table><tr><td>MMSD2.0</td><td>All</td><td>Positive</td><td>Negative</td></tr><tr><td>Train</td><td>19816</td><td>9572</td><td>10240</td></tr><tr><td>Validation</td><td>2410</td><td>1042</td><td>1368</td></tr><tr><td>Test</td><td>2409</td><td>1037</td><td>1372</td></tr></table>
+
+Table 3: Composition of MMSD2.0 dataset.
+
+After dynamic dropping, each remaining delta parameter is rescaled by  $1 / (1 - p_{ij})$  to preserve the expectation of input embeddings, as elaborated in Yu et al. (2024).
+
+# 4.3 Parameter Merging
+
+Let the delta parameters after dynamic dropping and re-scaling be  $\hat{d}_{ij}^{P}$  and  $\hat{d}_{ij}^{N}$ . Then the average of  $\hat{d}_{ij}^{P}$  and  $\hat{d}_{ij}^{N}$  is added to the base model parameter to derive the merged parameter  $W_{ij}^{*}$ :
+
+$$
+W _ {i j} ^ {*} = 0. 5 \hat {d} _ {i j} ^ {P} + 0. 5 \hat {d} _ {i j} ^ {N} + W _ {i j} ^ {\text {b a s e}} \tag {6}
+$$
+
+$W^{*}$  is the final model weights of our 3DM method, where bias is reduced and the overall performance is boosted.
+
+# 5 Experiments
+
+In this section, we first introduce the experimental setup, including the datasets, prompts, base models, and baselines. Then, we design experiments to validate our method. Distillation (5.2), merging (5.3), ensembling (5.5), and generalizability (5.6) are analyzed in this section.
+
+# 5.1 Experimental Setup
+
+# 5.1.1 Dataset
+
+We validate our approach on MMSD2.0 (Qin et al., 2023), a multi-modal sarcasm detection dataset whose test set contains 2409 sentences along with images, and we test the generalizability on MMSD1.0 (Cai et al., 2019). See Table 3 for dataset statistics.
+
+# 5.1.2 Implementation Details
+
+Prompt Template. We use a fixed template to format the prompt. The template is carefully designed to ensure consistency across all samples
+
+![](images/b7b7a36d002d95b163640f871d6f1861398247304ec2a4a9413c1d7a94d163f9.jpg)  
+Figure 4: Visualization of the percentage of different types of parameters we encounter when merging. Blue and red represent the sign of  $d_{ij}^{P}$  and  $d_{ij}^{N}$  being same and different. The  $y$  axis represents the last  $y$  layers in the model.
+
+and to minimize any potential bias introduced by expression. The following prompt template is used:
+
+"This is an image with: " as the caption. Is the image-text pair sarcastic?First answer the question with yes or no, then explain your reasons."
+
+Knowledge Distillation. To examine the validity of knowledge distillation in transferring both accuracy and bias from the teacher models, we choose LLaVA-1.5-7b (Liu et al., 2023) and InternVL-2.5-8b (Chen et al., 2024) as student models (base models), and select LLaVA-1.5-7b and ChatGLM4-9b (GLM et al., 2024) as teacher models. Our choices of small-sized MLLMs are intended to show that 3DM does not necessitate any pre-existing sarcasm detection capabilities in the student models.
+
+Dynamic Dropping. To assess the effectiveness of dynamic dropping, we fix InternVL as the base model and obtain positive and negative delta parameters distilled from LLaVA and ChatGLM, respectively. Choosing InternVL is informed by empirical observations (See Table 4), indicating that the pretrained InternVL exhibits weak bias  $(BR = 0.185)$  relatively, and has no pre-existing knowledge of sarcasm detection  $(Acc \approx 0.5)$ , making it an appropriate candidate for our experiment.
+
+Hyperparameter Searching The fixed drop rate of DARE and our ablation study (unused in 3DM) is set to 0.7, which is the result of tuning on the validation set of MMSD2.0 (Table 8).
+
+# 5.1.3 Baselines
+
+We compare 3DM with merging baselines including Average Merging (Wortsman et al., 2022),
+
+TIES (Yadav et al., 2024) and DARE (Yu et al., 2024), in addition to ensembling. TIES merges models by drop-elect-merge operations, where the "drop" step mitigates interference by removing redundant delta parameters based on their magnitudes.
+
+# 5.2 Distillation Experiments
+
+Table 4 and Table 5 present the performance and bias of both teacher models and student models after distillation. As observed, the student models effectively inherit the bias direction of their respective teacher models, while also achieving improved performance, except for the F1-score of LLaVA base model distilled from ChatGLM4. These results demonstrate that we can successfully prepare models for merging-based debiasing through distillation, without the need for elaborate training labels.
+
+For the subsequent merging experiments, we apply our proposed distill-merge pipeline for debiasing when LLaVA serves as the student model. For InternVL as the student model, we further compare our proposed dynamic dropping method with baseline merging approaches, as InternVL itself exhibits a weak bias and can therefore be used as the base model.
+
+# 5.3 Merging Experiments
+
+This section analyzes the results on the testing set of MMSD2.0 (Qin et al., 2023).
+
+For the LLaVA base model, we compare the performance of merged models against their original counterparts. As shown in Table 5, the average merging strategy fails to surpass the negative-biased model. However, applying DARE (fixed dropping) leads to significant improvements, with both accuracy and F1-score approximating those of the zero-shot inference of teacher models, alongside a substantial reduction in the absolute value of BR. This highlights the potential of our distill- merge pipeline for debiasing tasks when combined with a well-designed merging method.
+
+Similarly, Table 4 implies that all merging strategies significantly reduce the absolute value of BR, compared to student base models distilled from biased models into InternVL, further demonstrating the effectiveness of our distill-merge pipeline. Moreover, 3DM, which introduces a tailored dropping mechanism in the "merge" phase, achieves state-of-the-art performance in accuracy, F1-score and BR across all merging approaches. This under
+
+<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.689</td><td>0.555</td><td>466</td><td>177</td><td>1195</td><td>571</td><td>-</td><td>-0.422</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.499</td><td>0.509</td><td>625</td><td>796</td><td>576</td><td>412</td><td>weak</td><td>0.183</td></tr><tr><td rowspan="7">InternVL-2.5-8b</td><td rowspan="2">Distillation</td><td>positive learning</td><td>0.543</td><td>0.629</td><td>934</td><td>998</td><td>374</td><td>103</td><td>+</td><td>0.628</td></tr><tr><td>negative learning</td><td>0.644</td><td>0.428</td><td>321</td><td>141</td><td>1231</td><td>716</td><td>-</td><td>-0.588</td></tr><tr><td rowspan="4">Merging</td><td>average merging</td><td>0.688</td><td>0.614</td><td>599</td><td>314</td><td>1058</td><td>438</td><td>weak</td><td>-0.194</td></tr><tr><td>TIES</td><td>0.648</td><td>0.484</td><td>397</td><td>208</td><td>1164</td><td>640</td><td>-</td><td>-0.466</td></tr><tr><td>DARE</td><td>0.684</td><td>0.609</td><td>592</td><td>316</td><td>1056</td><td>445</td><td>weak</td><td>-0.199</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.663</td><td>0.516</td><td>431</td><td>205</td><td>1159</td><td>605</td><td>-</td><td>-0.434</td></tr></table>
+
+Table 4: Results of applying multiple debiasing methods, including average merging, fixed dropping (DARE), our proposed 3DM and ensembling methods. "+" and "-" indicate that the model tends to give positive and negative answers.  
+
+<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan="5">LLaVA-v1.5-7b</td><td rowspan="2">Distillation</td><td>positive learning</td><td>0.516</td><td>0.628</td><td>982</td><td>1110</td><td>262</td><td>55</td><td>+</td><td>0.765</td></tr><tr><td>negative learning</td><td>0.710</td><td>0.666</td><td>572</td><td>233</td><td>1139</td><td>465</td><td>-</td><td>-0.279</td></tr><tr><td rowspan="2">Merging</td><td>average merging</td><td>0.671</td><td>0.474</td><td>357</td><td>113</td><td>1259</td><td>680</td><td>-</td><td>-0.573</td></tr><tr><td>DARE</td><td>0.714</td><td>0.649</td><td>617</td><td>290</td><td>1082</td><td>400</td><td>weak</td><td>-0.189</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.716</td><td>0.693</td><td>773</td><td>421</td><td>951</td><td>264</td><td>weak</td><td>0.05</td></tr></table>
+
+Table 5: Results of applying debiasing methods on LLaVA-based models. Because LLaVA itself has a positive bias, we apply the original model to the "positive learning" row.  
+
+<table><tr><td>Model</td><td>Ablation type</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan="4">InternVL-2.5-8b</td><td>Bias-free</td><td>0.663</td><td>0.661</td><td>790</td><td>564</td><td>808</td><td>247</td><td>weak</td><td>0.173</td></tr><tr><td>Uni. &amp; Bi.</td><td>0.649</td><td>0.477</td><td>386</td><td>195</td><td>1177</td><td>651</td><td>-</td><td>-0.486</td></tr><tr><td>Async.</td><td>0.691</td><td>0.637</td><td>654</td><td>362</td><td>1010</td><td>383</td><td>weak</td><td>-0.105</td></tr><tr><td>3DM</td><td>0.697</td><td>0.643</td><td>658</td><td>351</td><td>1021</td><td>379</td><td>weak</td><td>-0.110</td></tr></table>
+
+Table 6: Ablation study. In 3DM, we classify each position into two categories based on their signs. In this part, we remove one of them, and test the method's performance. We also examine the performance of the synchronized dropping mechanism.
+
+scores the effectiveness and superiority of dynamic dropping.
+
+We provide insights into why 3DM outperforms other merging approaches. While TIES mitigates interference between delta parameters through sign selection, it struggles in cases like Fig. 3(c), where it may remain on the wrong side. DARE, on the other hand, applies a uniform drop rate to all delta parameters, disregarding their distinct roles. However, as illustrated in Fig. 4, the proportion of bias-free delta parameters (blue) is comparable to that of unidirectional and bidirectional delta parameters (red), highlighting the necessity of dynamically assigning drop rates based on their roles (Sec. 4.2) and merging conditions (Fig. 3).
+
+# 5.4 Ablation Study
+
+To better understand the role of dynamic dropping in 3DM, we conduct an ablation study by modify-
+
+ing key components of the mechanism.
+
+As shown in Table 6, Bias-free, which replaces the adaptive drop rates of unidirectional and bidirectional deltas in Eq. 5 with a fixed rate, results in lower accuracy, along with a higher absolute value of BR. This suggests that a fixed drop rate fails to effectively leverage the variations in  $d_{ij}^{P}$  and  $d_{ij}^{N}$ . Similarly, Uni. & Bi., which follows DARE by applying a fixed drop rate to bias-free deltas instead of fully preserving them, also performs suboptimally compared to 3DM.
+
+Additionally, we evaluate a less aggressive strategy than 3DM (synchronized dropping), called Async., which drops delta parameters individually based on Eq. 5. This reduces the likelihood of simultaneously eliminating delta parameters<sup>1</sup> in the scenario shown in Fig. 3(c). While this approach
+
+<table><tr><td>Model</td><td>Method</td><td>Strategy</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td>LLaVA-v1.5-7b</td><td>/</td><td>zero-shot inference</td><td>0.445</td><td>0.587</td><td>952</td><td>1331</td><td>119</td><td>7</td><td>+</td><td>0.911</td></tr><tr><td>ChatGLM4-9b</td><td>/</td><td>zero-shot inference</td><td>0.713</td><td>0.587</td><td>492</td><td>225</td><td>1225</td><td>467</td><td>-</td><td>-0.332</td></tr><tr><td>InternVL-2.5-8b</td><td>/</td><td>zero-shot inference</td><td>0.483</td><td>0.473</td><td>559</td><td>846</td><td>604</td><td>400</td><td>weak</td><td>0.166</td></tr><tr><td rowspan="7">InternVL-2.5-8b</td><td rowspan="2">Distillation</td><td>positive learning</td><td>0.501</td><td>0.592</td><td>871</td><td>1113</td><td>337</td><td>88</td><td>+</td><td>0.676</td></tr><tr><td>negative learning</td><td>0.667</td><td>0.466</td><td>350</td><td>193</td><td>1257</td><td>609</td><td>-</td><td>-0.502</td></tr><tr><td rowspan="4">Merging</td><td>average merging</td><td>0.691</td><td>0.619</td><td>605</td><td>390</td><td>1060</td><td>354</td><td>weak</td><td>-0.100</td></tr><tr><td>TIES</td><td>0.676</td><td>0.519</td><td>422</td><td>244</td><td>1206</td><td>537</td><td>-</td><td>-0.392</td></tr><tr><td>DARE</td><td>0.686</td><td>0.613</td><td>600</td><td>397</td><td>1053</td><td>359</td><td>weak</td><td>-0.101</td></tr><tr><td>3DM</td><td>0.691</td><td>0.636</td><td>651</td><td>436</td><td>1014</td><td>308</td><td>weak</td><td>-0.020</td></tr><tr><td>Ensembling</td><td>ensembling</td><td>0.680</td><td>0.530</td><td>433</td><td>241</td><td>1200</td><td>526</td><td>-</td><td>-0.381</td></tr></table>
+
+Table 7: Performance of methods on MMSD1.0 dataset.
+
+achieves a slightly lower BR, it suffers a small drop in accuracy and F1-score. This could be due to it tends to retain a delta parameter in a single wrong direction, thus degenerating into TIES. This reinforces the effectiveness of the synchronized dropping mechanism, which not only preserves flexibility in handling unidirectional deltas but also forces the dropping of delta parameters in the bidirectional delta condition, where they may introduce greater bias or interference.
+
+# 5.5 Comparison with Ensemble
+
+We conduct a systematic comparison between our 3DM method and ensemble approaches. For sarcasm detection, ensemble methods generate individual probability distributions and aggregate them for final predictions. While achieving acceptable performance, these methods incur substantial computational overhead, with inference costs scaling as  $O(n)$ , compared to  $O(1)$  for merging methods. This establishes a fundamental advantage for merging approaches.
+
+In our experiments, we implement basic averaging ensemble, where model distributions are arithmetically averaged. As shown in Table 4, Table 5, and Table 7, this approach demonstrates limited effectiveness on the testing dataset. Although more sophisticated ensemble techniques might surpass 3DM's performance, they cannot overcome the inherent computational limitations of all ensemble methods, which remain a fundamental constraint compared to merging approaches.
+
+# 5.6 Generalizability Analysis
+
+In order to test the generalizability of our method, we validate our method on the testing set of MMSD1.0 (Cai et al., 2019). We retain the checkpoints in Sec. 5.2, and apply average merging,
+
+TIES, DARE and 3DM in exactly the same way as Sec. 5.3, but on the MMSD1.0 dataset. Table 7 presents the results of multiple methods, where 3DM exhibits the highest accuracy, the highest F1-score, and the lowest absolute value of BR. Moreover, all merging-based methods reduce the absolute value of BR. The results in Table 7 imply comparable tendency with Table 4, demonstrating the advanced generalizability of 3DM.
+
+# 5.7 Hyperparameter Tuning for DARE
+
+We search the hyperparameter on the validation set of MMSD2.0, and report the result in Table 8. Based on the result, we select 0.7 as the drop rate for DARE in our experiment.
+
+# 6 Conclusion
+
+In this study, we present a comprehensive analysis of biases in MLLMs, empirically demonstrating that the majority of existing MLLMs exhibit significant biases in sarcasm detection tasks, with varying directional tendencies. Our work represents the first systematic effort to develop an architecture-agnostic merging framework specifically designed to address and mitigate biases in models with divergent bias orientations, particularly in debiasing tasks.
+
+The core contributions of our research include: (1) a generalized distill-merge pipeline applicable to both black-box and white-box MLLMs, and (2) a novel dynamic dropping mechanism that assigns individualized drop rates to delta parameters based on each parameter's functional role in the model. Notably, our distill-merge pipeline serves as a general, plug-and-play component that can be seamlessly integrated into various merging methodologies.
+
+This research establishes a new paradigm for bias mitigation in MLLMs through advanced merge
+
+<table><tr><td>Method</td><td>Hyperparameter</td><td>Acc</td><td>F1</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>Bias direction</td><td>Bias Ratio</td></tr><tr><td rowspan="4">DARE</td><td>drop rate = 0.1</td><td>0.676</td><td>0.592</td><td>566</td><td>304</td><td>1064</td><td>476</td><td>weak</td><td>-0.235</td></tr><tr><td>drop rate = 0.3</td><td>0.682</td><td>0.588</td><td>547</td><td>272</td><td>1096</td><td>495</td><td>weak</td><td>-0.276</td></tr><tr><td>drop rate = 0.5</td><td>0.686</td><td>0.610</td><td>591</td><td>306</td><td>1062</td><td>451</td><td>weak</td><td>-0.209</td></tr><tr><td>drop rate = 0.7</td><td>0.694</td><td>0.613</td><td>584</td><td>279</td><td>1089</td><td>458</td><td>weak</td><td>-0.236</td></tr></table>
+
+Table 8: Hyperparameter sensitivity of DARE on the validation set of MMSD2.0
+
+ing techniques, while simultaneously introducing a parameter-specific analytical framework for understanding and utilizing delta parameters. We anticipate that our findings will stimulate further research in this emerging area of MLLM optimization and bias reduction.
+
+# Limitations
+
+In this study, we introduce a distill-merge pipeline designed for architectural alignment, alongside a dynamic merging mechanism that assigns a unique drop rate for each delta parameter. Nonetheless, the current implementation of assigning drop rates overlooks the intricate interplay of synergistic and antagonistic interactions among multiple delta parameters, which could potentially influence the optimization process and outcomes. For instance, several delta parameters altogether contributes to biases, while any one of them individually can not. This limitation suggests a fertile ground for future research to explore and integrate these complex parameter interactions, thereby refining the mechanism's efficacy and robustness.
+
+# Acknowledgments
+
+This work is supported by the National Natural Science Foundation of China (62076008) and the Key Project of Natural Science Foundation of China (61936012).
+
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2025/7 Points to Tsinghua but 10 Points to _ Assessing Large Language Models in Agentic Multilingual National Bias/829ef2ac-f616-4f29-a624-b3c6ab2656e8_content_list.json

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+        "type": "text",
+        "text": "7 Points to Tsinghua but 10 Points to清华?",
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+        "text": "Assessing Agentic Large Language Models in Multilingual National Bias",
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+        "text": "Qianying Liu<sup>1</sup> Katrina Qiyao Wang<sup>2</sup> Fei Cheng<sup>3</sup> Sadao Kurohashi<sup>1,3</sup>",
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+        "text": "$^{1}$  National Institute of Informatics, Japan",
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+        "text": "2 University of Wisconsin—Madison, USA",
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+        "text": "$^{3}$  Kyoto University, Japan",
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+        "text": "ying@nii.ac.jp; katrina.wang@wisc.edu; {feicheng, kuro}@i.kyoto-u.ac.jp",
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+        "type": "text",
+        "text": "Abstract",
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+        "text": "Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases are limited to immediate context preferences. Cross-language disparities in reasoning-based recommendations remain largely unexplored, with a lack of even descriptive analysis. This study is the first to address this gap. We test LLM's applicability and capability in providing personalized advice across three key scenarios: university applications, travel, and relocation. We investigate multilingual bias in state-of-the-art LLMs by analyzing their responses to decision-making tasks across multiple languages. We quantify bias in model-generated scores and assess the impact of demographic factors and reasoning strategies (e.g., Chain-of-Thought prompting) on bias patterns. Our findings reveal that local language bias is prevalent across different tasks, with GPT-4 and Sonnet reducing bias for English-speaking countries compared to GPT-3.5 but failing to achieve robust multilingual alignment, highlighting broader implications for multilingual AI agents and applications such as education.",
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+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
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+    },
+    {
+        "type": "text",
+        "text": "Large Language Models (LLMs) have demonstrated remarkable capabilities in multilingual natural language processing (NLP) task execution: understanding, generation, and translation across diverse languages (Shi et al., 2022; Blasi et al., 2022). Beyond these conventional applications, due to their rising reasoning ability, LLMs are increasingly utilized as inquiry agents, serving a diverse global user base (Armstrong et al., 2024; Zheng, 2024). LLMs are widely used for providing personalized advice on real-world topics such as travel planning and career development across multiple languages. Despite substantial research attention to the immediate context preferences of LLMs, signif",
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+        "image_caption": [
+            "Task I: Academic Career Planning Advisor",
+            "Figure 1: ChatGPT 3.5 response to University Application inquiries in English and Chinese. GPT-3.5 exhibits significant inconsistency between different languages. Tsinghua University is assigned significantly higher scores in Chinese (10/10) than English (7/10), and its disadvantages are dismissed in the reasoning."
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+        "text": "icant gaps remain in the literature (Gallegos et al., 2024). Hence, research on the extent to which LLMs exhibit biases in complex decision-making tasks across languages remains a substantial lacuna in the NLP field.",
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+        "text": "This study seeks to fill this gap by exploring the multilingual nationality biases of state-of-the-art (SOTA) models, which role as widely used intelligent assistants, in reasoning-based decision-making processes. Rather than focusing on biased immediate context detection, we investigate how these models behave when tasked with heavy reasoning tasks of offering advice in real-world scenar",
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+        "text": "26430",
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+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: ACL 2025, pages 26430-26442",
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+    {
+        "type": "footer",
+        "text": "July 27 - August 1, 2025 ©2025 Association for Computational Linguistics",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "ios. As illustrated in Figure 1, when queried about university application recommendations across various countries, ChatGPT demonstrates notable inconsistencies between different languages concerning Tsinghua University. The response in Chinese predominantly emphasizes the advantages of Tsinghua University, assigning it a higher rating (10/10, full score) compared to the English response (7/10). Recommendations and judgments provided by LLMs in different languages reveal evidence of nationality bias, wherein LLMs tend to favor or disadvantage certain groups based on their nationality. The absorption and dissemination of such biases by these models may perpetuate stereotypes, marginalize specific groups, and result in inequitable treatment (Ferrara, 2023). To investigate this phenomenon, we examine three distinct and culturally sensitive tasks where LLMs are expected to act as universal advisory agents: university application recommendations, travel destination recommendations, and city relocation suggestions. We aim to investigate the patterns of bias in LLM-generated recommendations when making decisions on national issues. Specifically, we examine how these recommendations vary across different languages, yielding multilingual nationality bias.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "To quantify the presence of bias, we reformulate the agent's potential nationality bias as a comprehensive assessment problem. Specifically, we evaluate how the agent rates the same entity (e.g., a university or city) across different language contexts, hypothesizing that various bias dimensions inherent in LLMs may influence these ratings. Drawing inspiration from psychophysics and decision-making studies, we revisit Thurstone's Law of Comparative Judgment (Thurstone, 1927), which provides a framework for quantifying subjective preferences through pairwise comparisons. Our methodology involves compiling lists of top universities, economically leading cities, and travel destinations, from various countries, forming triplets of options for each task (e.g., the University of Tokyo, Peking University, and Stanford University). We then prompt SOTA LLMs to assign numerical scores to each candidate within the triplet, reflecting their recommendation preferences. This process is repeated for hundreds of triplets across multiple languages, enabling us to observe patterns of bias in the agent's scores towards the candidates.",
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+    {
+        "type": "text",
+        "text": "Two primary research questions are addressed here to guide our investigation:",
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+    {
+        "type": "text",
+        "text": "RQ1: How do LLMs exhibit varying bias when acting as agents in providing advice on national issues across different languages?",
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+    {
+        "type": "text",
+        "text": "In this study, we observe the overall pattern of score distribution varies markedly across languages. LLMs display local language biases across different tasks, especially in scenarios such as university application recommendations. Edge-cutting models like GPT-4 showcase lower bias when operating in English. However, they show significant bias in non-English languages, which impacts the fairness and consistency of the agent's recommendations.",
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+    {
+        "type": "text",
+        "text": "RQ2: What role do user demographics and reasoning strategies, such as Chain-of-Thought (CoT) prompting, play in influencing the bias patterns of LLMs when they act as agents on national issues across different languages?",
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+    {
+        "type": "text",
+        "text": "Our results highlight that user demographics (gender, language group) and CoT play crucial roles in shaping LLM bias patterns on national issues. CoT does not always mitigate bias, it can amplify disparities, especially in non-English languages. Furthermore, bias dynamics vary based on demographic factors, such as gendered speech patterns in different cultures. These findings underscore the need for multilingual bias mitigation strategies that account for both demographic variation and the impact of reasoning strategies like CoT.",
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+    {
+        "type": "text",
+        "text": "Explicating these inquiries provides us with a unique perspective in studying the nationality biases present in multilingual LLMs when performing complex reasoning-based decision-making tasks. This empirical exploration not only highlights the importance of understanding these biases, but also underscores the need for further research to enhance the personalization and inclusiveness of AI-driven applications across linguistic, educational, and demographic boundaries.",
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+        "text": "2 Related Works",
+        "text_level": 1,
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+        "text": "Bias in Multilingual LLMs Bias in MLLMs presents us with a quandary. It has emerged as a critical challenge to the fairness of MLLMs and thus significantly restricts real-world deployment (Xu et al., 2023). Numerous studies have been conducted to measure language bias, which refers to the unidentical performance of MLLMs across different languages in terms of race, religion, nationality, gender, and other factors (Zhao et al., 2024; Mihaylov and Shtedritski, 2024; Mukherjee",
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+    {
+        "type": "page_number",
+        "text": "26431",
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+    {
+        "type": "text",
+        "text": "et al., 2023; Neplenbroek et al., 2024; Li et al., 2024; Vashishtha et al., 2023; Naous et al., 2024; Hofmann et al., 2024). Most of these studies primarily focus on the lexical preferences of models, either by assigning the descriptions of specific groups with positive or negative meanings or by assessing the model's ability to infer the identity of a subject described in an objectively neutral manner. Among the most relevant studies in this line of research, Narayanan Venkit et al. (2023) examine whether the use of adjectives by language models defined by nationalities in English are positive or negative. Zhu et al. (2024) further extend this analysis to a Chinese context. Additionally, Kamruzzaman et al. (2024), Nie et al. (2024) and Parrish et al. (2022) constructed multiple-choice selection evaluations in English. Their models were asked either to choose between neutral, positive, or negative adjectives to describe a nationality or to infer which nationality a given description applies to. While these studies provide valuable insights into nationality bias in LLMs, they are largely limited to monolingual settings and focus primarily on lexical-level biases. There remains a significant gap in research on multilingual biases in LLMs, particularly beyond lexicon-based evaluations.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Bias in LLMs Reasoning Agents Recent studies have extended bias research beyond immediate context preference to examine complex reasoning and decision-making tasks. Several studies have investigated the use of LLMs as simulations of multilingual survey subjects. Jin et al. (2024) examined LLM performance in moral reasoning tasks, particularly in responding to variations of the Trolley Problem. Durmus et al. (2023) explored the subjective global opinions of LLMs by prompting models to answer binary-choice questions under explicit persona settings in a multilingual context. Kwok et al. (2024) further advanced this approach by developing the Simulation of Synthetic Personas, and designing questionnaires based on real-world news to assess biases in model-generated responses. While these studies provide valuable insights into biases in complex reasoning and decision-making tasks under multilingual settings, fall short of providing a comprehensive understanding of real-world applications. Other studies addressed tasks such as hiring screening agents (Armstrong et al., 2024) and university application agents (Zheng, 2024) in English. Not only their studies limited to English, but they con",
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+    {
+        "type": "text",
+        "text": "strain the models by restricting their ability to engage in chain-of-thought (CoT)-like reasoning during responses. This significantly limits the scope and depth of bias analysis in structured decision-making processes.",
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+        "type": "text",
+        "text": "3 Methodology",
+        "text_level": 1,
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+        "text": "We begin by formalizing our decision-making tasks as comprehensive evaluation problems, where the goal is to assign overall ratings to entities—such as universities, cities, or travel destinations. This formulation acknowledges that complex advisory tasks are susceptible to multiple sources of bias, including but not limited to linguistic and gender biases. Our framework is designed to systematically detect how these different bias dimensions influence the final ratings provided by LLMs.",
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+    {
+        "type": "text",
+        "text": "To empirically evaluate these effects, we simulate real-life advisory scenarios across three domains: (1) an academic career planning advisor assisting with university application decisions, (2) a career planning advisor supporting city relocation suggestions, and (3) a travel planner offering destination recommendations. For each scenario, we generate triplets consisting of three diverse candidate options (e.g., universities or cities) and prompt LLMs to provide a recommendation along with an analysis and rating that reflects its underlying preferences.",
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+    {
+        "type": "text",
+        "text": "By repeating this process across hundreds of triplets in multiple languages, we collect statistical data that allows us to uncover patterns of bias in the model's recommendations. This approach not only highlights the influence of the primary language environment on decision-making but also enables us to assess the impact of additional bias dimensions, such as gender, on the model's evaluations.",
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+        "text": "3.1 Triplet Collection Process",
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+    {
+        "type": "text",
+        "text": "To evaluate the multilingual bias of LLMs, we first identified suitable options for each of the three tasks: university applications, travel destinations, and city relocations. The options were selected from reputable and current sources to ensure relevance and diversity.",
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+        "type": "text",
+        "text": "We rely on well-known rankings for option selection. For university recommendations, we used the \"Quacquarelli Symonds World University Rankings 2024\" (QS2024). For city relocation recommendations, we used the data on Gross Domestic Product (GDP) in the year 2022, sourced from the \"City",
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+    {
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+        "text": "26432",
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+    {
+        "type": "text",
+        "text": "Population\". Travel destination options were selected based on the \"World's Top 100 City Destinations for 2023\" report by Euromonitor International. Further details could be found in Appendices A.",
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+    {
+        "type": "text",
+        "text": "We organized the options into two categories: a target option set, which includes the main options used for bias evaluation, and a comparison option set, which includes alternative options used to form multiple triplets per target. Each triplet consists of one target and two comparison options, but only the target option is used in the final bias calculation. The comparison set options are randomly combined into 100 fixed comparison pairs of the form (target option, comparison option 1, comparison option 2), which are then reused across all targets to generate the final triplets.",
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+        "type": "text",
+        "text": "To ensure diversity in the comparison, we paired each option from the comparison set such that one was from an English-speaking country and the other from a non-English-speaking country or a country where English is not the only official language. This pairing strategy helps capture the cultural diversity of the options.",
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+        "type": "text",
+        "text": "For each pair, we used a blank placeholder and randomized the order of the options to create a triplet template. Then, we replaced the placeholder with each option from the target option set, resulting in a consistent comparison structure. This approach ensures that for each target option, the comparison triplet remains identical, enabling fair evaluation of the LLM's responses.",
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+        "text": "3.2 Prompt Design",
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+        "text": "In designing prompts for this study, we structured each prompt to simulate a real-world inquiry scenario, guiding the LLM to act as an advisory agent. As illustrated in Figure 2 and Appendices B, each prompt begins with a detailed description of the agent's persona. For example, The agent is introduced as an experienced academic career planning advisor with a strong reputation in the field of undergraduate education. This setting aims to establish the model's role and ensure consistency in the advice provided across different languages. Next, the prompt includes information about the hypothetical user client seeking advice. For instance, the student's need for guidance in applying to three specific universities is described. This setup helps frame the context of the inquiry, making the scenario more realistic and relatable for the model. We then provide clear instructions on the nature of the advice to be given. The model is asked to",
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+    {
+        "type": "text",
+        "text": "consider the advantages and disadvantages of each university comprehensively and to assign a rating score out of 10, along with explanations for each score. To ensure that the output aligns with the desired format, the prompt includes rules about how the response should be structured. Specifically, it emphasizes that the model should not simply replicate the template but should treat it as a formal response, providing analyses for each university and a final summary with scores. The prompt ends with the three options including the target option for evaluation, ensuring that the comparison triplet is presented clearly.",
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+        "type": "text",
+        "text": "For each language used in the study, we translated the prompt while preserving this structure, verifying that no semantic meaning was altered during translation. The model is expected to output both a rating score for each option and a rationale for each rating, reflecting a thoughtful evaluation that aligns with the agent persona and task requirements.",
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+        "text": "3.3 Experimental Settings",
+        "text_level": 1,
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+        "type": "text",
+        "text": "To ensure a comprehensive evaluation of multilingual biases, we selected a diverse set of countries and languages for our experiments. The selection criteria focused on including countries that have more than three universities ranked within the QS World University Rankings 2024 Top 150, ensuring that the model would have sufficient knowledge about the candidates being ranked. The selection of these languages also helps to maintain a balance between global representation and linguistic diversity in the study.",
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+    {
+        "type": "text",
+        "text": "English-speaking countries: The study includes countries where English is the primary language of instruction, such as the United States (US), the United Kingdom (UK), Canada (CA), and Australia (AU). These countries are included because they have a high number of institutions in the QS Top 100, providing a strong baseline for comparison.",
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+    {
+        "type": "text",
+        "text": "Single-major language countries: This category includes countries where a single language is predominant in education and public life, such as China (CH, Mandarin), Japan (JP, Japanese), France (FR, French), Germany (DE, German), and South Korea (KR, Korean). These countries are included for their significant academic presence and the linguistic uniqueness they bring to the study.",
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+    {
+        "type": "page_number",
+        "text": "26433",
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+    {
+        "type": "text",
+        "text": "You are an academic career planning advisor specializing in undergraduate education planning. You have a good reputation and many years of experience in the field. Now, a student is seeking your advice on applying to undergraduate universities, who has three target universities, which we will mention at the end of this passage.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "For each university, please consider the advantages and disadvantages comprehensively, give a total score out of 10, and explain the reasons.",
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+    {
+        "type": "text",
+        "text": "Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please think like a real-life academic career planning advisor who gives advice or comments to a high school student with undergraduate schooling needs. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:",
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+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1. Pseudo University 1: An analysis of Pseudo University 1.",
+            "2. Pseudo University 2: An analysis of Pseudo University 2.",
+            "3. Pseudo University 3: An analysis of Pseudo University 3."
+        ],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "In summary: Pseudo University 1: certain score; Pseudo University 2: certain score; Pseudo University 3: certain score",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Input: Harvard University, Kyoto University, Tsinghua University",
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Figure 2: Illustration of the structured prompt used in the study for University Application, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.",
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+    {
+        "type": "text",
+        "text": "Multiple-major language countries: In this category, countries like Hong Kong (HK), Singapore (SG), and Switzerland (CH) are included. These countries have multilingual educational environments, which pose unique challenges and opportunities for the models in terms of processing and understanding diverse linguistic inputs. They also possess universities within the QS Top 100, providing a comparative context with countries that use a single major language.",
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+        "text": "\"Global South\" representation: This category focuses on countries that belong to regions often considered underrepresented in global academic rankings but still have notable academic institutions. Specifically, we selected one representative from each of the following regions: Southeast Asia, South Asia, the Middle East, Africa, South America, and Central America. To broaden the representation of this study, we adopted more inclusive ranking criteria solely in this category. For example, in the university application scenario, we expanded the target option set to include institutions ranked within the QS Top 200.",
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+    {
+        "type": "text",
+        "text": "This ensures that our study incorporates perspectives from regions that are often underrepresented in AI research but are important for global diversity.",
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+    {
+        "type": "text",
+        "text": "The official languages of the first three categories of selected countries—English, Chinese, Japanese, Korean, French, and German—were used as the target languages for the study. By analyzing the",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "models' responses in these languages, we aimed to capture linguistic nuances and biases in a multilingual context.",
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+        "type": "text",
+        "text": "For the experiments, to promise the models' ability to instruction following and reasoning, we employed three state-of-the-art language models, GPT-3.5 $^{1}$ , GPT-4 $^{2}$  and Claude-Sonnet $^{3}$ . This allows us to compare their performance and observe differences in bias expression across languages, providing insights into advancements in multilingual capabilities between versions.",
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+    {
+        "type": "text",
+        "text": "4 Results",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "4.1 Distributions of Scores",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "To research how the models score suggestions across different languages, we conducted the following evaluation. This allowed us to quantify potential differences in score distributions and gain an initial insight into each model's bias. Figure 3 presents the overall distribution of model suggestions across six languages for three distinct tasks: university application recommendations, relocation advice, and travel suggestions. The distribution patterns vary significantly across languages, indicating the presence of nationality bias in the model's responses. modify this part It is essential to highlight the differences among the selected models. For example, GPT-4 tends to cluster tightly around",
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+    {
+        "type": "page_footnote",
+        "text": "gpt-3.5-turbo-0125",
+        "bbox": [
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+    },
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+        "type": "page_footnote",
+        "text": "gpt-4-turbo-2024-04-09",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "${}^{3}$  anthropicclaude-3-5-sonnet-20240620-v1:0",
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+    },
+    {
+        "type": "page_number",
+        "text": "26434",
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+    {
+        "type": "table",
+        "img_path": "images/f744aa9f7bfa10fe6a3a23b111c96e8df50491b6e165519a410fee553a5f21cc.jpg",
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+        "table_body": "<table><tr><td>Model</td><td>EN</td><td>JA</td><td>ZH</td><td>FR</td><td>DE</td><td>KO</td><td>Overall</td></tr><tr><td colspan=\"8\">University Application</td></tr><tr><td>GPT-3.5</td><td>0.37</td><td>0.39</td><td>0.41</td><td>0.58</td><td>0.39</td><td>0.33</td><td>0.41</td></tr><tr><td>GPT-4</td><td>0.28</td><td>0.30</td><td>0.35</td><td>0.32</td><td>0.42</td><td>0.35</td><td>0.33</td></tr><tr><td>Sonnet</td><td>0.38</td><td>0.33</td><td>0.50</td><td>0.40</td><td>0.29</td><td>0.36</td><td>0.38</td></tr><tr><td colspan=\"8\">Relocate</td></tr><tr><td>GPT-3.5</td><td>0.38</td><td>0.42</td><td>0.31</td><td>0.46</td><td>0.35</td><td>0.32</td><td>0.37</td></tr><tr><td>GPT-4</td><td>0.34</td><td>0.35</td><td>0.43</td><td>0.40</td><td>0.52</td><td>0.35</td><td>0.40</td></tr><tr><td>Sonnet</td><td>0.37</td><td>0.32</td><td>0.60</td><td>0.33</td><td>0.34</td><td>0.36</td><td>0.39</td></tr><tr><td colspan=\"8\">Travel</td></tr><tr><td>GPT-3.5</td><td>0.56</td><td>0.48</td><td>0.43</td><td>0.51</td><td>0.42</td><td>0.46</td><td>0.48</td></tr><tr><td>GPT-4</td><td>0.33</td><td>0.36</td><td>0.43</td><td>0.44</td><td>0.41</td><td>0.31</td><td>0.38</td></tr><tr><td>Sonnet</td><td>0.47</td><td>0.36</td><td>0.55</td><td>0.42</td><td>0.42</td><td>0.40</td><td>0.44</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 1: Jensen-Shannon Divergence (JSD) scores across languages for different tasks and models. The JSD score is applied to provide a more detailed analysis of linguistic disparities in suggestion tendencies. Higher values indicate greater dissimilarity.",
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+    {
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+        "text": "higher scores in the travel category across multiple languages. In contrast, GPT-3.5 exhibits broader variability in university application recommendations: some languages show a wide spread from 5 to almost 10. Meanwhile, the Sonnet model displays relatively uniform distributions in certain tasks, though distinctions remain, that some languages consistently receive higher median scores than others.",
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+    {
+        "type": "text",
+        "text": "The bias for each language within each LLM is calculated here. The Jensen-Shannon Divergence (JSD) score is applied to provide a more detailed analysis of linguistic disparities in suggestion tendencies, the divergence between a language-specific distribution and the global distribution serves as our bias score. Higher values indicate greater dissimilarity, signaling a stronger potential bias.",
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+    {
+        "type": "text",
+        "text": "Formally, let  $P$  denote the global score distribution and  $Q$  the score distribution for a particular language. The JSD between  $P$  and  $Q$  is defined as:",
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+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathrm {J S D} (P \\parallel Q) = \\frac {1}{2} \\mathrm {K L} (P \\parallel M) + \\frac {1}{2} \\mathrm {K L} (Q \\parallel M),\n$$\n",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "This enables a more detailed analysis of linguistic disparities in suggestion tendencies, whereas higher values suggest greater dissimilarity and hence a stronger signal of potential bias.",
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+    {
+        "type": "text",
+        "text": "A key finding is that more powerful models (i.e., GPT-4) show the lowest English bias. GPT-4 consistently has a lower JSD score for English than weaker models. However, it does not always achieve a lower overall JSD. In the relocate task, its bias score is higher than other models. This suggests that alignment technologies help English but",
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+    {
+        "type": "text",
+        "text": "lack coverage for multilingual scenarios. For GPT-3.5, JSD values can be relatively high in specific cases, such as the score of French (0.58) in the university application task. This indicates a substantial deviation from the global distribution for that language. In contrast, GPT-4 generally shows moderate JSD values but with a distinct spike for German in the relocate task, suggesting a pronounced bias in that context. For Sonnet, JSD scores often lie between those of GPT-3.5 and GPT-4. Collectively, JSD values by tasks and languages not only provide a quantitative assessment of how model responses differ across languages and tasks, but they offer a qualitative and systematic measure of potential biases embedded in the output distributions.",
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+        "type": "text",
+        "text": "4.2 Analysis of Multilingual Nationality Bias",
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+        "type": "text",
+        "text": "To assess the external validity of the score distribution across languages in Section 4.1, we examine whether inherent biases affect the comparability of scores assigned by different language groups. To ensure a fair analysis of multilingual nationality bias, we first apply normalization to the scores generated by each language. Then, we measure the degree of deviation by subtracting the mean score of each language group from individual scores, capturing how much a group's evaluation of a nation/region differs from its overall scoring.",
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+    {
+        "type": "text",
+        "text": "The violin plots in Figure 4 use the same language-nation groups as those in Figure 4: English (en), Japanese (ja), Chinese (zh), French (fr), German (de), and Korean (ko). The tasks and models remain the same. The x-axis represents language-nation pairs. The y-axis shows scores normalized by language average, with a range span",
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+        "text": "26435",
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+    {
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+        "img_path": "images/5b10df885845551c017dca2f2736812775bc1614b3248fdaee55bde67f434c5e.jpg",
+        "image_caption": [
+            "Figure 3: Violin plots illustrating the overall distribution of scores assigned by GPT-3.5, GPT-4, and Sonnet across six languages (en, fr, ja, zh, de, ko) for three tasks: university application, relocation, and travel. The x-axis denotes language, and the y-axis shows the numerical scores ranging from 5 to 10."
+        ],
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+    {
+        "type": "text",
+        "text": "ning from  $-2.0$  to 2.0. Positive values indicate that a language group assigned a higher-than-average score to a given country, suggesting a more favorable evaluation. Conversely, negative values indicate a lower-than-average score, reflecting a less favorable assessment by that language group. Red dots highlight scores assigned by a nation's local language group, representing self-assessment. Gray dots represent how different language groups evaluate a language-nation pair relative to the overall average, reflecting external perceptions. For example, a red dot for \"US\" represents the score assigned by the English language group to the United States. In contrast, gray dots correspond to scores given by other language groups, such as Chinese, Japanese, and German. Certain countries, such as the UK and Australia, show narrower distributions across language groups, suggesting relatively consistent perceptions. In contrast, others, like China and Germany, exhibit greater variability. Some language groups also have wider violins overall, indicating more within-group variation in country assessments. It is worth noting task variations, that travel scores vary more across languages than relocation scores. This shows greater diversity in travel preferences. Key observations from Figure 4 reveal two significant findings: Local language bias is prevalent across different tasks. Non-English, single-language countries show strong local language bias in university applications, while East Asian countries exhibit similar biases in travel and relocation tasks. Red dots (representing local lan",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "guage scores) are predominantly clustered in the positive region, indicating that LLMs tend to assign higher scores to countries where their language is spoken. For instance, red dots for \"CN\" suggest that models consistently assign higher scores to China when assessed in Chinese. This trend appears across multiple nations, highlighting a systematic preference for home countries and reinforcing the strong presence of local language bias. GPT-4 and Sonnet, as more powerful models, reduce bias for English-speaking countries compared to GPT-3.5 but fail to achieve robust multilingual alignment. This is particularly evident in the university application task, where GPT-4 and Sonnet display significantly less bias for English-speaking countries but continue to show substantial bias for China (CN), Japan (JP), Germany (DE), and South Korea (KR). These findings highlight the limitations of current alignment methodologies in multilingual settings, revealing that while English alignment has improved, non-English biases persist, suggesting that further refinements in multilingual alignment strategies are necessary. Across all tasks, consistent inter-model trends emerge. GPT-3.5, GPT-4, and Sonnet preserve similar rankings of countries, though the magnitude of bias varies.",
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+        "text": "4.3 Robustness Checks",
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+        "text": "4.3.1 With or Without Chain-of-Thought Bias",
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+        "text": "Since Chain-of-Thought (CoT) prompting encourages step-by-step explanations, it has the potential to both mitigate inconsistencies and reinforce bi",
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+            "Figure 4: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet across three tasks: university application, relocation, and travel."
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+    {
+        "type": "text",
+        "text": "ases present in pre-training data. To disentangle the effects of explicit reasoning from the model's inherent biases, we compare model outputs with and without CoT prompting. This serves as a robustness check by assessing whether biases persist independently of reasoning structure or if they are exacerbated by the CoT framework.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "We focus solely on the mean score difference rather than full distributional divergence. Further details for the distribution could be found in Appendices C. Cross-country comparisons of JSD scores are problematic due to inherent variations in natural score distributions. Different countries may have distinct baseline distributions, making direct JSD comparisons across nations unreliable. Specifically, we compute local bias as Mean Divergence (MD) Score using this formula: Mean Divergence Score  $= \\mu_{\\mathrm{local}} - \\mu_{\\mathrm{global}}$ , where  $\\mu_{\\mathrm{local}}$  is the mean score assigned by the local language group for a given country. And  $\\mu_{\\mathrm{global}}$  is the mean score assigned by all language groups for that country. We examine models' factor importance rankings (e.g., Reputation, Program) detailed in Appendices D and find consistency across languages, indicating that differences arise from implicit nationality bias rather than varying factor valuations.",
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+    {
+        "type": "text",
+        "text": "In Table 2, GPT-4 exhibits the lowest scores over",
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+        "type": "text",
+        "text": "all, suggesting it maintains more stable and consistent multilingual alignment than GPT-3.5 and Sonnet. First, CoT has a stronger influence on bias in English-speaking countries. Under CoT prompting, GPT-4's MD scores are very low or even negative in English-speaking countries (e.g., US: 0.01, UK: -0.03). However, without CoT, the MD scores in these regions drop further into negative values (e.g., US: -0.22, UK: -0.24). This suggests that CoT changes GPT-4's decision-making process in English-speaking contexts more than in non-English ones. Second, in non-English countries, CoT does not reduce bias as effectively—the MD scores remain relatively high (e.g., CN: 0.52, KR: 0.33). We find out that in GPT-3.5 and Sonnet, CoT prompting increases bias. In GPT-3.5, CoT generally results in much higher MD scores than without CoT, particularly for China (0.68 vs. 0.19), France (0.49 vs. 0.15), and Korea (0.51 vs. 0.38). Sonnet also experiences higher MD in CoT, especially in China (0.47), Japan (0.52), and Korea (0.48), indicating that structured reasoning does not necessarily mitigate nationality biases.",
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+    {
+        "type": "text",
+        "text": "English-speaking countries do not show the same bias amplification. CoT may be more aligned with Western fairness norms, while it reinforces cultural specificity in non-English languages. This shows an imbalance in multilingual fairness mecha",
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+    {
+        "type": "page_number",
+        "text": "26437",
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+    {
+        "type": "table",
+        "img_path": "images/2826ddc259126405f5c3626ed1c70e246186604227a503200749cb1bcb77c813.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Factor</td><td>US</td><td>UK</td><td>CA</td><td>AU</td><td>CN</td><td>JP</td><td>FR</td><td>DE</td><td>KR</td></tr><tr><td colspan=\"10\">GPT-3.5</td></tr><tr><td>CoT</td><td>0.27</td><td>0.16</td><td>0.19</td><td>0.12</td><td>0.68</td><td>0.29</td><td>0.49</td><td>0.33</td><td>0.51</td></tr><tr><td>female</td><td>0.22</td><td>0.12</td><td>0.20</td><td>-0.11</td><td>0.48</td><td>0.19</td><td>0.30</td><td>0.41</td><td>0.65</td></tr><tr><td>male</td><td>0.19</td><td>0.22</td><td>0.40</td><td>-0.06</td><td>0.46</td><td>0.12</td><td>0.33</td><td>-0.03</td><td>0.30</td></tr><tr><td>w/o CoT</td><td>0.49</td><td>0.36</td><td>0.12</td><td>0.18</td><td>0.19</td><td>0.21</td><td>0.15</td><td>0.30</td><td>0.38</td></tr><tr><td colspan=\"10\">GPT-4</td></tr><tr><td>CoT</td><td>0.01</td><td>-0.03</td><td>0.12</td><td>0.03</td><td>0.52</td><td>0.17</td><td>0.26</td><td>0.27</td><td>0.33</td></tr><tr><td>female</td><td>0.08</td><td>0.15</td><td>0.18</td><td>-0.06</td><td>0.45</td><td>0.13</td><td>0.18</td><td>0.17</td><td>0.73</td></tr><tr><td>male</td><td>0.13</td><td>0.17</td><td>0.12</td><td>0.11</td><td>0.42</td><td>0.14</td><td>0.42</td><td>0.22</td><td>0.75</td></tr><tr><td>w/o CoT</td><td>-0.22</td><td>-0.24</td><td>0.41</td><td>0.24</td><td>0.54</td><td>0.46</td><td>0.10</td><td>0.03</td><td>0.09</td></tr><tr><td colspan=\"10\">Sonnet</td></tr><tr><td>CoT</td><td>0.14</td><td>0.04</td><td>-0.12</td><td>0.07</td><td>0.47</td><td>0.52</td><td>-0.01</td><td>0.15</td><td>0.48</td></tr><tr><td>female</td><td>0.16</td><td>0.11</td><td>0.06</td><td>0.10</td><td>0.56</td><td>0.52</td><td>0.10</td><td>0.27</td><td>0.54</td></tr><tr><td>male</td><td>0.11</td><td>0.03</td><td>0.05</td><td>0.07</td><td>0.45</td><td>0.49</td><td>-0.12</td><td>0.14</td><td>0.31</td></tr><tr><td>w/o CoT</td><td>0.07</td><td>0.11</td><td>-0.02</td><td>-0.14</td><td>0.39</td><td>0.26</td><td>0.19</td><td>0.17</td><td>0.43</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "Table 2: Mean Divergence (MD) scores across languages for different tasks and models. The MD Score is calculated as the gap between the mean score difference of global and local language groups, rather than full distributional divergence. We isolate systematic local language bias while avoiding confounding factors introduced by cross-country distributional differences.",
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "nisms, where bias mitigation efforts may be disproportionately developed for English-speaking cultures, leaving non-Western biases more embedded. Establishing a bias baseline without CoT can allow us to evaluate whether structured reasoning frameworks introduce additional bias artifacts, raising concerns about fairness in multilingual AI systems.",
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "4.3.2 Gender Bias",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "We examine gender bias as a robustness check alongside the linguistic and cultural diversity of the selected countries: how LLMs may perpetuate or mitigate biases in different academic and societal contexts. We focus on assessing whether the persona-driven responses maintain robustness or exhibit vulnerability when subjected to cross-lingual tasks and the impact of language-specific cultural nuances on bias amplification. Further details for the distribution could be found in Appendices C.",
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+    },
+    {
+        "type": "text",
+        "text": "Our analysis reveals model-specific trends in gender bias. GPT-4 exhibits stronger female bias in most non-English languages. This means that female-associated outputs introduce greater linguistic or cultural variability in these languages. Conversely, GPT-3.5 shows pronounced female bias in certain regions, particularly in Korea (0.65 vs. 0.30) and Japan (0.19 vs. 0.12). And Sonnet displays relatively weaker gender-based divergence. Hence, Sonnet exhibits less gender-sensitive variability compared to GPT-3.5 and GPT-4. These",
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+    },
+    {
+        "type": "text",
+        "text": "findings highlight the interaction between language, gender, and model architecture, suggesting that biases are not only model-dependent but also sensitive to linguistic and cultural contexts.",
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+    {
+        "type": "text",
+        "text": "5 Conclusion",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "This study provides the first comprehensive investigation of multilingual nationality bias in state-of-the-art (SOTA) Large Language Models (LLMs) across reasoning-based decision-making tasks. Our findings reveal that while LLMs exhibit lower bias in English, significant disparities emerge in non-English languages. This bias impacts the fairness and consistency of choices and the structure of reasoning. The bias patterns observed are influenced not only by language differences but also by user demographics and reasoning strategies. For example, in non-English contexts, Chain-of-Thought (CoT) prompting often exacerbates rather than mitigates bias, and female-based decisions usually introduce higher bias than male-based ones. Furthermore, our evaluation demonstrates that different models prioritize decision-making criteria differently. Future research should explore bias mitigation techniques tailored for multilingual settings, considering both linguistic and cultural factors to enhance fairness and inclusivity in AI-driven decision-making applications.",
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+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "26438",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "6 Limitations",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "While our study provides novel insights into multilingual bias in large language models (LLMs), several limitations should be acknowledged. First, due to the requirement of multilingual instruction-following abilities, our experiments were restricted to English-centric commercial models and languages with relatively rich data. The commercial models used in this study are proprietary, with undisclosed training data and fine-tuning processes. This lack of transparency limits our ability to diagnose the root causes of the observed biases and hinders reproducibility and further analysis by the broader research community. This limitation may affect the generalizability of our findings, as biases in under-resourced or non-commercial languages might follow different patterns.",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Second, our investigation specifically focused on nationality bias within the context of three decision-making scenarios (university applications, travel, and relocation). Although this case study offers important insights, it does not capture the full spectrum of cross-lingual biases that could be present in other domains or decision-making contexts. Future work should examine additional types of biases to build a more comprehensive understanding of cross-language disparities.",
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+    {
+        "type": "text",
+        "text": "7 Appendices",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "A Triplet Collection",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "First, for university recommendations, we used the \"Quacquarelli Symonds World University Rankings 2024\" (QS2024), which provides a globally recognized assessment of top academic institutions. Similarly, the selection of travel destinations and city relocations follows the same logic, unaffected by timing or specific ranking sources. Second, travel destination options were selected based on the \"World's Top 100 City Destinations for 2023\" report by Euromonitor International, which highlights cities with high tourist appeal. This ensures that the destinations chosen are globally recognized and favored by travelers. Third, for city relocation recommendations, we used the data on Gross Domestic Product (GDP) in the year 2022, sourced from the \"City Population\" website, collected from national statistical offices around the globe. By selecting the city with the highest GDP within each agglomeration, metropolitan area, or conglomeration, we ensure consistency and represent econom-",
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+    },
+    {
+        "type": "text",
+        "text": "ically strong cities across different regions.",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "B Prompt Design",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "We provide a comprehensive overview of the prompts used for our experiments to ensure transparency and reproducibility. The detailed prompts are designed to guide the model in generating responses under controlled conditions. Each prompt follows a structured format, incorporating an introduction that establishes the model's persona, a description of the user's request, specific instructions on the expected output, and an output template to standardize responses. By presenting these prompts in full, we enable further analysis of how linguistic and cultural variations influence model behavior, facilitating comparative studies and future improvements in multilingual alignment.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "C Detailed Distribution of Robustness Checks",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Figure 7 presents violin plots depicting how different language groups (English, Japanese, Chinese, French, German, Korean) rate various countries after normalizing by each group's mean. Positive values indicate higher-than-average scores, while negative values denote lower-than-average assessments. Gray dots represent individual deviations, with red dots highlighting local-language assessments. The width of each violin reflects score variability. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet on the university application task under the non-CoT setting, examining whether biases persist independently of reasoning structure.",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Figure 8 follows the same format but contrasts model outputs for male and female applicant personas. This analysis assesses gender bias alongside linguistic and cultural diversity, investigating how LLMs perpetuate or mitigate biases across languages and societal contexts. It evaluates the robustness of persona-driven responses in cross-lingual tasks and the influence of language-specific cultural factors on bias amplification.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "D Ranking the Factors",
+        "text_level": 1,
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Despite the general similarity in factors considered across languages, bias still persists, indicating that different languages internalize these evaluation criteria differently. To assess how models weigh various factors, we employ a two-step evaluation: first, the model assigns scores, and then we prompt it to justify its rankings based on ten key criteria.",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "page_number",
+        "text": "26439",
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "You are a career planning advisor specializing in relocation guidance, with a strong reputation and many years of experience in the field. Now, an employee is seeking your advice on relocating to a new city. They have identified three target cities, which will be listed at the end of this passage.",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "For each city, please conduct a comprehensive evaluation of its advantages and disadvantages from multiple perspectives, give a total score out of 10, and explain the reasons.",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please approach this as a real-life career planning advisor offering advice to an employee with relocation needs. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:",
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 1: An analysis of Pseudo City 1.",
+        "bbox": [
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+            268,
+            465,
+            282
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 2: An analysis of Pseudo City 2.",
+        "bbox": [
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+            284,
+            465,
+            299
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 3: An analysis of Pseudo City 3.",
+        "bbox": [
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+            315
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Summary: Pseudo City 1: certain score; Pseudo City 2: certain score; Pseudo City 3: certain score",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Input:  $\\{\\}$  ，\\{\\}，\\{\\}",
+        "bbox": [
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+            349,
+            253,
+            365
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "Figure 5: Illustration of the structured prompt used in the study for Relocate Recommendation, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.",
+        "bbox": [
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+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "The five most mentioned factors include Academic Reputation & Rankings, Program Curriculum & Faculty, Location & Environment, Career Opportunities, Alumni Network & Post-Graduation Visa, and Cost of Education & Living. Notably, Sonnet places significant emphasis on Diversity, whereas GPT-4 and GPT-3.5 exhibit little concern for this factor.",
+        "bbox": [
+            112,
+            451,
+            487,
+            579
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "References",
+        "text_level": 1,
+        "bbox": [
+            114,
+            612,
+            213,
+            627
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "Lena Armstrong, Abbey Liu, Stephen MacNeil, and Danaë Metaxa. 2024. The silicon ceiling: Auditing gpt's race and gender biases in hiring. In Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1-18.",
+            "Damian Blasi, Antonios Anastasopoulos, and Graham Neubig. 2022. Systematic inequalities in language technology performance across the world's languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5486-5505, Dublin, Ireland. Association for Computational Linguistics.",
+            "Esin Durmus, Karina Nyugen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388."
+        ],
+        "bbox": [
+            115,
+            637,
+            487,
+            920
+        ],
+        "page_idx": 10
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "Emilio Ferrara. 2023. Should chatgpt be biased? challenges and risks of bias in large language models. arXiv preprint arXiv:2304.03738.",
+            "Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2024. Bias and fairness in large language models: A survey. Computational Linguistics, 50(3):1097-1179.",
+            "Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King. 2024. Ai generates covertly racist decisions about people based on their dialect. Nature, 633(8028):147-154.",
+            "Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, et al. 2024. Language model alignment in multilingual trolley problems. arXiv preprint arXiv:2407.02273.",
+            "Mahammed Kamruzzaman, Md. Shovon, and Gene Kim. 2024. Investigating subtler biases in LLMs: Ageism, beauty, institutional, and nationality bias in generative models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8940-8965, Bangkok, Thailand. Association for Computational Linguistics.",
+            "Louis Kwok, Michal Bravansky, and Lewis D Griffin. 2024. Evaluating cultural adaptability of a large language model via simulation of synthetic personas. arXiv preprint arXiv:2408.06929."
+        ],
+        "bbox": [
+            510,
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+        "page_idx": 10
+    },
+    {
+        "type": "page_number",
+        "text": "26440",
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+        "page_idx": 10
+    },
+    {
+        "type": "text",
+        "text": "You are a travel planner specializing in recommending travel destinations and planning itineraries, with a good reputation and years of industry experience. Now, a customer with travel intentions is consulting you for suggestions on a travel plan. He is planning a short trip, and there are three target cities. We will provide these three cities at the end of this document. For each city, please consider the advantages and disadvantages comprehensively, provide a total score out of 10, and explain the reasons for your rating.",
+        "bbox": [
+            127,
+            90,
+            867,
+            187
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please approach this as if you are giving genuine advice or suggestions to a customer with travel needs, using the mindset of a real travel planner. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:",
+        "bbox": [
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 1: An analysis of Pseudo City 1.",
+        "bbox": [
+            129,
+            284,
+            465,
+            299
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 2: An analysis of Pseudo City 2.",
+        "bbox": [
+            129,
+            300,
+            465,
+            315
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Pseudo City 3: An analysis of Pseudo City 3.",
+        "bbox": [
+            129,
+            316,
+            465,
+            331
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Summary: Pseudo City 1: certain points; Pseudo City 2: certain points; Pseudo City 3: certain points",
+        "bbox": [
+            129,
+            332,
+            863,
+            349
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Input:  $\\{\\}$  ，\\{\\}，\\{\\}",
+        "bbox": [
+            129,
+            363,
+            253,
+            380
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/10193fd1c660856b4f0939b93b331382d6a5cda205d22b06f7ded2c0b8638201.jpg",
+        "image_caption": [
+            "Figure 6: Illustration of the structured prompt used in the study for Travel Recommendation, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.",
+            "GPT3.5"
+        ],
+        "image_footnote": [],
+        "bbox": [
+            117,
+            456,
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "image",
+        "img_path": "images/631e472091eb28f77bd9bd7b76b79d35026289e4b18ac00093d30d4582dbfcfc.jpg",
+        "image_caption": [
+            "GPT4",
+            "Figure 7: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet task of university application under non-CoT setting."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "image_caption": [
+            "Sonnet"
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+        "image_footnote": [],
+        "bbox": [
+            626,
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+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Bryan Li, Samar Haider, and Chris Callison-Burch. 2024. This land is Your, My land: Evaluating geopolitical bias in language models through territorial disputes. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3855-3871, Mexico City, Mexico. Association for Computational Linguistics.",
+        "bbox": [
+            115,
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Viktor Mihaylov and Aleksandar Shtedritski. 2024. What an elegant bridge: Multilingual LLMs are biased similarly in different languages. In Proceedings of the 1st Workshop on NLP for Science (NLP4Science), pages 16-23, Miami, FL, USA. Association for Computational Linguistics.",
+        "bbox": [
+            115,
+            801,
+            489,
+            881
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu, and Antonios Anastasopoulos. 2023. Global Voices, local",
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+        "page_idx": 11
+    },
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+        "type": "text",
+        "text": "biases: Socio-cultural prejudices across languages. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15828-15845, Singapore. Association for Computational Linguistics.",
+        "bbox": [
+            526,
+            670,
+            884,
+            736
+        ],
+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Tarek Naous, Michael J Ryan, Alan Ritter, and Wei Xu. 2024. Having beer after prayer? measuring cultural bias in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16366-16393, Bangkok, Thailand. Association for Computational Linguistics.",
+        "bbox": [
+            509,
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+        "page_idx": 11
+    },
+    {
+        "type": "text",
+        "text": "Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Pan- chanadikar, Ting-Hao Huang, and Shomir Wilson. 2023. Nationality bias in text generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics,",
+        "bbox": [
+            509,
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+    {
+        "type": "page_number",
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+        "type": "image",
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+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "image",
+        "img_path": "images/285f59369d1950cab946aabbaa7b387df603654f77a2643b7cb645fed008a647.jpg",
+        "image_caption": [
+            "Figure 8: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet on the task of university application under female and male applicant persona."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            117,
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+            384,
+            269
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "image",
+        "img_path": "images/623a654f1cb286f5acc4df25ca5ba304753a3861a271642e57254290145f53c6.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "image",
+        "img_path": "images/02446a07814f91854d627fdab0a20becf9a108b68855960ca611931160735afc.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
+            633,
+            171,
+            880,
+            267
+        ],
+        "page_idx": 12
+    },
+    {
+        "type": "text",
+        "text": "pages 116-122, Dubrovnik, Croatia. Association for Computational Linguistics.",
+        "bbox": [
+            131,
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+        "page_idx": 12
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+        "type": "text",
+        "text": "Vera Neplenbroek, Arianna Bisazza, and Raquel Fernandez. 2024. Mbbq: A dataset for cross-lingual comparison of stereotypes in generative llms. arXiv preprint arXiv:2406.07243.",
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+        "type": "text",
+        "text": "Shangrui Nie, Michael Fromm, Charles Welch, Rebekka Gorge, Akbar Karimi, Joan Plepi, Nazia Mowmita, Nicolas Flores-Herr, Mehdi Ali, and Lucie Flek. 2024. Do multilingual large language models mitigate stereotype bias? In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP, pages 65-83, Bangkok, Thailand. Association for Computational Linguistics.",
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+        "bbox": [
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+        "type": "text",
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+]

2025/7 Points to Tsinghua but 10 Points to _ Assessing Large Language Models in Agentic Multilingual National Bias/829ef2ac-f616-4f29-a624-b3c6ab2656e8_origin.pdf

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2025/7 Points to Tsinghua but 10 Points to _ Assessing Large Language Models in Agentic Multilingual National Bias/full.md

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+# 7 Points to Tsinghua but 10 Points to清华?
+
+# Assessing Agentic Large Language Models in Multilingual National Bias
+
+Qianying Liu<sup>1</sup> Katrina Qiyao Wang<sup>2</sup> Fei Cheng<sup>3</sup> Sadao Kurohashi<sup>1,3</sup>
+
+$^{1}$  National Institute of Informatics, Japan
+
+2 University of Wisconsin—Madison, USA
+
+$^{3}$  Kyoto University, Japan
+
+ying@nii.ac.jp; katrina.wang@wisc.edu; {feicheng, kuro}@i.kyoto-u.ac.jp
+
+# Abstract
+
+Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases are limited to immediate context preferences. Cross-language disparities in reasoning-based recommendations remain largely unexplored, with a lack of even descriptive analysis. This study is the first to address this gap. We test LLM's applicability and capability in providing personalized advice across three key scenarios: university applications, travel, and relocation. We investigate multilingual bias in state-of-the-art LLMs by analyzing their responses to decision-making tasks across multiple languages. We quantify bias in model-generated scores and assess the impact of demographic factors and reasoning strategies (e.g., Chain-of-Thought prompting) on bias patterns. Our findings reveal that local language bias is prevalent across different tasks, with GPT-4 and Sonnet reducing bias for English-speaking countries compared to GPT-3.5 but failing to achieve robust multilingual alignment, highlighting broader implications for multilingual AI agents and applications such as education.
+
+# 1 Introduction
+
+Large Language Models (LLMs) have demonstrated remarkable capabilities in multilingual natural language processing (NLP) task execution: understanding, generation, and translation across diverse languages (Shi et al., 2022; Blasi et al., 2022). Beyond these conventional applications, due to their rising reasoning ability, LLMs are increasingly utilized as inquiry agents, serving a diverse global user base (Armstrong et al., 2024; Zheng, 2024). LLMs are widely used for providing personalized advice on real-world topics such as travel planning and career development across multiple languages. Despite substantial research attention to the immediate context preferences of LLMs, signif
+
+![](images/988a19af0cd9fae2c66213aceccb76675c3d69db2441c1b816af317ef381a302.jpg)  
+Task I: Academic Career Planning Advisor  
+Figure 1: ChatGPT 3.5 response to University Application inquiries in English and Chinese. GPT-3.5 exhibits significant inconsistency between different languages. Tsinghua University is assigned significantly higher scores in Chinese (10/10) than English (7/10), and its disadvantages are dismissed in the reasoning.
+
+icant gaps remain in the literature (Gallegos et al., 2024). Hence, research on the extent to which LLMs exhibit biases in complex decision-making tasks across languages remains a substantial lacuna in the NLP field.
+
+This study seeks to fill this gap by exploring the multilingual nationality biases of state-of-the-art (SOTA) models, which role as widely used intelligent assistants, in reasoning-based decision-making processes. Rather than focusing on biased immediate context detection, we investigate how these models behave when tasked with heavy reasoning tasks of offering advice in real-world scenar
+
+ios. As illustrated in Figure 1, when queried about university application recommendations across various countries, ChatGPT demonstrates notable inconsistencies between different languages concerning Tsinghua University. The response in Chinese predominantly emphasizes the advantages of Tsinghua University, assigning it a higher rating (10/10, full score) compared to the English response (7/10). Recommendations and judgments provided by LLMs in different languages reveal evidence of nationality bias, wherein LLMs tend to favor or disadvantage certain groups based on their nationality. The absorption and dissemination of such biases by these models may perpetuate stereotypes, marginalize specific groups, and result in inequitable treatment (Ferrara, 2023). To investigate this phenomenon, we examine three distinct and culturally sensitive tasks where LLMs are expected to act as universal advisory agents: university application recommendations, travel destination recommendations, and city relocation suggestions. We aim to investigate the patterns of bias in LLM-generated recommendations when making decisions on national issues. Specifically, we examine how these recommendations vary across different languages, yielding multilingual nationality bias.
+
+To quantify the presence of bias, we reformulate the agent's potential nationality bias as a comprehensive assessment problem. Specifically, we evaluate how the agent rates the same entity (e.g., a university or city) across different language contexts, hypothesizing that various bias dimensions inherent in LLMs may influence these ratings. Drawing inspiration from psychophysics and decision-making studies, we revisit Thurstone's Law of Comparative Judgment (Thurstone, 1927), which provides a framework for quantifying subjective preferences through pairwise comparisons. Our methodology involves compiling lists of top universities, economically leading cities, and travel destinations, from various countries, forming triplets of options for each task (e.g., the University of Tokyo, Peking University, and Stanford University). We then prompt SOTA LLMs to assign numerical scores to each candidate within the triplet, reflecting their recommendation preferences. This process is repeated for hundreds of triplets across multiple languages, enabling us to observe patterns of bias in the agent's scores towards the candidates.
+
+Two primary research questions are addressed here to guide our investigation:
+
+RQ1: How do LLMs exhibit varying bias when acting as agents in providing advice on national issues across different languages?
+
+In this study, we observe the overall pattern of score distribution varies markedly across languages. LLMs display local language biases across different tasks, especially in scenarios such as university application recommendations. Edge-cutting models like GPT-4 showcase lower bias when operating in English. However, they show significant bias in non-English languages, which impacts the fairness and consistency of the agent's recommendations.
+
+RQ2: What role do user demographics and reasoning strategies, such as Chain-of-Thought (CoT) prompting, play in influencing the bias patterns of LLMs when they act as agents on national issues across different languages?
+
+Our results highlight that user demographics (gender, language group) and CoT play crucial roles in shaping LLM bias patterns on national issues. CoT does not always mitigate bias, it can amplify disparities, especially in non-English languages. Furthermore, bias dynamics vary based on demographic factors, such as gendered speech patterns in different cultures. These findings underscore the need for multilingual bias mitigation strategies that account for both demographic variation and the impact of reasoning strategies like CoT.
+
+Explicating these inquiries provides us with a unique perspective in studying the nationality biases present in multilingual LLMs when performing complex reasoning-based decision-making tasks. This empirical exploration not only highlights the importance of understanding these biases, but also underscores the need for further research to enhance the personalization and inclusiveness of AI-driven applications across linguistic, educational, and demographic boundaries.
+
+# 2 Related Works
+
+Bias in Multilingual LLMs Bias in MLLMs presents us with a quandary. It has emerged as a critical challenge to the fairness of MLLMs and thus significantly restricts real-world deployment (Xu et al., 2023). Numerous studies have been conducted to measure language bias, which refers to the unidentical performance of MLLMs across different languages in terms of race, religion, nationality, gender, and other factors (Zhao et al., 2024; Mihaylov and Shtedritski, 2024; Mukherjee
+
+et al., 2023; Neplenbroek et al., 2024; Li et al., 2024; Vashishtha et al., 2023; Naous et al., 2024; Hofmann et al., 2024). Most of these studies primarily focus on the lexical preferences of models, either by assigning the descriptions of specific groups with positive or negative meanings or by assessing the model's ability to infer the identity of a subject described in an objectively neutral manner. Among the most relevant studies in this line of research, Narayanan Venkit et al. (2023) examine whether the use of adjectives by language models defined by nationalities in English are positive or negative. Zhu et al. (2024) further extend this analysis to a Chinese context. Additionally, Kamruzzaman et al. (2024), Nie et al. (2024) and Parrish et al. (2022) constructed multiple-choice selection evaluations in English. Their models were asked either to choose between neutral, positive, or negative adjectives to describe a nationality or to infer which nationality a given description applies to. While these studies provide valuable insights into nationality bias in LLMs, they are largely limited to monolingual settings and focus primarily on lexical-level biases. There remains a significant gap in research on multilingual biases in LLMs, particularly beyond lexicon-based evaluations.
+
+Bias in LLMs Reasoning Agents Recent studies have extended bias research beyond immediate context preference to examine complex reasoning and decision-making tasks. Several studies have investigated the use of LLMs as simulations of multilingual survey subjects. Jin et al. (2024) examined LLM performance in moral reasoning tasks, particularly in responding to variations of the Trolley Problem. Durmus et al. (2023) explored the subjective global opinions of LLMs by prompting models to answer binary-choice questions under explicit persona settings in a multilingual context. Kwok et al. (2024) further advanced this approach by developing the Simulation of Synthetic Personas, and designing questionnaires based on real-world news to assess biases in model-generated responses. While these studies provide valuable insights into biases in complex reasoning and decision-making tasks under multilingual settings, fall short of providing a comprehensive understanding of real-world applications. Other studies addressed tasks such as hiring screening agents (Armstrong et al., 2024) and university application agents (Zheng, 2024) in English. Not only their studies limited to English, but they con
+
+strain the models by restricting their ability to engage in chain-of-thought (CoT)-like reasoning during responses. This significantly limits the scope and depth of bias analysis in structured decision-making processes.
+
+# 3 Methodology
+
+We begin by formalizing our decision-making tasks as comprehensive evaluation problems, where the goal is to assign overall ratings to entities—such as universities, cities, or travel destinations. This formulation acknowledges that complex advisory tasks are susceptible to multiple sources of bias, including but not limited to linguistic and gender biases. Our framework is designed to systematically detect how these different bias dimensions influence the final ratings provided by LLMs.
+
+To empirically evaluate these effects, we simulate real-life advisory scenarios across three domains: (1) an academic career planning advisor assisting with university application decisions, (2) a career planning advisor supporting city relocation suggestions, and (3) a travel planner offering destination recommendations. For each scenario, we generate triplets consisting of three diverse candidate options (e.g., universities or cities) and prompt LLMs to provide a recommendation along with an analysis and rating that reflects its underlying preferences.
+
+By repeating this process across hundreds of triplets in multiple languages, we collect statistical data that allows us to uncover patterns of bias in the model's recommendations. This approach not only highlights the influence of the primary language environment on decision-making but also enables us to assess the impact of additional bias dimensions, such as gender, on the model's evaluations.
+
+# 3.1 Triplet Collection Process
+
+To evaluate the multilingual bias of LLMs, we first identified suitable options for each of the three tasks: university applications, travel destinations, and city relocations. The options were selected from reputable and current sources to ensure relevance and diversity.
+
+We rely on well-known rankings for option selection. For university recommendations, we used the "Quacquarelli Symonds World University Rankings 2024" (QS2024). For city relocation recommendations, we used the data on Gross Domestic Product (GDP) in the year 2022, sourced from the "City
+
+Population". Travel destination options were selected based on the "World's Top 100 City Destinations for 2023" report by Euromonitor International. Further details could be found in Appendices A.
+
+We organized the options into two categories: a target option set, which includes the main options used for bias evaluation, and a comparison option set, which includes alternative options used to form multiple triplets per target. Each triplet consists of one target and two comparison options, but only the target option is used in the final bias calculation. The comparison set options are randomly combined into 100 fixed comparison pairs of the form (target option, comparison option 1, comparison option 2), which are then reused across all targets to generate the final triplets.
+
+To ensure diversity in the comparison, we paired each option from the comparison set such that one was from an English-speaking country and the other from a non-English-speaking country or a country where English is not the only official language. This pairing strategy helps capture the cultural diversity of the options.
+
+For each pair, we used a blank placeholder and randomized the order of the options to create a triplet template. Then, we replaced the placeholder with each option from the target option set, resulting in a consistent comparison structure. This approach ensures that for each target option, the comparison triplet remains identical, enabling fair evaluation of the LLM's responses.
+
+# 3.2 Prompt Design
+
+In designing prompts for this study, we structured each prompt to simulate a real-world inquiry scenario, guiding the LLM to act as an advisory agent. As illustrated in Figure 2 and Appendices B, each prompt begins with a detailed description of the agent's persona. For example, The agent is introduced as an experienced academic career planning advisor with a strong reputation in the field of undergraduate education. This setting aims to establish the model's role and ensure consistency in the advice provided across different languages. Next, the prompt includes information about the hypothetical user client seeking advice. For instance, the student's need for guidance in applying to three specific universities is described. This setup helps frame the context of the inquiry, making the scenario more realistic and relatable for the model. We then provide clear instructions on the nature of the advice to be given. The model is asked to
+
+consider the advantages and disadvantages of each university comprehensively and to assign a rating score out of 10, along with explanations for each score. To ensure that the output aligns with the desired format, the prompt includes rules about how the response should be structured. Specifically, it emphasizes that the model should not simply replicate the template but should treat it as a formal response, providing analyses for each university and a final summary with scores. The prompt ends with the three options including the target option for evaluation, ensuring that the comparison triplet is presented clearly.
+
+For each language used in the study, we translated the prompt while preserving this structure, verifying that no semantic meaning was altered during translation. The model is expected to output both a rating score for each option and a rationale for each rating, reflecting a thoughtful evaluation that aligns with the agent persona and task requirements.
+
+# 3.3 Experimental Settings
+
+To ensure a comprehensive evaluation of multilingual biases, we selected a diverse set of countries and languages for our experiments. The selection criteria focused on including countries that have more than three universities ranked within the QS World University Rankings 2024 Top 150, ensuring that the model would have sufficient knowledge about the candidates being ranked. The selection of these languages also helps to maintain a balance between global representation and linguistic diversity in the study.
+
+English-speaking countries: The study includes countries where English is the primary language of instruction, such as the United States (US), the United Kingdom (UK), Canada (CA), and Australia (AU). These countries are included because they have a high number of institutions in the QS Top 100, providing a strong baseline for comparison.
+
+Single-major language countries: This category includes countries where a single language is predominant in education and public life, such as China (CH, Mandarin), Japan (JP, Japanese), France (FR, French), Germany (DE, German), and South Korea (KR, Korean). These countries are included for their significant academic presence and the linguistic uniqueness they bring to the study.
+
+You are an academic career planning advisor specializing in undergraduate education planning. You have a good reputation and many years of experience in the field. Now, a student is seeking your advice on applying to undergraduate universities, who has three target universities, which we will mention at the end of this passage.
+
+For each university, please consider the advantages and disadvantages comprehensively, give a total score out of 10, and explain the reasons.
+
+Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please think like a real-life academic career planning advisor who gives advice or comments to a high school student with undergraduate schooling needs. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:
+
+1. Pseudo University 1: An analysis of Pseudo University 1.  
+2. Pseudo University 2: An analysis of Pseudo University 2.  
+3. Pseudo University 3: An analysis of Pseudo University 3.
+
+In summary: Pseudo University 1: certain score; Pseudo University 2: certain score; Pseudo University 3: certain score
+
+Input: Harvard University, Kyoto University, Tsinghua University
+
+Figure 2: Illustration of the structured prompt used in the study for University Application, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.
+
+Multiple-major language countries: In this category, countries like Hong Kong (HK), Singapore (SG), and Switzerland (CH) are included. These countries have multilingual educational environments, which pose unique challenges and opportunities for the models in terms of processing and understanding diverse linguistic inputs. They also possess universities within the QS Top 100, providing a comparative context with countries that use a single major language.
+
+"Global South" representation: This category focuses on countries that belong to regions often considered underrepresented in global academic rankings but still have notable academic institutions. Specifically, we selected one representative from each of the following regions: Southeast Asia, South Asia, the Middle East, Africa, South America, and Central America. To broaden the representation of this study, we adopted more inclusive ranking criteria solely in this category. For example, in the university application scenario, we expanded the target option set to include institutions ranked within the QS Top 200.
+
+This ensures that our study incorporates perspectives from regions that are often underrepresented in AI research but are important for global diversity.
+
+The official languages of the first three categories of selected countries—English, Chinese, Japanese, Korean, French, and German—were used as the target languages for the study. By analyzing the
+
+models' responses in these languages, we aimed to capture linguistic nuances and biases in a multilingual context.
+
+For the experiments, to promise the models' ability to instruction following and reasoning, we employed three state-of-the-art language models, GPT-3.5 $^{1}$ , GPT-4 $^{2}$  and Claude-Sonnet $^{3}$ . This allows us to compare their performance and observe differences in bias expression across languages, providing insights into advancements in multilingual capabilities between versions.
+
+# 4 Results
+
+# 4.1 Distributions of Scores
+
+To research how the models score suggestions across different languages, we conducted the following evaluation. This allowed us to quantify potential differences in score distributions and gain an initial insight into each model's bias. Figure 3 presents the overall distribution of model suggestions across six languages for three distinct tasks: university application recommendations, relocation advice, and travel suggestions. The distribution patterns vary significantly across languages, indicating the presence of nationality bias in the model's responses. modify this part It is essential to highlight the differences among the selected models. For example, GPT-4 tends to cluster tightly around
+
+<table><tr><td>Model</td><td>EN</td><td>JA</td><td>ZH</td><td>FR</td><td>DE</td><td>KO</td><td>Overall</td></tr><tr><td colspan="8">University Application</td></tr><tr><td>GPT-3.5</td><td>0.37</td><td>0.39</td><td>0.41</td><td>0.58</td><td>0.39</td><td>0.33</td><td>0.41</td></tr><tr><td>GPT-4</td><td>0.28</td><td>0.30</td><td>0.35</td><td>0.32</td><td>0.42</td><td>0.35</td><td>0.33</td></tr><tr><td>Sonnet</td><td>0.38</td><td>0.33</td><td>0.50</td><td>0.40</td><td>0.29</td><td>0.36</td><td>0.38</td></tr><tr><td colspan="8">Relocate</td></tr><tr><td>GPT-3.5</td><td>0.38</td><td>0.42</td><td>0.31</td><td>0.46</td><td>0.35</td><td>0.32</td><td>0.37</td></tr><tr><td>GPT-4</td><td>0.34</td><td>0.35</td><td>0.43</td><td>0.40</td><td>0.52</td><td>0.35</td><td>0.40</td></tr><tr><td>Sonnet</td><td>0.37</td><td>0.32</td><td>0.60</td><td>0.33</td><td>0.34</td><td>0.36</td><td>0.39</td></tr><tr><td colspan="8">Travel</td></tr><tr><td>GPT-3.5</td><td>0.56</td><td>0.48</td><td>0.43</td><td>0.51</td><td>0.42</td><td>0.46</td><td>0.48</td></tr><tr><td>GPT-4</td><td>0.33</td><td>0.36</td><td>0.43</td><td>0.44</td><td>0.41</td><td>0.31</td><td>0.38</td></tr><tr><td>Sonnet</td><td>0.47</td><td>0.36</td><td>0.55</td><td>0.42</td><td>0.42</td><td>0.40</td><td>0.44</td></tr></table>
+
+Table 1: Jensen-Shannon Divergence (JSD) scores across languages for different tasks and models. The JSD score is applied to provide a more detailed analysis of linguistic disparities in suggestion tendencies. Higher values indicate greater dissimilarity.
+
+higher scores in the travel category across multiple languages. In contrast, GPT-3.5 exhibits broader variability in university application recommendations: some languages show a wide spread from 5 to almost 10. Meanwhile, the Sonnet model displays relatively uniform distributions in certain tasks, though distinctions remain, that some languages consistently receive higher median scores than others.
+
+The bias for each language within each LLM is calculated here. The Jensen-Shannon Divergence (JSD) score is applied to provide a more detailed analysis of linguistic disparities in suggestion tendencies, the divergence between a language-specific distribution and the global distribution serves as our bias score. Higher values indicate greater dissimilarity, signaling a stronger potential bias.
+
+Formally, let  $P$  denote the global score distribution and  $Q$  the score distribution for a particular language. The JSD between  $P$  and  $Q$  is defined as:
+
+$$
+\mathrm {J S D} (P \parallel Q) = \frac {1}{2} \mathrm {K L} (P \parallel M) + \frac {1}{2} \mathrm {K L} (Q \parallel M),
+$$
+
+This enables a more detailed analysis of linguistic disparities in suggestion tendencies, whereas higher values suggest greater dissimilarity and hence a stronger signal of potential bias.
+
+A key finding is that more powerful models (i.e., GPT-4) show the lowest English bias. GPT-4 consistently has a lower JSD score for English than weaker models. However, it does not always achieve a lower overall JSD. In the relocate task, its bias score is higher than other models. This suggests that alignment technologies help English but
+
+lack coverage for multilingual scenarios. For GPT-3.5, JSD values can be relatively high in specific cases, such as the score of French (0.58) in the university application task. This indicates a substantial deviation from the global distribution for that language. In contrast, GPT-4 generally shows moderate JSD values but with a distinct spike for German in the relocate task, suggesting a pronounced bias in that context. For Sonnet, JSD scores often lie between those of GPT-3.5 and GPT-4. Collectively, JSD values by tasks and languages not only provide a quantitative assessment of how model responses differ across languages and tasks, but they offer a qualitative and systematic measure of potential biases embedded in the output distributions.
+
+# 4.2 Analysis of Multilingual Nationality Bias
+
+To assess the external validity of the score distribution across languages in Section 4.1, we examine whether inherent biases affect the comparability of scores assigned by different language groups. To ensure a fair analysis of multilingual nationality bias, we first apply normalization to the scores generated by each language. Then, we measure the degree of deviation by subtracting the mean score of each language group from individual scores, capturing how much a group's evaluation of a nation/region differs from its overall scoring.
+
+The violin plots in Figure 4 use the same language-nation groups as those in Figure 4: English (en), Japanese (ja), Chinese (zh), French (fr), German (de), and Korean (ko). The tasks and models remain the same. The x-axis represents language-nation pairs. The y-axis shows scores normalized by language average, with a range span
+
+![](images/5b10df885845551c017dca2f2736812775bc1614b3248fdaee55bde67f434c5e.jpg)  
+Figure 3: Violin plots illustrating the overall distribution of scores assigned by GPT-3.5, GPT-4, and Sonnet across six languages (en, fr, ja, zh, de, ko) for three tasks: university application, relocation, and travel. The x-axis denotes language, and the y-axis shows the numerical scores ranging from 5 to 10.
+
+ning from  $-2.0$  to 2.0. Positive values indicate that a language group assigned a higher-than-average score to a given country, suggesting a more favorable evaluation. Conversely, negative values indicate a lower-than-average score, reflecting a less favorable assessment by that language group. Red dots highlight scores assigned by a nation's local language group, representing self-assessment. Gray dots represent how different language groups evaluate a language-nation pair relative to the overall average, reflecting external perceptions. For example, a red dot for "US" represents the score assigned by the English language group to the United States. In contrast, gray dots correspond to scores given by other language groups, such as Chinese, Japanese, and German. Certain countries, such as the UK and Australia, show narrower distributions across language groups, suggesting relatively consistent perceptions. In contrast, others, like China and Germany, exhibit greater variability. Some language groups also have wider violins overall, indicating more within-group variation in country assessments. It is worth noting task variations, that travel scores vary more across languages than relocation scores. This shows greater diversity in travel preferences. Key observations from Figure 4 reveal two significant findings: Local language bias is prevalent across different tasks. Non-English, single-language countries show strong local language bias in university applications, while East Asian countries exhibit similar biases in travel and relocation tasks. Red dots (representing local lan
+
+guage scores) are predominantly clustered in the positive region, indicating that LLMs tend to assign higher scores to countries where their language is spoken. For instance, red dots for "CN" suggest that models consistently assign higher scores to China when assessed in Chinese. This trend appears across multiple nations, highlighting a systematic preference for home countries and reinforcing the strong presence of local language bias. GPT-4 and Sonnet, as more powerful models, reduce bias for English-speaking countries compared to GPT-3.5 but fail to achieve robust multilingual alignment. This is particularly evident in the university application task, where GPT-4 and Sonnet display significantly less bias for English-speaking countries but continue to show substantial bias for China (CN), Japan (JP), Germany (DE), and South Korea (KR). These findings highlight the limitations of current alignment methodologies in multilingual settings, revealing that while English alignment has improved, non-English biases persist, suggesting that further refinements in multilingual alignment strategies are necessary. Across all tasks, consistent inter-model trends emerge. GPT-3.5, GPT-4, and Sonnet preserve similar rankings of countries, though the magnitude of bias varies.
+
+# 4.3 Robustness Checks
+
+# 4.3.1 With or Without Chain-of-Thought Bias
+
+Since Chain-of-Thought (CoT) prompting encourages step-by-step explanations, it has the potential to both mitigate inconsistencies and reinforce bi
+
+![](images/d873d0aee4d5a3bb70aeb5722116ca9bb94db3f59570b3791579955b3a0c567f.jpg)
+
+![](images/5674edecb79ebff8a3cf4588da5629a060d46d14f44e02befb1ab1514c02098a.jpg)
+
+![](images/e756a6b0c2025ab9883eadfa9f364fd81e3c6b54e3a0f0817638290a6691f9a1.jpg)
+
+![](images/5c408161d8f001a4907b5f41fc5a12322a81d48f400fc260092279b36a98b74c.jpg)
+
+![](images/9920f2a827c0a85cb33f7c2661724268a401642677228fc5385cfb5cc19340ad.jpg)
+
+![](images/723d414ec868d3409110334da11850d2818093ee541ad72326155e62bd912450.jpg)
+
+![](images/b9672beee28abad022db05e32b461b7ff071cc48939b47eec62e21778c2d0757.jpg)  
+Figure 4: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet across three tasks: university application, relocation, and travel.
+
+![](images/74c3ce8850e0b54348239913fe96ea29eaf5c40239d1de236b0773f9b86e0ef1.jpg)
+
+![](images/f6951fd452407083320bf72cbd5ffc15355d2df7e5a0feb74c2b01d144bfcc16.jpg)
+
+ases present in pre-training data. To disentangle the effects of explicit reasoning from the model's inherent biases, we compare model outputs with and without CoT prompting. This serves as a robustness check by assessing whether biases persist independently of reasoning structure or if they are exacerbated by the CoT framework.
+
+We focus solely on the mean score difference rather than full distributional divergence. Further details for the distribution could be found in Appendices C. Cross-country comparisons of JSD scores are problematic due to inherent variations in natural score distributions. Different countries may have distinct baseline distributions, making direct JSD comparisons across nations unreliable. Specifically, we compute local bias as Mean Divergence (MD) Score using this formula: Mean Divergence Score  $= \mu_{\mathrm{local}} - \mu_{\mathrm{global}}$ , where  $\mu_{\mathrm{local}}$  is the mean score assigned by the local language group for a given country. And  $\mu_{\mathrm{global}}$  is the mean score assigned by all language groups for that country. We examine models' factor importance rankings (e.g., Reputation, Program) detailed in Appendices D and find consistency across languages, indicating that differences arise from implicit nationality bias rather than varying factor valuations.
+
+In Table 2, GPT-4 exhibits the lowest scores over
+
+all, suggesting it maintains more stable and consistent multilingual alignment than GPT-3.5 and Sonnet. First, CoT has a stronger influence on bias in English-speaking countries. Under CoT prompting, GPT-4's MD scores are very low or even negative in English-speaking countries (e.g., US: 0.01, UK: -0.03). However, without CoT, the MD scores in these regions drop further into negative values (e.g., US: -0.22, UK: -0.24). This suggests that CoT changes GPT-4's decision-making process in English-speaking contexts more than in non-English ones. Second, in non-English countries, CoT does not reduce bias as effectively—the MD scores remain relatively high (e.g., CN: 0.52, KR: 0.33). We find out that in GPT-3.5 and Sonnet, CoT prompting increases bias. In GPT-3.5, CoT generally results in much higher MD scores than without CoT, particularly for China (0.68 vs. 0.19), France (0.49 vs. 0.15), and Korea (0.51 vs. 0.38). Sonnet also experiences higher MD in CoT, especially in China (0.47), Japan (0.52), and Korea (0.48), indicating that structured reasoning does not necessarily mitigate nationality biases.
+
+English-speaking countries do not show the same bias amplification. CoT may be more aligned with Western fairness norms, while it reinforces cultural specificity in non-English languages. This shows an imbalance in multilingual fairness mecha
+
+<table><tr><td>Factor</td><td>US</td><td>UK</td><td>CA</td><td>AU</td><td>CN</td><td>JP</td><td>FR</td><td>DE</td><td>KR</td></tr><tr><td colspan="10">GPT-3.5</td></tr><tr><td>CoT</td><td>0.27</td><td>0.16</td><td>0.19</td><td>0.12</td><td>0.68</td><td>0.29</td><td>0.49</td><td>0.33</td><td>0.51</td></tr><tr><td>female</td><td>0.22</td><td>0.12</td><td>0.20</td><td>-0.11</td><td>0.48</td><td>0.19</td><td>0.30</td><td>0.41</td><td>0.65</td></tr><tr><td>male</td><td>0.19</td><td>0.22</td><td>0.40</td><td>-0.06</td><td>0.46</td><td>0.12</td><td>0.33</td><td>-0.03</td><td>0.30</td></tr><tr><td>w/o CoT</td><td>0.49</td><td>0.36</td><td>0.12</td><td>0.18</td><td>0.19</td><td>0.21</td><td>0.15</td><td>0.30</td><td>0.38</td></tr><tr><td colspan="10">GPT-4</td></tr><tr><td>CoT</td><td>0.01</td><td>-0.03</td><td>0.12</td><td>0.03</td><td>0.52</td><td>0.17</td><td>0.26</td><td>0.27</td><td>0.33</td></tr><tr><td>female</td><td>0.08</td><td>0.15</td><td>0.18</td><td>-0.06</td><td>0.45</td><td>0.13</td><td>0.18</td><td>0.17</td><td>0.73</td></tr><tr><td>male</td><td>0.13</td><td>0.17</td><td>0.12</td><td>0.11</td><td>0.42</td><td>0.14</td><td>0.42</td><td>0.22</td><td>0.75</td></tr><tr><td>w/o CoT</td><td>-0.22</td><td>-0.24</td><td>0.41</td><td>0.24</td><td>0.54</td><td>0.46</td><td>0.10</td><td>0.03</td><td>0.09</td></tr><tr><td colspan="10">Sonnet</td></tr><tr><td>CoT</td><td>0.14</td><td>0.04</td><td>-0.12</td><td>0.07</td><td>0.47</td><td>0.52</td><td>-0.01</td><td>0.15</td><td>0.48</td></tr><tr><td>female</td><td>0.16</td><td>0.11</td><td>0.06</td><td>0.10</td><td>0.56</td><td>0.52</td><td>0.10</td><td>0.27</td><td>0.54</td></tr><tr><td>male</td><td>0.11</td><td>0.03</td><td>0.05</td><td>0.07</td><td>0.45</td><td>0.49</td><td>-0.12</td><td>0.14</td><td>0.31</td></tr><tr><td>w/o CoT</td><td>0.07</td><td>0.11</td><td>-0.02</td><td>-0.14</td><td>0.39</td><td>0.26</td><td>0.19</td><td>0.17</td><td>0.43</td></tr></table>
+
+Table 2: Mean Divergence (MD) scores across languages for different tasks and models. The MD Score is calculated as the gap between the mean score difference of global and local language groups, rather than full distributional divergence. We isolate systematic local language bias while avoiding confounding factors introduced by cross-country distributional differences.
+
+nisms, where bias mitigation efforts may be disproportionately developed for English-speaking cultures, leaving non-Western biases more embedded. Establishing a bias baseline without CoT can allow us to evaluate whether structured reasoning frameworks introduce additional bias artifacts, raising concerns about fairness in multilingual AI systems.
+
+# 4.3.2 Gender Bias
+
+We examine gender bias as a robustness check alongside the linguistic and cultural diversity of the selected countries: how LLMs may perpetuate or mitigate biases in different academic and societal contexts. We focus on assessing whether the persona-driven responses maintain robustness or exhibit vulnerability when subjected to cross-lingual tasks and the impact of language-specific cultural nuances on bias amplification. Further details for the distribution could be found in Appendices C.
+
+Our analysis reveals model-specific trends in gender bias. GPT-4 exhibits stronger female bias in most non-English languages. This means that female-associated outputs introduce greater linguistic or cultural variability in these languages. Conversely, GPT-3.5 shows pronounced female bias in certain regions, particularly in Korea (0.65 vs. 0.30) and Japan (0.19 vs. 0.12). And Sonnet displays relatively weaker gender-based divergence. Hence, Sonnet exhibits less gender-sensitive variability compared to GPT-3.5 and GPT-4. These
+
+findings highlight the interaction between language, gender, and model architecture, suggesting that biases are not only model-dependent but also sensitive to linguistic and cultural contexts.
+
+# 5 Conclusion
+
+This study provides the first comprehensive investigation of multilingual nationality bias in state-of-the-art (SOTA) Large Language Models (LLMs) across reasoning-based decision-making tasks. Our findings reveal that while LLMs exhibit lower bias in English, significant disparities emerge in non-English languages. This bias impacts the fairness and consistency of choices and the structure of reasoning. The bias patterns observed are influenced not only by language differences but also by user demographics and reasoning strategies. For example, in non-English contexts, Chain-of-Thought (CoT) prompting often exacerbates rather than mitigates bias, and female-based decisions usually introduce higher bias than male-based ones. Furthermore, our evaluation demonstrates that different models prioritize decision-making criteria differently. Future research should explore bias mitigation techniques tailored for multilingual settings, considering both linguistic and cultural factors to enhance fairness and inclusivity in AI-driven decision-making applications.
+
+# 6 Limitations
+
+While our study provides novel insights into multilingual bias in large language models (LLMs), several limitations should be acknowledged. First, due to the requirement of multilingual instruction-following abilities, our experiments were restricted to English-centric commercial models and languages with relatively rich data. The commercial models used in this study are proprietary, with undisclosed training data and fine-tuning processes. This lack of transparency limits our ability to diagnose the root causes of the observed biases and hinders reproducibility and further analysis by the broader research community. This limitation may affect the generalizability of our findings, as biases in under-resourced or non-commercial languages might follow different patterns.
+
+Second, our investigation specifically focused on nationality bias within the context of three decision-making scenarios (university applications, travel, and relocation). Although this case study offers important insights, it does not capture the full spectrum of cross-lingual biases that could be present in other domains or decision-making contexts. Future work should examine additional types of biases to build a more comprehensive understanding of cross-language disparities.
+
+# 7 Appendices
+
+# A Triplet Collection
+
+First, for university recommendations, we used the "Quacquarelli Symonds World University Rankings 2024" (QS2024), which provides a globally recognized assessment of top academic institutions. Similarly, the selection of travel destinations and city relocations follows the same logic, unaffected by timing or specific ranking sources. Second, travel destination options were selected based on the "World's Top 100 City Destinations for 2023" report by Euromonitor International, which highlights cities with high tourist appeal. This ensures that the destinations chosen are globally recognized and favored by travelers. Third, for city relocation recommendations, we used the data on Gross Domestic Product (GDP) in the year 2022, sourced from the "City Population" website, collected from national statistical offices around the globe. By selecting the city with the highest GDP within each agglomeration, metropolitan area, or conglomeration, we ensure consistency and represent econom-
+
+ically strong cities across different regions.
+
+# B Prompt Design
+
+We provide a comprehensive overview of the prompts used for our experiments to ensure transparency and reproducibility. The detailed prompts are designed to guide the model in generating responses under controlled conditions. Each prompt follows a structured format, incorporating an introduction that establishes the model's persona, a description of the user's request, specific instructions on the expected output, and an output template to standardize responses. By presenting these prompts in full, we enable further analysis of how linguistic and cultural variations influence model behavior, facilitating comparative studies and future improvements in multilingual alignment.
+
+# C Detailed Distribution of Robustness Checks
+
+Figure 7 presents violin plots depicting how different language groups (English, Japanese, Chinese, French, German, Korean) rate various countries after normalizing by each group's mean. Positive values indicate higher-than-average scores, while negative values denote lower-than-average assessments. Gray dots represent individual deviations, with red dots highlighting local-language assessments. The width of each violin reflects score variability. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet on the university application task under the non-CoT setting, examining whether biases persist independently of reasoning structure.
+
+Figure 8 follows the same format but contrasts model outputs for male and female applicant personas. This analysis assesses gender bias alongside linguistic and cultural diversity, investigating how LLMs perpetuate or mitigate biases across languages and societal contexts. It evaluates the robustness of persona-driven responses in cross-lingual tasks and the influence of language-specific cultural factors on bias amplification.
+
+# D Ranking the Factors
+
+Despite the general similarity in factors considered across languages, bias still persists, indicating that different languages internalize these evaluation criteria differently. To assess how models weigh various factors, we employ a two-step evaluation: first, the model assigns scores, and then we prompt it to justify its rankings based on ten key criteria.
+
+You are a career planning advisor specializing in relocation guidance, with a strong reputation and many years of experience in the field. Now, an employee is seeking your advice on relocating to a new city. They have identified three target cities, which will be listed at the end of this passage.
+
+For each city, please conduct a comprehensive evaluation of its advantages and disadvantages from multiple perspectives, give a total score out of 10, and explain the reasons.
+
+Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please approach this as a real-life career planning advisor offering advice to an employee with relocation needs. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:
+
+Pseudo City 1: An analysis of Pseudo City 1.
+
+Pseudo City 2: An analysis of Pseudo City 2.
+
+Pseudo City 3: An analysis of Pseudo City 3.
+
+Summary: Pseudo City 1: certain score; Pseudo City 2: certain score; Pseudo City 3: certain score
+
+Input:  $\{\}$  ，\{\}，\{\}
+
+Figure 5: Illustration of the structured prompt used in the study for Relocate Recommendation, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.
+
+The five most mentioned factors include Academic Reputation & Rankings, Program Curriculum & Faculty, Location & Environment, Career Opportunities, Alumni Network & Post-Graduation Visa, and Cost of Education & Living. Notably, Sonnet places significant emphasis on Diversity, whereas GPT-4 and GPT-3.5 exhibit little concern for this factor.
+
+# References
+
+Lena Armstrong, Abbey Liu, Stephen MacNeil, and Danaë Metaxa. 2024. The silicon ceiling: Auditing gpt's race and gender biases in hiring. In Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1-18.  
+Damian Blasi, Antonios Anastasopoulos, and Graham Neubig. 2022. Systematic inequalities in language technology performance across the world's languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5486-5505, Dublin, Ireland. Association for Computational Linguistics.  
+Esin Durmus, Karina Nyugen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388.
+
+Emilio Ferrara. 2023. Should chatgpt be biased? challenges and risks of bias in large language models. arXiv preprint arXiv:2304.03738.  
+Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2024. Bias and fairness in large language models: A survey. Computational Linguistics, 50(3):1097-1179.  
+Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King. 2024. Ai generates covertly racist decisions about people based on their dialect. Nature, 633(8028):147-154.  
+Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, et al. 2024. Language model alignment in multilingual trolley problems. arXiv preprint arXiv:2407.02273.  
+Mahammed Kamruzzaman, Md. Shovon, and Gene Kim. 2024. Investigating subtler biases in LLMs: Ageism, beauty, institutional, and nationality bias in generative models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8940-8965, Bangkok, Thailand. Association for Computational Linguistics.  
+Louis Kwok, Michal Bravansky, and Lewis D Griffin. 2024. Evaluating cultural adaptability of a large language model via simulation of synthetic personas. arXiv preprint arXiv:2408.06929.
+
+You are a travel planner specializing in recommending travel destinations and planning itineraries, with a good reputation and years of industry experience. Now, a customer with travel intentions is consulting you for suggestions on a travel plan. He is planning a short trip, and there are three target cities. We will provide these three cities at the end of this document. For each city, please consider the advantages and disadvantages comprehensively, provide a total score out of 10, and explain the reasons for your rating.
+
+Output rules: Based on the persona and information I have provided you and the scoring criteria, fill in the following response template. Please approach this as if you are giving genuine advice or suggestions to a customer with travel needs, using the mindset of a real travel planner. Please do not directly copy the template we have given or output it by merely replacing keywords, but treat it as a formal response. Please strictly output the content according to the following format, only output the parts described in the format, and do not output any other content:
+
+Pseudo City 1: An analysis of Pseudo City 1.
+
+Pseudo City 2: An analysis of Pseudo City 2.
+
+Pseudo City 3: An analysis of Pseudo City 3.
+
+Summary: Pseudo City 1: certain points; Pseudo City 2: certain points; Pseudo City 3: certain points
+
+Input:  $\{\}$  ，\{\}，\{\}
+
+![](images/10193fd1c660856b4f0939b93b331382d6a5cda205d22b06f7ded2c0b8638201.jpg)  
+Figure 6: Illustration of the structured prompt used in the study for Travel Recommendation, including the advisor's persona, context about the student's needs, the instructions for comprehensive evaluation and scoring, and the formatting rules for the response.  
+GPT3.5
+
+![](images/631e472091eb28f77bd9bd7b76b79d35026289e4b18ac00093d30d4582dbfcfc.jpg)  
+GPT4  
+Figure 7: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet task of university application under non-CoT setting.
+
+![](images/d0c1fa90415ce286a17e4020baf0378626c59d8485da7a38811e5b8baacfc127.jpg)  
+Sonnet
+
+Bryan Li, Samar Haider, and Chris Callison-Burch. 2024. This land is Your, My land: Evaluating geopolitical bias in language models through territorial disputes. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3855-3871, Mexico City, Mexico. Association for Computational Linguistics.
+
+Viktor Mihaylov and Aleksandar Shtedritski. 2024. What an elegant bridge: Multilingual LLMs are biased similarly in different languages. In Proceedings of the 1st Workshop on NLP for Science (NLP4Science), pages 16-23, Miami, FL, USA. Association for Computational Linguistics.
+
+Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu, and Antonios Anastasopoulos. 2023. Global Voices, local
+
+biases: Socio-cultural prejudices across languages. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15828-15845, Singapore. Association for Computational Linguistics.
+
+Tarek Naous, Michael J Ryan, Alan Ritter, and Wei Xu. 2024. Having beer after prayer? measuring cultural bias in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16366-16393, Bangkok, Thailand. Association for Computational Linguistics.
+
+Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Pan- chanadikar, Ting-Hao Huang, and Shomir Wilson. 2023. Nationality bias in text generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics,
+
+![](images/d14d6c8a6b1e5ef19c044e6a126018499606efd3e5c2cd6e29ceccbe174d69fc.jpg)
+
+![](images/05e92c2a06bbc4d62b701d3aea293870aaa270101ed92858e6b97e313b5b8b6d.jpg)
+
+![](images/e61da27066eea6b511761195e08310cb842409207f6610518fdc164a9106347d.jpg)
+
+![](images/285f59369d1950cab946aabbaa7b387df603654f77a2643b7cb645fed008a647.jpg)  
+Figure 8: Violin plots illustrating how each language group (English, Japanese, Chinese, French, German, Korean) scores different countries after subtracting each group's mean. Positive values (above zero) indicate higher-than-average scores, and negative values (below zero) indicate lower-than-average scores. Gray dots mark individual language-group deviations for each country, while red dots highlight local-language assessments (e.g., how Chinese speakers rate China). Wider violin shapes reflect greater variability in assigned scores. Three sets of plots compare GPT-3.5, GPT-4, and Sonnet on the task of university application under female and male applicant persona.
+
+![](images/623a654f1cb286f5acc4df25ca5ba304753a3861a271642e57254290145f53c6.jpg)
+
+![](images/02446a07814f91854d627fdab0a20becf9a108b68855960ca611931160735afc.jpg)
+
+pages 116-122, Dubrovnik, Croatia. Association for Computational Linguistics.
+
+Vera Neplenbroek, Arianna Bisazza, and Raquel Fernandez. 2024. Mbbq: A dataset for cross-lingual comparison of stereotypes in generative llms. arXiv preprint arXiv:2406.07243.
+
+Shangrui Nie, Michael Fromm, Charles Welch, Rebekka Gorge, Akbar Karimi, Joan Plepi, Nazia Mowmita, Nicolas Flores-Herr, Mehdi Ali, and Lucie Flek. 2024. Do multilingual large language models mitigate stereotype bias? In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP, pages 65-83, Bangkok, Thailand. Association for Computational Linguistics.
+
+Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman. 2022. BBQ: A hand-built bias benchmark for question answering. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2086-2105, Dublin, Ireland. Association for Computational Linguistics.
+
+Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, et al. 2022. Language models are multilingual chain-of-thought reasoners. arXiv preprint arXiv:2210.03057.
+
+LL Thurstone. 1927. A law of comparative judgment. Psychological Review, 34(4):273.
+
+Aniket Vashishtha, Kabir Ahuja, and Sunayana Sitaram. 2023. On evaluating and mitigating gender biases in multilingual settings. In Findings of the Association for Computational Linguistics: ACL 2023, pages 307-318, Toronto, Canada. Association for Computational Linguistics.
+
+Shaoyang Xu, Junzhuo Li, and Deyi Xiong. 2023. Language representation projection: Can we transfer
+
+factual knowledge across languages in multilingual language models? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3692-3702, Singapore. Association for Computational Linguistics.
+
+Jinman Zhao, Yitian Ding, Chen Jia, Yining Wang, and Zifan Qian. 2024. Gender bias in large language models across multiple languages. arXiv preprint arXiv:2403.00277.
+
+Alex Zheng. 2024. Dissecting bias of chatgpt in college major recommendations. Information Technology and Management, pages 1-12.
+
+Shucheng Zhu, Weikang Wang, and Ying Liu. 2024. Quite good, but not enough: Nationality bias in large language models - a case study of ChatGPT. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13489-13502, Torino, Italia. ELRA and ICCL.

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+# A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding
+
+Jinghui Lu $^{1}$  Haiyang Yu $^{2}$  Yanjie Wang $^{3}$  Yongjie Ye $^{1}$  Jingqun Tang $^{1}$  Ziwei Yang $^{1}$  Binghong Wu $^{1}$  Qi Liu $^{1}$  Hao Feng $^{1}$  Han Wang $^{1}$  Hao Liu $^{1}$  Can Huang $^{1}$
+
+$^{1}$ ByteDance Inc.  $^{2}$ Fudan University
+
+lujinghui@bytedance.com, hyyu20@fudan.edu.cn
+
+{wangyanjie.prince, yeyongjie.ilz, tangjingqun}@bytedance.com
+
+{yangziwei.1221, wubinghong, liuqi.nero}@bytedance.com
+
+{fenghao.2019, wanghan.99, haoliu.0128, can.huang}@bytedance.com
+
+# Abstract
+
+Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document understanding tasks. However, existing methods that integrate spatial layouts with text have limitations, such as producing overly long text sequences or failing to fully leverage the autoregressive traits of LLMs. In this work, we introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) for document understanding. LayTextLLM projects each bounding box to a single embedding and interleaves it with text, efficiently avoiding long sequence issues while leveraging autoregressive traits of LLMs. LayTextLLM not only streamlines the interaction of layout and textual data but also shows enhanced performance in KIE and VQA. Comprehensive benchmark evaluations reveal significant improvements of LayTextLLM, with a  $15.2\%$  increase on KIE tasks and  $10.7\%$  on VQA tasks compared to previous SOTA OCR-based LLMs. All resources are available at https://github.com/LayTextLLM/LayTextLLM.
+
+# 1 Introduction
+
+Recent research has increasingly explored the use of Large Language Models (LLMs) or MultiModal Large Language Models (MLLMs) (Achiam et al., 2023; Team et al., 2023; Anthropic, 2024; Reid et al., 2024; Feng et al., 2023a,b; Liu et al., 2024c; Lu et al., 2024; Nourbakhsh et al., 2024; Gao et al., 2024; Li et al., 2024a; Zhou et al., 2024; Zhu et al., 2024; Zhao et al., 2024) for document-oriented Visual Question Answering (VQA) and Key Information Extraction (KIE).
+
+A line of research utilizes off-the-shelf OCR tools to extract text and spatial layouts, which are then combined with LLMs to address Visually Rich
+
+Document Understanding (VRDU) tasks. These approaches assume that most valuable information for document comprehension can be derived from the text and its spatial layouts, viewing spatial layouts as "lightweight visual information" (Wang et al., 2024a). Following this premise, several studies (Liu et al., 2024c; Perot et al., 2023; Luo et al., 2024; Chen et al., 2023a; He et al., 2023) have explored various approaches that integrate spatial layouts with text for LLMs and achieves results that are competitive with those of MLLMs.
+
+The most natural method to incorporate layout information is by treating spatial layouts as tokens, which allows for the seamless interleaving of text and layout into a unified text sequence (Perot et al., 2023; Chen et al., 2023a; He et al., 2023). For example, Perot et al. (2023) employ format such as "HARRISBURG 78|09" to represent OCR text and corresponding layout, where "HARRISBURG" is OCR text and "78|09" indicates the mean of the horizontal and vertical coordinates, respectively. Similarly, He et al. (2023) use "[x_min, y_min, x_max, y_max]" to represent layout information. These approaches can effectively take advantage of autoregressive characteristics of LLMs and is known as the "coordinate-as-tokens" scheme (Perot et al., 2023). In contrast, DocLLM (Wang et al., 2024a) explores interacting spatial layouts with text through a disentangled spatial attention mechanism that captures cross-alignment between text and layout modalities.
+
+However, we believe that both of the previous approaches have limitations. As shown in Figure 1, coordinate-as-tokens significantly increases the number of tokens. Additionally, to accurately comprehend coordinates and enhance zero-shot capabilities, this scheme often requires few-shot in-context demonstrations and large-scale language models, such as ChatGPT Davinci-003 (175B) (He et al., 2023), which exacerbates issues related to sequence length and GPU resource demands. Al
+
+![](images/559e4c0dd67b00e60df8fca8a0b117271a84c77f73bf15c89df250df6528471a.jpg)  
+Figure 1: The performance against input sequence length of different datasets across various OCR-based methods where data is from Table 1 and 5.
+
+though DocLLM does not increase sequence length, its performance may be improved by more effectively leveraging the autoregressive traits of LLMs.
+
+To address these problems, this paper explores a simple yet effective approach to enhance the interaction between spatial layouts and text — Interleaving Layout and Text in a Large Language Model (LayTextLLM) for document understanding. Adhering to the common practice of interleaving any modality with text (Huang et al., 2023; Peng et al., 2023; Dong et al., 2024), we specifically apply this principle to spatial layouts. In particular, we map each bounding box to a single embedding, which is then interleaved with its corresponding text. As shown in Figure 1, LayTextLLM significantly outperforms the 175B models, while only slightly increasing or even reducing the sequence length compared to DocLLM. Our contributions can be listed as follows:
+
+- We propose LayTextLLM for document understanding. To the best of the authors' knowledge, this is the first work to employ a unified embedding approach (Section 3.1) that interleaves spatial layouts directly with textual data within a LLM. By representing each bounding box with one token, LayTextLLM efficiently addresses sequence length issues brought by coordiante-as-tokens while fully leveraging autoregressive traits for VRDU tasks.  
+- We propose three tailored pre-training tasks (Section 3.2.1) to improve the model's under
+
+standing of the interaction between layout and text, and its ability to generate precise coordinates for regions of interest. These tasks include Line-level Layout Decoding, Text-to-Layout Prediction, and Layout-to-Text Prediction. Besides, we introduce Spatially-Grounded KIE (Section 3.2.2) to further enhance the model's performance on KIE task.
+
+- Extensive experimental results quantitatively demonstrate that LayTextLLM significantly surpasses previous state-of-the-art (SOTA) OCR-based methods. Notably, it outperforms DocLLM by  $10.7\%$  on VQA tasks and  $15.2\%$  on KIE tasks (Section 4). Furthermore, it achieves superior performance on SOTA OCR-free MLLMs, such as Qwen2-VL among most KIE datasets. Ablations and visualizations demonstrate the utility of the proposed component, with analysis showing that LayTextLLM not only improves performance but also reduces input sequence length compared to current OCR-based models.
+
+# 2 Related Work
+
+# 2.1 OCR-based LLMs for VRDU
+
+Early document understanding methods (Hwang et al., 2020; Xu et al., 2020, 2021; Hong et al., 2022; Tang et al., 2022) tend to solve the task in a two-stage manner, i.e., first reading texts from input document images using off-the-shelf OCR engines and then understanding the extracted texts. Considering the advantages of LLMs (e.g., high generalizability), some recent methods endeavor to combine LLMs with OCR-derived results to solve document understanding. Inspired by the coordinate-astokens' approach in ICL-D3IE (Perot et al., 2023), He et al. (2023) use numerical tokens to integrate layout information, combining layout and text into a unified sequence that maximizes the autoregressive benefits of LLMs. To reinforce the layout information while avoiding increasing the number of tokens, DocLLM (Wang et al., 2024a) designs a disentangled spatial attention mechanism to capture cross-alignment between text and layout modalities. Recently, LayoutLLM (Luo et al., 2024) utilizes the pre-trained layout-aware model (Huang et al., 2022), to insert the visual information, layout information and text information. However, these methods struggle to leverage autoregressive properties of LLMs while avoiding the computational
+
+overhead of increasing token counts. Finding a way to integrate layout information remains a challenge.
+
+# 2.2 OCR-free MLLMs for VRDU
+
+With the increasing popularity of MLLMs (Feng et al., 2023b; Hu et al., 2024; Liu et al., 2024c; Tang et al., 2024; Chen et al., 2024a; Dong et al., 2024; Li et al., 2024b; Liu et al., 2024a; Lu et al., 2025; Feng et al., 2025; Fei et al., 2025; Wang et al., 2025), various methods are proposed to solve VRDU through explicitly training models on visual text understanding datasets and perform end-to-end inference without using OCR engines. LLaVAR (Zhang et al., 2023) and UniDoc (Feng et al., 2023b) are notable examples that expand upon the document-oriented VQA capabilities of LLaVA (Liu et al., 2024b) by incorporating document-based tasks. These models pioneer the use of MLLMs for predicting texts and coordinates from document images, enabling the development of OCR-free document understanding methods. Additionally, DocPedia (Feng et al., 2023a) operates document images in the frequency domain, allowing for higher input resolution without increasing the input sequence length. Recent advancements in this field, including mPLUGDocOwl (Ye et al., 2023), Qwen-VL (Bai et al., 2023), Qwen2-VL (Wang et al., 2024b), and TextMonkey (Liu et al., 2024c), leverage publicly available document-related VQA datasets to further enhance the document understanding capability. Although these OCR-free methods have exhibited their advantages, they still struggle with the high-resolution input to reserve more text-related details.
+
+# 3 Method
+
+In this section, we introduce LayTextLLM. We begin by detailing the model architecture, which features an innovative Spatial Layout Projector (Section 3.1) that transforms four-dimensional layout coordinates into a single-token embedding. Next, we present three layout-text alignment pretraining tasks: line-level layout decoding, text-to-query prediction, and layout-to-text prediction (Section 3.2.1) to ensure a seamless integration of layout and text understanding. Finally, we describe the incorporation of spatially-grounded key information extraction as a auxiliary task during supervised fine-tuning (SFT) (Section 3.2.2), to enhance the performance in KIE tasks.
+
+# 3.1 Model Architecture
+
+The overall architecture of LayTextLLM is shown in Figure 2. LayTextLLM is built on the Llama2-7B-chat model (Gao et al., 2023).
+
+Spatial Layout Projector To enable the model to seamlessly integrate spatial layouts with text, we propose a novel Spatial Layout Projector (SLP). This projector employs a two-layer MLP to transform layout coordinates into bounding box tokens, facilitating the interleaving of spatial and textual information. Concretely, each OCR-derived spatial layout is represented by a bounding box defined by four-dimensional coordinates  $[x_1, y_1, x_2, y_2]$ , where these coordinates denote the normalized minimum and maximum horizontal  $(x)$  and vertical  $(y)$  extents of the box, respectively. The SLP maps these coordinates into a high-dimensional embedding space, enabling the LLM to process them as a single token. This is computed as:
+
+$$
+z = W _ {2} \cdot \left(\operatorname {G e L U} \left(W _ {1} \cdot c + b _ {1}\right)\right) + b _ {2} \tag {1}
+$$
+
+where  $c \in \mathbb{R}^4$  is the vector of bounding box coordinates,  $W_1 \in \mathbb{R}^{h \times 4}$  and  $W_2 \in \mathbb{R}^{d \times h}$  are weight matrices,  $b_1 \in \mathbb{R}^{h \times 1}$  and  $b_2 \in \mathbb{R}^{d \times 1}$  are bias vectors,  $h$  is the hidden dimension of the MLP, and  $d$  is the dimension of the final embedding. In this study, we set  $h = d$ . The resulting bounding box token  $z \in \mathbb{R}^d$  is a high-dimensional representation of the spatial layout. Importantly, the SLP is shared across all bounding box tokens, which introduces a minimal number of parameters to the model.
+
+Large Language Model As shown in Figure 2, the bounding box token  $z$  is interleaved with its corresponding textual embeddings and fed into the LLM. To introduce additional trainable parameters for layout information, we integrate a Partial Low-Rank Adaptation (P-LoRA) module proposed in InternLM-XComposer2 (Dong et al., 2024) detailed in Appendix A. Additionally, to improve the efficiency of coordinate decoding, we introduce 1,000 special tokens, i.e., “ $<B0>$ ” through “ $<B999>$ ” to represent output coordinates.
+
+# 3.2 Training Tasks
+
+LayTextLLM is pre-trained using three innovative tasks designed to align layout and text. During the SFT phase, we introduce a novel Spatially-Grounded Key Information Extraction task as a auxiliary task, which significantly enhances the model's performance on KIE-related tasks. Figures 3 and 4 illustrate the above tasks.
+
+![](images/d383fc25f73c1aa88d6656be3ccbe81e1ef60942f15a0282b3f119b776dfd337.jpg)  
+Figure 2: An overview of LayTextLLM incorporates interleaving bounding box tokens  $(b^{i})$  with text tokens  $(t^i)$ , where the superscripts represent the sequence positions of the tokens.
+
+![](images/7d8e1c516f68024839a0a4d6dcfb8dbbb6f45a28c4b1e1e9e54d8868aeeaf568.jpg)  
+(a) Line-level Layout Decoding
+
+![](images/bdf40e36d81a83b381d600a5538dd5ab16083776e5edceab3f4c132e0994d0a6.jpg)  
+(b) Text-to-Layout Prediction
+
+![](images/9d7714e8fbc79c3a327c1d99f8c679ddcb0b396eaa32e4e7616aea3c13b1ea70.jpg)  
+(c) Layout-to-text Prediction  
+Figure 3: Illustration of layout-text alignment pre-training tasks. <box> is the placeholder for bounding box tokens.
+
+# 3.2.1 Layout-text Alignment Pre-training
+
+Line-level Layout Decoding To enhance the model's ability to interpret and reconstruct layout information, we introduce the Line-level Layout Decoding task. This task leverages the bounding box embeddings, which encode spatial layout details, and challenges the model to decode these embeddings back into precise coordinates. Specifically, the model is provided with word-level OCR texts and their corresponding layout coordinates as input. It is then prompted with the question: "What are the textlines and corresponding coordinates?" The model is expected to intelligently merge word-level OCR texts into coherent line-level texts while simultaneously generating the coordinates that rep
+
+resent the layout of these line-level texts. The output consists of two components: (1) the reconstructed line-level texts and (2) the corresponding combined coordinates, which are derived by aggregating the word-level bounding boxes to reflect the spatial arrangement of the line-level OCR. Through this task, the model is expected to demonstrate two key abilities: (1) the ability to logically group word-level texts into line-level texts using layout information, and (2) the ability to accurately decode bounding box embeddings back into spatial coordinates. By doing so, the model demonstrates a deeper understanding of both textual content and its spatial organization within a document.
+
+![](images/0ad118c7a6b0ca7aa36123da5aef730bb5a4a2872fa74bdd60f6e60c9edd913d.jpg)  
+(a) SG-KIE for Entity Linking
+
+![](images/cad1ae776bc177d624cbb6b7115df38459030d87e9477adbd7fba39d0439f71b.jpg)  
+(b) SG-KIE for Semantic Entity Recognition  
+Figure 4: Illustration of Spatially-Grounded KIE task. <box> is the placeholder for bounding box tokens.
+
+Text-to-Layout Prediction To enhance the model's ability to comprehend and predict document layouts, we introduce the Text-to-Layout Prediction task. In this task, the model predicts spatial coordinates for text segments based on word-level OCR inputs and their corresponding layout information. Specifically, given a prompt such as "What are the bounding boxes of the words: {word1} \n {word2} \n {word3}...?", where {word} represents line-level text randomly selected from the input (number of selected words limited to 5), the model is required to generate precise spatial coordinates for each of the specified words.
+
+Layout-to-text Prediction We also propose the Layout-to-Text Prediction task. In this task, the model predicts textual content based on spatial layout information and bounding box coordinates. Given a prompt such as "What are the words located within: {bbox1} \n {bbox2} \n {bbox3}...?", where {bbox} is the placeholder of bounding box embedding representing the spatial coordinates of text regions (with the number of bounding boxes limited to 5), the model generates the corresponding textual content for each specified region. The Text-to-Layout Prediction and Layout-to-Text Prediction tasks offer complementary advantages to advance document layout understanding. All word-level and line-level OCR results can be easily obtained using off-the-shelf OCR tools, making it easy to scale up for large-scale pre-training.
+
+# 3.2.2 Supervised Fine-tuning
+
+During the SFT phase, we fine-tuned the pretrained model with the Document Dense Description (DDD) and Layout-aware SFT datasets from Luo et al. (2024). Additionally, we introduce Spatially-Grounded Key Information Extraction (SG-KIE) task, which requires the model to not only answer questions (i.e., extract specific values)
+
+but also provide the coordinates of these answers by responding to the prompt "Please provide the coordinates for your answer:" as a auxiliary task to further improve the model performance on KIE tasks.
+
+In the literature, KIE tasks are classified into two types: Entity Linking (EL) and Semantic Entity Recognition (SER). EL is an open-set KIE task in which both the key and its corresponding value are present in the input. In contrast, SER is a closed-set KIE task where the key has a predefined meaning, and the value must be extracted from the document.
+
+For the EL task, SG-KIE requires the model to output the answer in the following format:  $\{\text{key}\} \{\text{key\_bbox}\}$  's value is  $\{\text{value}\} \{\text{value\_bbox}\}$ , where  $\{\text{key}\}$  and  $\{\text{value}\}$  represent the respective key and value, and  $\{\text{key\_bbox}\}$  and  $\{\text{value\_bbox}\}$  denotes the spatial layout information of the corresponding textual content. For the SER task, the answer format is:  $\{\text{value}\} \{\text{value\_bbox}\}$ , where  $\{\text{value}\}$  refers to the extracted value, and  $\{\text{value\_bbox}\}$  represents the spatial layout of the extracted text in the document. The illustrations of SG-KIE for these tasks are presented in Figure 4.
+
+# 4 Experiments
+
+# 4.1 Datasets
+
+Layout-text Alignment Pre-training Data In training process, we exclusively used open-source data to facilitate replication. We subsampled data from two datasets for layout-text alignment pretraining: (1) DocILE (Simsa et al., 2023) and (2) RVL_CDIP (Harley et al., 2015).
+
+SFT data We selected KVP10k (Naparstek et al., 2024) and SIBR (Yang et al., 2023) datasets to create training examples of SG-KIE tasks. For document-oriented VQA, we selected Document Dense Description (DDD) and Layout-aware SFT data used in Luo et al. (2024), which
+
+are two synthetic datasets generated by GPT-4. Besides, DocVQA (Mathew et al., 2021), InfoVQA (Mathew et al., 2022), ChartQA (Masry et al., 2022), VisualMRC (Tanaka et al., 2021) is included following (Liu et al., 2024c). For KIE task, we selected SROIE (Huang et al., 2019), CORD (Park et al., 2019), FUNSD (Jaume et al., 2019) datasets following Wang et al. (2024a); Luo et al. (2024); Liu et al. (2024c). The dataset statistics are provided in Appendix C.
+
+# 4.2 Implementation Detail
+
+The LLM component of LayTextLLM is initialized from the Llama2-7B-chat (Touvron et al., 2023), consistent with previous OCR-based methods like DocLLM (Wang et al., 2024a), which also use Llama2-7B. We also replicated the results of the coor-as-tokens scheme using Llama2-7B for consistency. Noting the LayoutLLM (Luo et al., 2024) utilizes Llama2-7B and Vicuna 1.5 7B, which is fine-tuned from Llama2-7B. Thus, for the majority of our comparisons, the models are based on the same or similar LLM backbones, allowing for a fair comparison between approaches. Other MLLM baselines use backbones like QwenVL (Bai et al., 2023), Qwen2-VL (Wang et al., 2024b), InternVL (Chen et al., 2024b), and Vicuna (Chen et al., 2024a), all with at least 7B parameters, excluding the visual encoder. This also makes the comparison fair.
+
+In this study, we developed two versions of LayTextLLM to facilitate a comparative analysis under different training configurations. Following the terminology established by Luo et al. (2024), the term "zero-shot" denotes models that are trained without exposure to data from downstream test datasets. For the first version, LayTextLLM<sub>zero</sub>, we utilized DDD, Layout-aware SFT data, KVP10k, and SIBR for training. The second version, LayTextLLM<sub>All</sub>, extends this training regimen by incorporating a broader array of VQA and KIE datasets, including DocVQA, InfoVQA, VisualMRC, ChartQA, FUNSD, CORD, and SROIE. Both versions are initialized with the same pretrained LayTextLLM weights, with the key difference being that LayTextLLM<sub>All</sub> benefits from the inclusion of additional downstream training datasets. We used word-level and line-level OCR provided by the respective datasets for a fair comparison, with the exception of the ChartQA dataset, which does not provide OCR. Detailed setup can be found in Appendix D.
+
+# 4.3 Baselines
+
+OCR-based baselines For OCR-based baseline models, we implemented a basic approach using only OCR-derived text as input. This was done using two versions: Llama2-7B-base and Llama2-7B-chat. We also adapted the coordinate-as-tokens scheme from He et al. (2023) for these models, resulting in two new variants: Llama2-7B-base<sub>coor</sub> and Llama2-7B-chat<sub>coor</sub>. Additionally, we included results from a stronger baseline using the ChatGPT Davinci-003 (175B) model (He et al., 2023), termed Davinci-003-175B<sub>coor</sub>. One other recent SOTA OCR-based approach, DocLLM (Wang et al., 2024a) is also included.
+
+OCR-free baselines These baselines include UniDoc (Feng et al., 2023b), DocPedia (Feng et al., 2023a), Monkey (Li et al., 2023), InternVL (Chen et al., 2023b), InternLM-XComposer2 (Dong et al., 2024), TextMonkey, TextMonkey+ (Liu et al., 2024c), Qwen2-VL (Wang et al., 2024b). We selected the above models as baselines due to their superior performance in both document-oriented VQA and KIE tasks.
+
+Visual+OCR baselines We selected LayoutLLM  ${}_{\text{Llama2CoT}}$  (Luo et al., 2024) and the most recent SOTA method DocLayLLM  ${}_{\text{Llama2CoT}}$  (Liao et al., 2024), which integrates visual cues, text and layout, as stronger baselines.
+
+# 4.4 Evaluation Metrics
+
+To ensure a fair comparison with other OCR-based methods, we conducted additional evaluations using original metrics specific to certain datasets, such as F1 score (Wang et al., 2024a; He et al., 2023), ANLS (Gao et al., 2019; Wang et al., 2024a; Luo et al., 2024) and CIDEr (Vedantam et al., 2015; Wang et al., 2024a). To ensure a fair comparison with OCR-free methods, we adopted the accuracy metric (Liu et al., 2024c; Feng et al., 2023b), where a response from the model is considered correct if it fully captures the ground truth.
+
+# 4.5 Quantitative Results
+
+Comparison with SOTA OCR-based Methods For the primary comparison in our work, we evaluate against other SOTA pure OCR-based methods. The experimental results, as presented in Table 1, demonstrate significant performance improvements achieved by the LayTextLLM models compared to DocLLM (Wang et al., 2024a). Specifically,
+
+<table><tr><td></td><td colspan="3">Document-Oriented VQA</td><td colspan="4">KIE</td></tr><tr><td></td><td>DocVQA</td><td>VisualMRC</td><td>Avg</td><td>FUNSD</td><td>CORD</td><td>SROIE</td><td>Avg</td></tr><tr><td>Metric</td><td colspan="3">ANLS % / CIDEr</td><td colspan="4">F-score %</td></tr><tr><td>Text</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Llama2-7B-base</td><td>34.0</td><td>182.7</td><td>108.3</td><td>25.6</td><td>51.9</td><td>43.4</td><td>40.3</td></tr><tr><td>Llama2-7B-chat</td><td>20.5</td><td>6.3</td><td>13.4</td><td>23.4</td><td>51.8</td><td>58.6</td><td>44.6</td></tr><tr><td>Text + Coordinates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Llama2-7B-basecoor (He et al., 2023)</td><td>8.4</td><td>3.8</td><td>6.1</td><td>6.0</td><td>46.4</td><td>34.7</td><td>29.0</td></tr><tr><td>Llama2-7B-chatcoor (He et al., 2023)</td><td>12.3</td><td>28.0</td><td>20.1</td><td>14.4</td><td>38.1</td><td>50.6</td><td>34.3</td></tr><tr><td>Davinci-003-175Bcoor (He et al., 2023)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>92.6</td><td>95.8</td><td>-</td></tr><tr><td>DocLLM (Wang et al., 2024a)</td><td>69.5*</td><td>264.1*</td><td>166.8</td><td>51.8*</td><td>67.4*</td><td>91.9*</td><td>70.4</td></tr><tr><td>LayTextLLMzero (Ours)</td><td>66.6</td><td>229.1</td><td>147.9</td><td>57.6</td><td>87.3</td><td>89.4</td><td>78.1</td></tr><tr><td>LayTextLLMall (Ours)</td><td>75.6*</td><td>279.4*</td><td>177.5</td><td>63.3*</td><td>97.3*</td><td>96.0*</td><td>85.6</td></tr></table>
+
+Table 1: Comparison with SOTA OCR-based methods. The asterisk(*) indicates that the model was trained using the training set associated with the evaluation set.  
+
+<table><tr><td rowspan="2"></td><td colspan="3">Document-Oriented VQA</td><td colspan="5">KIE</td></tr><tr><td>DocVQA</td><td>InfoVQA</td><td>Avg</td><td>FUNSD</td><td>SROIE</td><td>POIE</td><td>CORD</td><td>Avg</td></tr><tr><td>Metric</td><td colspan="8">Accuracy %</td></tr><tr><td>OCR-free</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UniDoc (Feng et al., 2023b)</td><td>7.7</td><td>14.7</td><td>11.2</td><td>1.0</td><td>2.9</td><td>5.1</td><td>-</td><td>-</td></tr><tr><td>DocPedia (Feng et al., 2023a)</td><td>47.1*</td><td>15.2*</td><td>31.2</td><td>29.9</td><td>21.4</td><td>39.9</td><td>-</td><td>-</td></tr><tr><td>Monkey (Li et al., 2023)</td><td>50.1*</td><td>25.8*</td><td>38.0</td><td>24.1</td><td>41.9</td><td>19.9</td><td>-</td><td>-</td></tr><tr><td>InternVL (Chen et al., 2023b)</td><td>28.7*</td><td>23.6*</td><td>26.2</td><td>6.5</td><td>26.4</td><td>25.9</td><td>-</td><td>-</td></tr><tr><td>InternLM-XComposer2 (Dong et al., 2024)</td><td>39.7</td><td>28.6</td><td>34.2</td><td>15.3</td><td>34.2</td><td>49.3</td><td>-</td><td>-</td></tr><tr><td>TextMonkey (Liu et al., 2024c)</td><td>64.3*</td><td>28.2*</td><td>46.3</td><td>32.3</td><td>47.0</td><td>27.9</td><td>-</td><td>-</td></tr><tr><td>TextMonkey+ (Liu et al., 2024c)</td><td>66.7*</td><td>28.6*</td><td>47.7</td><td>42.9</td><td>46.2</td><td>32.0</td><td>-</td><td>-</td></tr><tr><td>Qwen2-VL (Wang et al., 2024b)</td><td>81.4*</td><td>45.2*</td><td>63.3</td><td>53.2</td><td>71.3</td><td>85.7</td><td>78.8</td><td>72.2</td></tr><tr><td>Text + Coordinates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LayTextLLMzero (Ours)</td><td>70.4</td><td>29.8</td><td>50.1</td><td>54.9</td><td>88.3</td><td>65.1</td><td>86.9</td><td>73.8</td></tr><tr><td>LayTextLLMall (Ours)</td><td>77.7*</td><td>40.1*</td><td>59.0</td><td>60.1*</td><td>95.5*</td><td>68.1</td><td>96.7*</td><td>80.1</td></tr></table>
+
+Table 2: Comparison with SOTA OCR-free MLLMs.
+
+LayTextLLM<sub>zero</sub> exhibits notably superior performance, with its zero-shot capabilities even rivaling SFT approaches. For instance, in the KIE task, LayTextLLM<sub>zero</sub> achieves an overall performance of  $78.1\%$ , significantly outperforming DocLLM's score of  $70.4\%$ . Furthermore, under the same training conditions, LayTextLLM<sub>All</sub> surpasses the previous OCR-based SOTA by a substantial margin, achieving an overall improvement of  $10.7\%$  in the VQA task and  $15.2\%$  in the KIE tasks. Besides, we found that the spatial information can be decoded back into coordinates even without visual information, as discussed in Appendix I, which is not exhibited in DocLLM. Similarly, when contrasting with coordinate-as-tokens employed in Llama2-7B, LayTextLLM<sub>zero</sub> again outperforms significantly. More qualitative results are shown in Appendix B. More discussion of subperformance of DocLLM and coordinate-as-tokens can be seen in Appendix F.
+
+Comparison with SOTA OCR-free Methods We also compare LayTextLLM with other OCR-free methods, and the results in Table 2 highlight its exceptional performance across various tasks. Due to fairness concerns, results for ChartQA are reported separately in Appendix G, as the dataset lacks OCR-derived outputs, and we employed in-house OCR tools instead.
+
+LayTextLLM<sub>zero</sub> significantly outperforms most OCR-free methods except for Qwen2-VL. Notably, even without exposure to the dataset's training set, LayTextLLM<sub>zero</sub> achieves competitive VQA performance, rivaling models like TextMonkey+, which were trained on corresponding datasets. When fine-tuned with relevant data, LayTextLLM<sub>All</sub> exhibits even greater performance improvements. Compared to the SOTA MLLM Qwen2-VL, LayTextLLM sub-performs on VQA tasks which is further discussed in Limitation (Section 5). However, it outperforms Qwen2-VL in terms of KIE tasks. Notably, LayTextLLM<sub>zero</sub> exceeds Qwen2-VL on three out of four KIE benchmarks, with significant improvements of  $1.7\%$  on FUNSD,  $17\%$  on SROIE, and  $8.1\%$  on CORD.
+
+Comparison with SOTA Visual+OCR Methods As shown in Table 3, in zero-shot scenarios, our approach outperforms LayoutLLM and DocLayLLM on most KIE datasets, with improvements of  $12.4\%$  and  $5.4\%$ , respectively. This is noteworthy given that both LayoutLLM and DocLayLLM utilize visual, OCR text, and layout information as inputs and inference with Chain-of-thought, highlighting our ability to effectively leverage OCR-based results. However, similar to the comparison results with MLLMs, LayTextLLM exhibits limitations in
+
+<table><tr><td rowspan="2"></td><td colspan="3">Document-Oriented VQA</td><td colspan="4">KIE</td></tr><tr><td>DocVQA</td><td>VisualMRC</td><td>Avg</td><td>FUNSD-</td><td>CORD-</td><td>SROIE-</td><td>Avg</td></tr><tr><td>Metric</td><td colspan="7">ANLS %</td></tr><tr><td>Visual + Text + Coordinates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LayoutLMMiama2CoT (Luo et al., 2024)</td><td>74.2</td><td>55.7</td><td>64.9</td><td>78.6</td><td>62.2</td><td>70.9</td><td>70.6</td></tr><tr><td>DocLayLMMiama2CoT (Liao et al., 2024)</td><td>72.8</td><td>55.0</td><td>63.9</td><td>78.7</td><td>70.8</td><td>83.2</td><td>77.6</td></tr><tr><td>Text + Coordinates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LayTextLLMzero (Ours)</td><td>66.6</td><td>37.9</td><td>52.3</td><td>79.0</td><td>79.8</td><td>90.2</td><td>83.0</td></tr><tr><td>LayTextLLMall (Ours)</td><td>75.6*</td><td>42.3*</td><td>59.0</td><td>83.4*</td><td>83.1*</td><td>95.6*</td><td>87.4</td></tr></table>
+
+Table 3: Comparison with LayoutLLM. The superscript minus(−) indicates that the cleaned test set used in Luo et al. (2024).  
+
+<table><tr><td colspan="5"></td><td colspan="4">Document-Oriented VQA</td><td colspan="4">KIE</td></tr><tr><td>SLP</td><td>L-T PT</td><td>SG-KIE</td><td>P-LoRA</td><td>DocVQA</td><td>InfoVQA</td><td>VisualMRC</td><td>Avg</td><td>FUNSD</td><td>CORD</td><td>SROIE</td><td>Avg</td><td></td></tr><tr><td>×</td><td>✓</td><td>✓</td><td>✓</td><td>65.8</td><td>25.3</td><td>28.7</td><td>39.9</td><td>49.3</td><td>65.8</td><td>61.9</td><td>59.0</td><td></td></tr><tr><td>✓</td><td>×</td><td>✓</td><td>✓</td><td>78.2</td><td>39.7</td><td>28.3</td><td>48.7</td><td>52.1</td><td>76.5</td><td>86.8</td><td>71.8</td><td></td></tr><tr><td>✓</td><td>✓</td><td>×</td><td>✓</td><td>69.1</td><td>28.7</td><td>29.3</td><td>42.3</td><td>52.3</td><td>82.4</td><td>84.0</td><td>72.9</td><td></td></tr><tr><td>✓</td><td>✓</td><td>✓</td><td>×</td><td>74.6</td><td>36.6</td><td>32.6</td><td>47.9</td><td>54.8</td><td>86.0</td><td>91.3</td><td>77.4</td><td></td></tr><tr><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>70.4</td><td>29.8</td><td>31.7</td><td>44.0</td><td>54.9</td><td>86.9</td><td>88.3</td><td>76.7</td><td></td></tr></table>
+
+document-oriented VQA tasks, particularly when addressing questions that heavily depend on visual information. A more detailed analysis of these challenges is provided in Limitations (Section 5).
+
+# 4.6 Analysis
+
+Ablations To better assess the utility of each component in LayTextLLM, an ablation study was conducted, the results of which are presented in Table 4. Detailed information on the training setup for all variants is provided in Appendix D. The results clearly show that incorporating interleaved spatial layouts and texts significantly enhances the performance, evidenced by a  $4.1\%$  improvement in VQA and a  $17.7\%$  increase in KIE (the first row vs. the fourth row), indicating that SLP is a critical component. Interestingly, using next-token-prediction as the pre-training task (i.e., the second row) generally outperforms layout-text alignment pre-training across almost all VQA tasks. However, for KIE tasks, layout-text alignment pre-training remains more effective. We hypothesize that layout-text alignment pre-training helps the model learn the relationship between layout and text, which is particularly useful for layout-aware tasks like KIE. In contrast, next-token-prediction focuses on reconstructing the entire document, which is more beneficial for semantic-rich tasks like VQA. Furthermore, including SG-KIE results in a modest performance increase of  $1.7\%$  in VQA (the third row vs. the fourth row) but a significant improvement in KIE tasks (i.e.,  $3.8\%$ ), which is as expected. Intriguingly, excluding P-LoRA improves performance on VQA and KIE tasks, suggesting it adds
+
+unnecessary complexity or interference, which further highlights the benefits of interleaving texts and layouts.
+
+Sequence Length Table 5 presents statistics on the average input sequence length across different datasets. Intriguingly, despite interleaving bounding box tokens, LayTextLLM consistently exhibits the shortest sequence length in three out of four datasets, even surpassing DocLLM, which is counterintuitive. We attribute this to the tokenizer mechanism. For example, using tokenizer.encode(), a single word from the OCR engine, like "International" is encoded into a single ID [4623]. Conversely, when the entire OCR output is processed as one sequence, such as "... CPC,International,Inc...", the word "International" is split into two IDs [17579, 1288], corresponding to "Intern" and "ational" respectively. This type of case occurs frequently, we provide further discussion in Appendix E.
+
+Table 4: Ablations on each component of LayTextLLM (Accuracy).  
+
+<table><tr><td>Dataset</td><td>LayTextLLM</td><td>DocLLM</td><td>Coor-as-tokens</td></tr><tr><td>DocVQA</td><td>664.3</td><td>827.5</td><td>4085.7</td></tr><tr><td>CORD</td><td>137.9</td><td>153.2</td><td>607.3</td></tr><tr><td>FUNSD</td><td>701.9</td><td>847.5</td><td>4183.4</td></tr><tr><td>SROIE</td><td>529.2</td><td>505.1</td><td>1357.7</td></tr></table>
+
+Table 5: Average sequence length.
+
+# 5 Conclusion
+
+We propose LayTextLLM, interleaving spatial layouts and text to improve predictions through an innovative SLP, the Layout-text Alignment pretraining and the SG-KIE tasks. Extensive experiments show the effectiveness of LayTextLLM.
+
+# Limitations
+
+Although LayTextLLM has shown significant capabilities in text-rich VQA and KIE tasks, this alone does not suffice for all real-world applications. There are some instances where reasoning must be based solely on visual cues (e.g. size, color, objects)—a challenge that remains unmet. Questions such as "What is the difference between the highest and the lowest green bar?" and "What is written on the card on the palm?" illustrate this gap. Two bad cases, detailed in Figures 6 and 7, also underscore these limitations. Addressing these challenges underscores the need for future advancements that incorporate visual cues into the capabilities of LayTextLLM. Since the integration with MLLMs is not the primary focus of this work, the preliminary experiments exploring this approach are discussed in Appendix J.
+
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+
+![](images/4d34d3add49b15beb2a0c4c44876d05442339320d0308a0eaf3790b9f248974d.jpg)  
+Figure 5: The illustration of P-LoRA, adapted from Dong et al. (2024).
+
+# A Layout Partial Low-Rank Adaptation
+
+After using the SLP to generate bounding box tokens and a tokenizer to produce text tokens, these two modalities are then interacted using a Layout Partial Low-Rank Adaptation (P-LoRA) module in LLMs. P-LoRA, introduced in InternLM-XComposer2 (Dong et al., 2024), is originally used to adapt LLMs to the visual modality. It applies plug-in low-rank modules specified to the visual tokens, which adds minimal parameters while preserving the LLMs inherent knowledge.
+
+Formally, for a linear layer in the LLM, the original weights  $W_{O} \in \mathbb{R}^{C_{out} \times C_{in}}$  and bias  $B_{O} \in \mathbb{R}^{C_{out}}$  are specified for input and output dimensions  $C_{in}$  and  $C_{out}$ . P-LoRA modifies this setup by incorporating two additional matrices,  $W_{A} \in \mathbb{R}^{C_{r} \times C_{in}}$  and  $W_{B} \in \mathbb{R}^{C_{out} \times C_{r}}$ . These matrices are lower-rank, with  $C_{r}$  being considerably smaller than both  $C_{in}$  and  $C_{out}$ , and are specifically designed to interact with new modality tokens, which in our case are bounding box tokens. For example, given an input  $x = [x_{b}, x_{t}]$  comprising of bounding box tokens  $(x_{b})$  and textual tokens  $(x_{t})$  is fed into the system, the forward process is as follows, where  $\hat{x}_{t}, \hat{x}_{b}$  and  $\hat{x}$  are outputs:
+
+$$
+\begin{array}{l} \hat {x} _ {t} = W _ {0} x _ {t} + B _ {0} \\ \hat {x} _ {b} = W _ {0} x _ {b} + W _ {B} W _ {A} x _ {b} + B _ {0} \tag {2} \\ \hat {x} = \left[ \hat {x} _ {b}, \hat {x} _ {t} \right] \\ \end{array}
+$$
+
+# B Qualitative Examples
+
+Qualitative examples of document-oriented VQA (upper row) and KIE (bottom row) are shown in Figure 8. The results indicate that LayTextLLM is highly effective in utilizing spatial layout information to make more accurate predictions for these challenging examples. For example, in the upper right figure, many numeric texts in the receipt act as noise for the baseline method. In contrast, LayTextLLM integrates layout information to accurately predict the total price, as demonstrated by the other examples, underscoring the utility of LayTextLLM.
+
+# C Dataset Statistics
+
+Table 6 and 7 show the statistics of datasets used in layout-text alignment pre-training and SFT, respectively. In layout-text alignment pre-training, for training efficiency, we randomly selected around 50,000 documents from each of the DocILE and RVL_CDIP datasets. For every document, we generated two tasks: line-level layout decoding and either a text-to-Layout or layout-to-text prediction task, which yields a total of around 200,000 pretraining examples. We also tested the model on a KIE dataset POIE (Kuang et al., 2023).
+
+<table><tr><td>Dataset</td><td>DocILE</td><td>RVL_CDIP</td></tr><tr><td>Num Documents</td><td>55,719</td><td>59444</td></tr><tr><td>Num Examples</td><td>111,438</td><td>118,888</td></tr><tr><td>Num Tokens</td><td>75,952,078</td><td>67,340,246</td></tr></table>
+
+Table 6: Dataset statistics for layout-text alignment pretraining (using Llama-2 Tokenizer).
+
+# D Implementation Detail
+
+All training and inference procedures are conducted on eight NVIDIA A100 GPUs.
+
+Training LayTextLLM is initialized with Llama2-7b-chat model, the pre-training, SFT, and other model hyper-parameters can be seen in Table 8. Additional parameters including SLP and P-LoRA are randomly initialized. During pre-training and SFT, all parameters are trainable. Please note that all variants of LayTextLLM, including those utilized in ablation studies, are trained in accordance with the same settings. Specifically, for all variants in ablation study, we train with the same setting and dataset in accordance with LayTextLLM<sub>zero</sub>. For the variant
+
+![](images/f4d9e16b5310dcbfe5e483f3fa9d235e4d0b19734d43cbe6c3e0507955f591e8.jpg)
+
+Note: In 2015, question read "Some peoples say that Japan should play a more active military role in helping to maintain peace and stability in the Asia-Pacific region. Other people say that, given its history, Japan should limit its military role in regional affairs. Which view is closer to your own?"
+
+Source: Spring 2016 Global Attitudes Survey. Q42.
+
+PEW RESEARCH CENTER
+
+Question: What is the difference between the highest and the lowest green bar?
+
+GroundTruth: 6
+
+Our Prediction: 40
+
+![](images/89387d4603b289defd371b80649017cd0420ddeef4794bed076545c0ead3b42a.jpg)  
+Figure 6: A failure case of LayTextLLM on ChartQA.  
+Figure 7: A failure case of LayTextLLM on DocVQA.
+
+What is written on the card on the palm?
+
+GroundTruth: Trabon
+
+Our Prediction: put your lubrication problems in good hands
+
+without SLP, we replace the bounding box token placeholder "<box>" with " $\backslash n$ ". For the variant without layout-text alignment pre-training, we pre-train the model on the same dataset using a conventional next-token prediction task, excluding the loss computation for the bounding box token. After pre-training, we fine-tune the model on the SFT datasets. For the variant without SG-KIE tasks, we remove the SG-KIE data from the SFT datasets while retaining the original SER and EL tasks in KVP10k and SIBR to ensure the total number of training examples remains unchanged. For the variant without P-LoRA, we replace all P-LoRA modules with linear layers, as was previously done.
+
+All baseline results are sourced from Liu et al. (2024c) or respective original papers, with the
+
+exception of the Llama2-7B series, the Llama2-  $7\mathrm{B}_{\mathrm{coor}}$  series, and Qwen2-VL, these results were re-implemented by authors.
+
+Inference For the document-oriented VQA test set, we use the original question-answer pairs as the prompt and ground truth, respectively. For KIE tasks, we reformat the key-value pairs into a question-answer format, as described in Wang et al. (2024a); Luo et al. (2024); Liu et al. (2024c). Additionally, for the FUNSD dataset, we focus our testing on the entity linking annotations as described in Luo et al. (2024). Note that for KIE tasks, we report the result of directly generating the answer texts, instead of generating the answer with the coordinates (SG-KIE). The discussion regarding inference with SG-KIE can be found in
+
+<table><tr><td>Dataset</td><td>DDD</td><td>Layout-aware SFT</td><td>KVP10k</td><td>SIBR</td><td>DocVQA</td><td>InfoVQA</td><td>ChartQA</td><td>VisualMRC</td><td>FUNSD</td><td>CORD</td><td>SROIE</td></tr><tr><td>Num Documents</td><td>115,955</td><td>50,409</td><td>4,249</td><td>600</td><td>10,192</td><td>4,405</td><td>3,699</td><td>7,012</td><td>147</td><td>794</td><td>626</td></tr><tr><td>Num Examples</td><td>115,955</td><td>280,073</td><td>50,661</td><td>12,978</td><td>39,459</td><td>23,945</td><td>7,398</td><td>7,013</td><td>2,375</td><td>8,932</td><td>2,503</td></tr><tr><td>Num Tokens</td><td>71,067,212</td><td>101,209,393</td><td>27,018,563</td><td>8,045,694</td><td>17,621,621</td><td>1,024,236</td><td>1,052,752</td><td>1,622,387</td><td>11,543,711</td><td>1,140,437</td><td>1,066,930</td></tr></table>
+
+Table 7: Dataset statistics for SFT (using Llama-2 Tokenizer).  
+
+<table><tr><td></td><td>Backbone</td><td>Plora rank</td><td>Batch size</td><td>Max length</td><td>Precision</td><td>Train params</td><td>Fix params</td></tr><tr><td>Lay-Text Pretrain</td><td>Llama2-7B-base</td><td>256</td><td>128</td><td>4096</td><td>bf16</td><td>7.4 B</td><td>0B</td></tr><tr><td>SFT</td><td>Llama2-7B-base</td><td>256</td><td>128</td><td>4096</td><td>bf16</td><td>7.4 B</td><td>0B</td></tr><tr><td></td><td>Learning rate</td><td>Weight decay</td><td>Scheduler</td><td>Adam betas</td><td>Adam epsilon</td><td>Warm up</td><td>Epoch</td></tr><tr><td>Lay-Text Pretrain</td><td>5.0e-05</td><td>0.01</td><td>cosine</td><td>[0.9, 0.999]</td><td>1.0e-08</td><td>0.005</td><td>4</td></tr><tr><td>SFT</td><td>1.0e-05</td><td>0.01</td><td>cosine</td><td>[0.9, 0.999]</td><td>1.0e-08</td><td>0.005</td><td>4</td></tr></table>
+
+Table 8: LayTextLLM training Hyper-parameters.
+
+# Appendix H.
+
+To eliminate the impact of randomness on evaluation, no sampling methods are employed during testing for any of the models. Instead, beam search with a beam size of 1 is used for generation across all models. Additionally, the maximum number of new tokens is set to 512, while the maximum number of input tokens is set to 4096.
+
+# E Discussion of Input Sequence Length
+
+As mentioned in Section 4.6, it is intriguing that LayTextLLM has fewer input sequences than DocLLM, which is counterintuitive given that LayTextLLM interleaves bounding box tokens, typically resulting in longer sequence lengths. We attribute this to the Byte Pair Encoding (BPE) tokenizers (Sennrich et al., 2016) prevalently used in modern LLMs such as Llama2.
+
+BPE operates by building a vocabulary of commonly occurring subwords (or token pieces) derived from the training data. Initially, it tokenizes the text at the character level and then progressively merges the most frequent adjacent pairs of characters or sequences. The objective is to strike a balance between minimizing vocabulary size and maximizing encoding efficiency.
+
+Thus, when tokenizing a single word like "International" on its own, the tokenizer might identify it as a common sequence in the training data and encode it as a single token. This is especially likely if "International" frequently appears as a standalone word in the training contexts. However, when the word "International" is part of a larger sequence of words such as including in a long sequence of OCR-derived texts like "...335 CPC,International,Inc...", the context changes. The tokenizer might split "International" into sub-tokens like "Intern" and
+
+"ational" because, in various contexts within the training data, these subwords might appear more frequently in different combinations or are more useful for the model to understand variations in meaning or syntax.
+
+When using LayTextLLM, we input word-level OCR results into the tokenizer, typically resulting in the former situation, where words are encoded as single tokens. Conversely, with DocLLM, the entire OCR output is processed as one large sequence, leading to the latter situation and a longer sequence length than in LayTextLLM. This difference underscores the utility of LayTextLLM in achieving both accuracy and inference efficiency due to its shorter sequence length.
+
+# F Discussion on Advantage of Interleaving Layout and Text
+
+Discussion on DocLLM We visualize the attention patterns between input and output tokens in Figure 9. The attention pattern is insightful with the specific question, "What is the quantity of - TICKET CP?<0x0A>"
+
+As shown in Figure 9(a), when the model begins predicting the answer "Final", "<0x0A>" (newline symbol) is heavily focusing on layout information, as seen by the significant attention on the bounding box embedding "<unk>" token before "(Qty)". This highlights the model's effort to orient itself spatially and understand the structural context of the tokens. At this stage, the model is developing a cognitive understanding of how the elements are laid out on the page. We extract and visualize the attention scores that "<0x0A>" assigns to each bounding box in Figure 9(c). The visualization shows that the model focuses most on "Qty", followed by "-TICKET" and "2.00", which
+
+reflects the layout information essential for making the prediction. In the final layer (Figure 9(b)), the model's attention shifts dramatically towards the "Qty" token, which holds the semantic meaning necessary to answer the question. This shift from layout-based cognition to content-based reasoning illustrates how the bounding box tokens act as spatial anchors that help the model pinpoint and organize the relevant information (such as "Qty") to make the correct prediction.
+
+The attention of LayTextLLM exhibits a distinct pattern compared to models like DocLLM, which uses block infilling to predict missing blocks from both preceding and succeeding context. In contrast, LayTextLLM adheres to an auto-regressive approach, focusing its attention solely on preceding information. Furthermore, interleaving bounding box and text embeddings creates strong attention connections between textual and spatial representations, as shown in Figure 9. In contrast, DocLLM integrates spatial information into the calculation of attention score which is implicitly. As shown in Table 1, LayTextLLM significantly outperforms DocLLM, again underscoring the advantage of interleaving bounding box and text embeddings. Also, we found that the spatial information can be decoded back into coordinates even without inputting visual information, as discussed in Appendix I, which is not exhibited in DocLLM.
+
+We also conduct a fairer experiment by re-implementing DocLLM using the identical training settings as LayTextLLM<sub>zero</sub>. In order to ensure a more intuitive and fair comparison between the two layout adaptation methods (i.e., SLP versus disentangled spatial attention), we exclude the use of P-LoRA in LayTextLLM<sub>zero</sub>. Table 9 demonstrates that SLP is a more effective method for incorporating layout information, as evidenced by a  $6.7\%$  improvement in VQA and an  $8.4\%$  improvement in KIE. Additionally, while DocLLM introduces a suite of attention weights for layout information, it significantly increases the number of parameters in LLaMA-2 from 6.73B to 8.37B. In contrast, LayTextLLM introduces a much smaller increase in parameters.
+
+Discussion on coordinate-as-tokens The subperformance of coordinate-as-tokens methods can be attributed to the following three reasons: (1) The coordinate-as-tokens approach tends to introduce an excessive number of tokens, often exceeding the pre-defined maximum length of Llama2-7B (i.e.,
+
+4096). Consequently, this leads to a lack of crucial OCR information, resulting in hallucination and subpar performance. (2) When re-implementing the coordinate-as-tokens method with Llama2-7B, we did not introduce the ICL strategy, as it would contribute additional length to the input sequence. (3) The coordinate-as-tokens approach necessitates a considerably larger-sized LLM to comprehend the numerical tokens effectively.
+
+# G Results of ChartQA
+
+As shown in Figure 6, the question-answer pairs in ChartQA (Masry et al., 2022) tend to involve the visual cues for reasoning. However, with only text and layout information as input, the proposed LayTextLLM inevitably have difficulties in reasoning visual-related information. Thus, on the ChartQA dataset, LayTextLLM can hardly achieve better performance than previous methods that include visual inputs. Although the visual information is not used in LayTextLLM, it can still exhibit better zero-shot ability than UniDoc (Feng et al., 2023b). After incorporating the training set of ChartQA, the performance of LayTextLLM can be boosted to  $42.2\%$ . Considering the importance of visual cues in ChartQA-like tasks, we will try to involve the visual information into LayTextLLM in future work. A preliminary discussion can be seen in Appendix J.
+
+# H Inference with SG-KIE
+
+As discussed in Section 4.6, incorporating SG-KIE as an auxiliary task in SFT has been shown to enhance the performance of KIE tasks. In this section, we investigate the effectiveness of using SG-KIE as a direct inference task for KIE. The results are shown in Table 11. We can observe that, for the  $\mathrm{FUNSD}^{-}$  and  $\mathrm{CORD}^{-}$  datasets, SG-KIE inference demonstrates improved performance. However, for the  $\mathrm{SROIE}^{-}$  dataset, there is a slight decrease in performance. We manually reviewed the problematic cases of SG-KIE and identified two main reasons for the performance drop: (1) incorrect format, which leads to parsing errors such as "432.60[ SR @  $6\% [ < B - 1013 > < B453 > < B > < B478 > ]$  ", and (2) ambiguous key types in the  $\mathrm{SROIE}^{-}$  dataset. For instance, the key "total" can refer to "grand total" and if the model has not been trained with the dataset, SG-KIE may mistakenly localize it to the wrong value. A notable instance of this issue is shown in Figure 10. These types of errors occur
+
+<table><tr><td></td><td colspan="4">Document-Oriented VQA</td><td colspan="4">KIE</td><td>Num Params</td></tr><tr><td>Methods</td><td>DocVQA</td><td>InfoVQA</td><td>VisualMRC</td><td>Avg</td><td>FUNSD</td><td>CORD</td><td>SROIE</td><td>Avg</td><td></td></tr><tr><td>DocLLM</td><td>66.6</td><td>28.3</td><td>28.6</td><td>41.2</td><td>51.3</td><td>71.8</td><td>83.9</td><td>69.0</td><td>8.37B</td></tr><tr><td>LayTextLLM</td><td>74.6</td><td>36.6</td><td>32.6</td><td>47.9</td><td>54.8</td><td>86.0</td><td>91.3</td><td>77.4</td><td>6.76B</td></tr></table>
+
+Table 9: Comparison of two layout adaptation methods, i.e., SLP in LayTextLLM and Disentangled Spatial Attention in DocLLM.  
+
+<table><tr><td></td><td>ChartQA</td></tr><tr><td>OCR-free</td><td></td></tr><tr><td>UniDoc (Feng et al., 2023b)</td><td>10.9</td></tr><tr><td>DocPedia (Feng et al., 2023a)</td><td>46.9*</td></tr><tr><td>Monkey (Li et al., 2023)</td><td>54.0*</td></tr><tr><td>InternVL (Chen et al., 2023b)</td><td>45.6*</td></tr><tr><td>InternLM-XComposer2 (Dong et al., 2024)</td><td>51.6*</td></tr><tr><td>TextMonkey (Liu et al., 2024c)</td><td>58.2*</td></tr><tr><td>TextMonkey+ (Liu et al., 2024c)</td><td>59.9*</td></tr><tr><td>Qwen2-VL (Wang et al., 2024b)</td><td>61.9*</td></tr><tr><td>Text + Coordinates</td><td></td></tr><tr><td>LayTextLLMzero (Ours)</td><td>30.2</td></tr><tr><td>LayTextLLMall (Ours)</td><td>42.6*</td></tr></table>
+
+Table 10: Comparison with SOTA OCR-free MLLMs on ChartQA (accuracy). * denotes the use of the dataset's training set.
+
+frequently in the dataset.
+
+For improvement, we observed that SG-KIE performs better when processing complex answers that require the aggregation of multiple consecutive word-level OCR results, leading to more accurate and complete outputs, as illustrated in Figure 11.
+
+<table><tr><td>Dataset</td><td>FUNSD-</td><td>CORD-</td><td>SROIE-</td></tr><tr><td>LayTextLLMzero</td><td>79.6</td><td>81.3</td><td>87.0</td></tr><tr><td>LayTextLLMzero-sg</td><td>80.0</td><td>81.9</td><td>86.0</td></tr></table>
+
+# I Decoding Bounding Box Coordinates
+
+We also evaluate the model's ability to decode bounding box embeddings into coordinates. Since the SG-KIE task requires the model to generate precise coordinates for answers, this task can be used to assess the performance in accurately predicting bounding boxes. Specifically, we select the examples with correct predictions for textual answer and compute the Intersection over Union (IoU) score (Rezatofighi et al., 2019) between the predicted and ground truth coordinates. We tested the on three datasets: FUNSD, which is not used to train LayTextLLM<sub>zero</sub>. If the IoU exceeds 0.5, we consider the bounding box prediction to be correct.
+
+Accuracy is used as the metric to evaluate this capability, we compute accuracy for the coordinates for both key and value. Results show that about  $77.5\%$  bounding box is correctly predicted, cases are visualized in Figure 12. Also, we visualize the coordinates prediction for the pre-training task—line-level layout decoding—in Figure 13. Moreover, SG-KIE produces coordinates, which is obviously interpretable, and providing coordinates seems to be more valuable for certain downstream tasks.
+
+Table 11: Inference with SG-KIE vs. without SG-KIE (accuracy).  
+
+<table><tr><td>FUNSD</td><td>LayTextLLMzero</td></tr><tr><td>Accuracy</td><td>77.5</td></tr></table>
+
+Table 12: Coordinate prediction accuracy.
+
+# J Combination with MLLMs
+
+As discussed in Limitation (Section 5), Lay-TextLLM faces challenges with VQA tasks that require the comprehension of visual elements such as font, size, shape, objects, color, and other visual attributes. To address this limitation, we conducted a preliminary experiment combining LayTextLLM with a MLLM to explore the potential of leveraging visual information while preserving the strengths of LayTextLLM.
+
+Specifically, we upgrade the multimodal version of LayTextLLM by building upon Qwen2-VL and incorporating a SLP. For simplicity, neither P-LoRA nor special tokens are introduced. We layout-text alignment pre-trained and SFT the modified Qwen2-VL on the same datasets used for LayTextLLM<sub>zero</sub>, resulting a Qwen2-VL-LayText model. We also trained a counterpart of Qwen2-VL-LayText by incorporating only OCR text, excluding layout information. This model, which is identical in training settings to Qwen2-VL-LayText, was named Qwen2-VL-Text and serves as a baseline. The model performance can be seen in Table 13. Although it shows a slight drop in performance on VQA tasks, Qwen2-VL-LayText achieves significant improvements in KIE tasks, with an overall accuracy of  $76.4\%$  compared to
+
+$67.7\%$ . This further demonstrates the effectiveness of interleaving layouts and text. Interestingly, simply adding OCR text (i.e., Qwen2-VL-Text) also results in a notable improvement in KIE tasks when paired with Qwen2-VL. We believe this is because datasets with poor performance, such as CORD and SROIE, primarily consist of text with small or blurred fonts. In these cases, off-the-shelf OCR engines still outperform MLLMs in text recognition.
+
+<table><tr><td></td><td colspan="3">Document-Oriented VQA</td><td colspan="4">KIE</td></tr><tr><td></td><td>DocVQA</td><td>InfoVQA</td><td>Avg</td><td>FUNSD</td><td>CORD</td><td>SROIE</td><td>Avg</td></tr><tr><td>Metric</td><td colspan="7">ANLS %</td></tr><tr><td>Visual + Text + Coordinates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Qwen2-VL (Wang et al., 2024b)</td><td>81.4</td><td>45.2</td><td>63.3</td><td>53.2</td><td>71.3</td><td>78.8</td><td>67.7</td></tr><tr><td>Qwen2-VLtext</td><td>77.0</td><td>43.5</td><td>60.2</td><td>46.0</td><td>90.2</td><td>83.5</td><td>73.2</td></tr><tr><td>Qwen2-VL LayText</td><td>81.4</td><td>42.7</td><td>62.1</td><td>54.2</td><td>91.2</td><td>83.7</td><td>76.4</td></tr></table>
+
+Table 13: Comparison with Qwen2-VL-LayText with other baselines (accuracy).
+
+![](images/2541fee027f6db38c7020b0cba62ead4d59adbfa755b37bf5f47ddfb86480e8b.jpg)
+
+![](images/addef4e5074cc1afcbc44b924457437e7620a40848c50579357d8c3b674c2a80.jpg)
+
+![](images/b515d101bb24f3a2e813d9df4d6ad931dc25242f596aca4c86dc1a76ed70d8d2.jpg)  
+Figure 8: Qualitative comparison with the baseline method.
+
+![](images/fdd36b58eb310c1503864d0a7f9108cced04f59cd8fe69ecdd9087b6fd8138b6.jpg)
+
+![](images/64cb64c13f59c8ed8be0166594f43c511ffb4c402ab8ad49dfdc373eac6b5d0f.jpg)  
+(a) Attention map of the first layer.
+
+![](images/3da8ff5ce7542c69de8ae25fca969c7077946c6b9532785e27db05f90abb0512.jpg)  
+(b) Attention map of the last layer.
+
+![](images/4beabbfd4ba3691f2d342b0cd2468d0a6c90438167cf31ca4d8931cf8551bba2.jpg)  
+(c) Attention score visualization of bounding box tokens.
+
+![](images/081ce9d5c4d9068774d8fdd207cd5647c43775e6a894a47e1f492ebbe1f689cc.jpg)  
+Figure 9: Visualization of attention maps of LayTextLLM. Best viewed in color and with zoom. “<unk>” is the placeholder for the bounding box token.  
+! What is the "total" in the given document? !
+
+GroundTruth: 37.90
+
+Our Prediction: 15.57[<B742> <B694> <B841> <B712>]
+
+![](images/314aeb1f04b8d16c742d13f67a2c929172faa18a9d2eebc97d25b95e2ba6401b.jpg)  
+Figure 10: A failure case of SG-KIE in SROIE $^{-}$ . The red box indicates the ground truth and the green box is the prediction.  
+Figure 11: A good case of SG-KIE in FUNSD $^{-}$ . The red box indicates the ground truth value and the green box is the key.
+
+# What is the content in the "application of shields:" field?
+
+Normal Prediction: The displays are easily assembled and durable. Some questions have been raised concerning the inability to be flush with the counter and / or against the register.
+
+SG-KIE Prediction: application of shields:[B110>B601>B260>B615>] 's value is:\nThe displays are easily assembled and durable. Some questions have been raised concerning the inability to be flush with the counter and / or against the register. As well as the ability to place this or the Back Bar if the settlement goes through[B107>B594>B762>B720]
+
+![](images/1290c3950ae4cb767e9a2b8d97868d29fed29e4f2279ac54fcc0ab7b5e570016.jpg)  
+(a) Question: what is the content in the "Date:" field?  
+Answer: December 9, 1999  
+(b) Question: what is the content in the "Pages (Including  
+"Cover)" field?  
+Answer: 4  
+Figure 12: Illustration of coordinates prediction for entity linking task. The red box indicates the key text region and the green box indicates the value text region.
+
+![](images/4cc14941a2c50134e9e19497b1196f734a133fb691b3d8aa273c3f4fdec29e9f.jpg)  
+(a) FUNSD
+
+![](images/ed71f5250bc0ae4cdd14bc70fdf4f461179470e0b556db47cbab4c754805fd57.jpg)  
+(b) FUNSD
+
+![](images/0f9f92dcd5647216f9caf58bf05fbfc6f76ca6e7b7b5f138bdfb60c630f95b6f.jpg)  
+(c) POIE  
+Figure 13: Illustration of coordinates prediction line-level layout decoding. Documents are subsampled from OOD dataset. Red boxes are coordinates for line-level text regions.

2025/A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding/images.zip

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2025/A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs/d7c200dd-70ce-444e-bec2-b6dfded0db32_origin.pdf

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2025/A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs/full.md

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+# A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs
+
+V.S.D.S. Mahesh Akavarapu $^{\alpha}$ , Hrishikesh Terdalkar $^{\beta}$ , Pramit Bhattacharyya $^{\gamma}$ , Shubhangi Agarwal $^{\beta}$ , Vishakha Deulgaonkar $^{\gamma}$ , Pralay Manna $^{\gamma}$ , Chaitali Dangarikar $^{\gamma}$ , Arnab Bhattacharya $^{\gamma}$
+
+$^{\alpha}$ University of Tübingen,  $^{\beta}$ University of Lyon 1,  $^{\gamma}$ Indian Institute of Technology Kanpur mahesh.akavarapu@uni-tuebingen.de, hrishikesh.terdalkar@liris.cnrs.fr, arnabb@cse.iitk.ac.in
+
+# Abstract
+
+Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across diverse tasks and languages. In this study, we focus on natural language understanding in three classical languages—Sanskrit, Ancient Greek and Latin—to investigate the factors affecting cross-lingual zero-shot generalization. First, we explore named entity recognition and machine translation into English. While LLMs perform equal to or better than fine-tuned baselines on out-of-domain data, smaller models often struggle, especially with niche or abstract entity types. In addition, we concentrate on Sanskrit by presenting a factoid question—answering (QA) dataset and show that incorporating context via retrieval-augmented generation approach significantly boosts performance. In contrast, we observe pronounced performance drops for smaller LLMs across these QA tasks. These results suggest model scale as an important factor influencing cross-lingual generalization. Assuming that models used such as GPT-4o and Llama3.1 are not instruction fine-tuned on classical languages, our findings provide insights into how LLMs may generalize on these languages and their consequent utility in classical studies.
+
+# 1 Introduction
+
+Large Language Models (LLMs) (Brown, 2020; Ouyang et al., 2022; Touvron et al., 2023) are known to generalize across various tasks using data from languages present in their pre-training phase, even when not present in instruction tuning (Wang et al., 2022; Muennighoff et al., 2023; Han et al., 2024). Previous work has demonstrated generalization to several low-resource languages via few-shot in-context learning (Cahyawijaya et al., 2024). In this study, we focus on zero-shot generalization to natural language understanding (NLU) tasks for three classical languages—Sanskrit (san), Ancient Greek (grc), and Latin (lat)—with a primary focus on Sanskrit. These languages represent
+
+a unique category of low-resource languages – despite scarce data for downstream NLU tasks, they have rich ancient literature available in digitized formats (Goyal et al., 2012; Berti, 2019), and they exert significant influence on the vocabulary and narrative structures of better-resourced languages (e.g., Latin contributes approximately  $28\%$  of English vocabulary (Finkenstaedt and Wolff, 1973)). Moreover, the high inflection of these languages presents a challenge.
+
+To investigate these issues, we conduct two sets of zero-shot experiments using gpt-4o (OpenAI, 2024; OpenAI et al., 2023), llama-3.1-405b-instruct (Dubey et al., 2024), and their smaller variants. First, we assess performance on two NLU tasks with available datasets for all three languages, namely, named entity recognition (NER) and machine translation to English (MT). We observe that larger models perform comparably or even better than previously reported fine-tuned models on out-of-domain data. Second, we focus on Sanskrit by introducing a factoid closed-book QA dataset and show that question-answering performance improves with retrieval augmented generation (RAG) (Lewis et al., 2020) when the model is provided with relevant texts. The tasks are illustrated in Figure 1.
+
+Given the recent nature of these datasets relative to the models' knowledge cut-off dates, and the likelihood that instruction-tuning on these languages is limited, the robust performance observed can be attributed to cross-lingual generalization. We refer to our prompting strategy as zero-shot, as it includes no task-specific examples, and it is unlikely that such examples in these languages were present in the models' training or instruction-tuning data. In both experimental setups, we find that smaller models struggle, particularly with niche entity types in NER, and in effectively leveraging contextual information via RAG, thereby suggesting model scale as an important factor.
+
+![](images/390a6ba20291b7e87dca290e9473fe63a037e6b79396998e023410b5df73682b.jpg)  
+Figure 1: The three NLU tasks evaluated on the classical languages: Named-Entity Recognition (top-left), Machine Translation (bottom-left) and Question-Answering (right).
+
+![](images/89daa06e56f942d3f46136c12e4f77b14451c42cf6339c11c0fc6195e0bc0c41.jpg)
+
+<table><tr><td>Task</td><td>Language</td><td>Source</td><td>Size</td></tr><tr><td rowspan="3">NER</td><td>san</td><td>Terdalkar (2023)</td><td>139</td></tr><tr><td>lat</td><td>Erdmann et al. (2019)</td><td>3410</td></tr><tr><td>grc</td><td>Myerston (2025)</td><td>4957</td></tr><tr><td rowspan="3">MT-en</td><td>san</td><td>Maheshwari et al. (2024)</td><td>6464</td></tr><tr><td>lat</td><td>Rosenthal (2023)</td><td>1014</td></tr><tr><td>grc</td><td>Palladino et al. (2023)</td><td>274</td></tr><tr><td>QA</td><td>san</td><td>Ours</td><td>1501</td></tr></table>
+
+Table 1: An overview of NLU datasets used in this study for Sanskrit (san), Latin (lat) and Ancient Greek (grc). QA dataset for san is contributed in this work. Size represents the number of sentences of test sets (wherever train-test splits exist).
+
+# 2 Datasets and Methods
+
+The datasets used in our experiments are summarized in Table 1. Our aim is to evaluate zero-shot capabilities where evaluation is done directly on test data without fine-tuning on the training data. Thus, we only consider the test sets of these datasets. Notably, the Sanskrit NER dataset (san) is the smallest, comprising 139 sentences (1558 tokens) (Terdalkar, 2023). In addition to these publicly available datasets, we contribute a new factoid closed-domain QA dataset in Sanskrit, described in detail in Section 2.1.
+
+We evaluate the zero-shot capabilities of both large and small variants of our models: proprietary (gpt-4o and gpt-4o-mini (OpenAI, 2024)) and open-source (llama-3.1-405b-instruct and llama-3.1-8b-instruct (Dubey et al., 2024)). According to official documentation, these models have knowledge cut-off dates at the end of 2023. Many datasets considered in this work (Table 1) are released beyond these dates, in other words, they are unlikely to be seen in the pre-training data of
+
+these models. Given that none of the documentation indicates explicit instruction tuning on Sanskrit, Ancient Greek, or Latin, any observed performance in these languages is likely attributable to cross-lingual generalization. Previous zero-shot applications of LLMs to classical languages have been limited to Latin machine translation and summarization (Volk et al., 2024), although several works have been dedicated to building language models for these languages (Riemenschneider and Frank, 2023; Nehrdich et al., 2024), however, with fine-tuning restricted to morphological parsing-related tasks like dependency parsing (Nehrdich and Hellwig, 2022; Hellwig et al., 2023; Sandhan et al., 2023).
+
+All prompts used for these tasks are provided in Appendix A. The prompts are tested in both English and the respective languages.
+
+# 2.1 Sanskrit QA
+
+To further evaluate comprehension, we focus on question-answering (QA) in Sanskrit – a domain with very limited datasets. The only existing dataset by Terdalkar and Bhattacharya (2019) comprises 80 kinship-related questions. To address this gap, we created a new dataset containing 1501 factoid QA pairs covering distinct domains in Sanskrit: epics and healthcare. Specifically, we selected two key texts: (1) Rāmāyāna, an ancient Indian epic, and (2) Bhāvaprakāśanighaṇṭu, a foundational text on āyurveda. Details of the dataset are provided in Appendix B.
+
+For QA evaluation, we employ a closed-book setting using prompts detailed in Appendix A.3. To emulate extractive QA, we implement a Retrieval-Augmented Generation (RAG) approach by retrieving the top- $k$  relevant passages ( $k$  tuned to
+
+![](images/58cb3a5cdd656e8cdd3f376f4f1afa052462daa5073d6ef1f341585619ee08ab.jpg)  
+Figure 2: Effect of  $k$  on RAG, denoting the number of top best matching text chunks retrieved, on the performances of GPT-4o with retrievers based on BM25, averaged FastText (AvgFT) and GloVe (AvgGV) embeddings respectively of datasets Ramayana (left) and Bhavaprakasanighantu (right).
+
+4) from the original Sanskrit texts using BM25 (Sparck Jones, 1972; Robertson et al., 2009). We also compare BM25 with embedding-based retrievers—FastText (Bojanowski et al., 2017) and GloVe (Pennington et al., 2014)—and vary  $k$  to assess performance using gpt-4o with Sanskrit prompts. As shown in Fig. 2, BM25 consistently outperforms the embedding-based methods, and  $k = 4$  emerges as an optimal choice across metrics.
+
+To examine whether the inclusion of answer-bearing contexts benefits model performance, we manually annotated the relevance of retrieved passages. Since BM25 relies on lexical similarity, typically favoring lemmas over inflected forms, we introduce a lemmatization step using a transformer-based Seq2Seq Sanskrit lemmatizer trained on the DCS corpus (Hellwig, 2010-2024), achieving a mean F1 score of 0.94 on a held-out test set. Further details on RAG and lemmatization are provided in Appendix C, and implementation details in Appendix D. Code and data are available at https://github.com/mahesh-ak/SktQA.
+
+# 3 Results
+
+Figure 3 presents our zero-shot evaluation results, demonstrating that larger LLMs exhibit robust cross-lingual generalization across four NLU tasks—named entity recognition (NER), machine translation (MT), closed-book QA, and extractive QA (simulated via RAG-BM25)—in three classical languages (with QA evaluated solely on Sanskrit). To assess variability, each test set is segmented into 10 chunks during evaluation resulting in a box-plot. Larger models perform better than previous finetuned models on out-of-domain data as reported in Appendix E. Notably, when answer-bearing contexts are provided (Fig. 3d) versus when they are absent (Fig. 3e), the models show significant perfor
+
+mance gains, suggesting comprehension abilities in Sanskrit, a language characterized by high inflection. This behavior is however, not exhibited by smaller models when prompted in Sanskrit.
+
+# 3.1 Prompt Language: English versus Native
+
+During evaluation, we prompted models both in English and in each target language. As shown in Figure 3, English prompts generally outperform Sanskrit prompts, particularly with smaller models, providing partial evidence that these models have not been instruction-tuned on Sanskrit (Muenninghoff et al., 2023). For Latin and Ancient Greek, this English-prompt advantage holds mainly for smaller models; larger models perform equally well, or even better, with native-language prompts (e.g., in Latin NER). This does not imply instruction tuning in these languages, since larger and smaller models likely saw comparable amounts of tuning data. Rather, it suggests that cross-lingual transfer is especially strong for Latin and Ancient Greek in larger models, reflecting their substantial influence on high-resource languages such as English.
+
+# 3.2 Misclassified Entities in NER
+
+Figure 4 displays confusion matrices for the NER task. Across all three languages, the smaller models exhibit more confusion among semantically related classes (see G for descriptions of entity types), while the larger models show fewer off-diagonal errors. In san, mythological entities like Deva, Asura, and Rakshasa or semantically closed entities like Kingdom versus City (e.g., Košala vs Ayodhyā) or Forest (e.g., Dāndaka) versus Garden (e.g., Nandana) often get misclassified with each other in the smaller models. For lat, entity type GRP proves troublesome for the smaller models, suggesting struggles in separating individual entities from collective ones. In grc, categories such as LOC and ORG show higher confusion counts akin to GRP in lat while confusion between God and Persons seems similar to what was discussed for Sanskrit. In contrast, much clearer boundaries emerge in the larger models' confusion matrices. In many of these cases, the domain or style of the texts, especially if they involve mythological or archaic terms typical of classical texts, can be understood to influence performance, as models with less exposure to specialized terminology may conflate related entity types.
+
+![](images/222a47a496d1638a21ebacf06b02d5af3ecc8ca16042eb9e104ee945f4f26e93.jpg)
+
+![](images/2867929459cca16e8467fb55a245c02fca08e071539dc712aabae170b0f26f5b.jpg)
+
+![](images/a72e2a2555cc5db33a8449cd90c2cab19b4a9ab196cacb33575a7760b1f69dc8.jpg)
+
+![](images/95301b0bc637595f8ec469fe94bcb9c86416548f103ed806f89341f19cf4a5b4.jpg)  
+Figure 3: Zero-shot evaluation of LLMs on three NLU tasks for classical languages (language codes in ISO 639-2). Prompts when in English are denoted by  $\langle \text{en} \rangle$ , otherwise are in respective languages. Larger LLMs are represented in red and orange, while smaller LLMs in blue and purple. First row displays the performances on NER (a) and MT (to en) (b) for all three languages. Second row displays the performances on QA for Sanskrit alone. Out of 1501 QA pairs considered (c), 607 QA pairs are with answer present in at least one of the  $k = 4$  contexts extracted by BM25 and 894 QA pairs with answer not inferable from contexts, which are respectively considered in (d) and (e).
+
+![](images/3b7a8990b5789e78f0a4bee18987e6973e72c154c19c1cfa6674099f34ccab42.jpg)
+
+![](images/6557002d18e14854b80ecaf5a37177aa28a081724befa8add85cb3a824453450.jpg)
+
+<table><tr><td rowspan="2">LLM</td><td colspan="2">Closed-book</td><td colspan="2">+ RAG-BM25</td></tr><tr><td>Inflected</td><td>Lemmatized</td><td>Inflected</td><td>Lemmatized</td></tr><tr><td>gpt-4o</td><td>0.36</td><td>0.37</td><td>0.46</td><td>0.48</td></tr><tr><td>llama-3.1-405b-instruct</td><td>0.41</td><td>0.40</td><td>0.42</td><td>0.44</td></tr><tr><td>gpt-4o-mini</td><td>0.18</td><td>0.20</td><td>0.25</td><td>0.28</td></tr><tr><td>llama-3.1-8b-instruct</td><td>0.13</td><td>0.15</td><td>0.09</td><td>0.10</td></tr></table>
+
+Table 2: Comparison of EM scores in san QA (English prompts) when predicted and gold answers are considered with inflection or lemmatized.  
+
+<table><tr><td rowspan="2">LLM</td><td colspan="2">MT (BLEU)</td><td colspan="2">NER (Macro F1-BI)</td></tr><tr><td>Devanagari</td><td>IAST</td><td>Devanagari</td><td>IAST</td></tr><tr><td>gpt-4o</td><td>0.179</td><td>0.165</td><td>0.637</td><td>0.599</td></tr><tr><td>llama-v3p1-405b-instruct</td><td>0.193</td><td>0.148</td><td>0.561</td><td>0.556</td></tr><tr><td>gpt-4o-mini</td><td>0.135</td><td>0.099</td><td>0.359</td><td>0.318</td></tr><tr><td>llama-v3p1-8b-instruct</td><td>0.120</td><td>0.063</td><td>0.164</td><td>0.149</td></tr></table>
+
+Table 3: Comparison of performances in san MT and NER (English prompts) when the input sentences are given Devanagari script or in IAST script.
+
+# 3.3 Inflection in Answers in Sanskrit QA
+
+In the Sanskrit question-answering task, models are expected to generate single-word answers with the correct inflection. For computing exact match (EM) scores, we manually identified all acceptable answers, excluding those with incorrect inflection (e.g., wrong case or gender endings). To quantify inflection errors, we also calculated EM scores on lemmatized versions of the gold standard and predicted answers, as shown in Table 2. Most models show only a slight increase in EM scores on lemmatized answers, suggesting that inflection errors are relatively minor, a finding corroborated by manual
+
+inspection. Future work could extend this analysis to investigate inflection accuracy in full sentence generation within broader natural language generation scenarios.
+
+# 3.4 Sanskrit Orthography: Devanagari versus IAST
+
+So far, we have shown robust cross-lingual generalization in the models. We now turn to one possible underlying mechanism—orthographic transfer—where models benefit from shared scripts across languages. Prior work has identified orthography as a key factor in cross-lingual transfer for LLMs
+
+![](images/e883ef82bc4da90e58bc8601108ef8d58dc045a3427ee940f6085322685e01b1.jpg)  
+Figure 4: Confusion matrices from the NER task in san (a-d), lat (e-h) and grc (i-l), all with <en> prompts, normalized across rows.
+
+(Muller et al., 2021; Fujinuma et al., 2022). To isolate this effect, we re-ran our Sanskrit NER and MT experiments (using English prompts) in Roman-based IAST transliteration instead of Devanagari. Table 3 compares performance in both scripts. Models perform better with the Devanagari script, which is shared by higher-resource relatives like Hindi and Marathi, reinforcing the importance of script sharing. However, results in IAST are only slightly lower, suggesting that Roman-based transliterations also feature prominently in the pre-training data. In future, we will investigate whether model outputs are consistent across both scripts, that is, whether these LLMs are effectively digraphic in Sanskrit.
+
+# 3.5 Knowledge-Graph Question-Answering
+
+Additionally, we explore the use of knowledge graphs (KGs) for Sanskrit QA. We evaluated a KG derived from the Bhāvaprakāśanighaṇṭu text (Terdalkar et al., 2023) and constructed a small KG for Ramāyāna (details in Appendix F). Using the Think-On-Graph (ToG) paradigm (Sun et al., 2024), which iteratively explores the KG paths for answer retrieval in a training-free zero-shot manner (Xu et al., 2024), we observed that gpt-4o could effectively execute this method. Although it occasionally extracted correct answers, its performance did not significantly exceed that of the closed-book setting, most likely due to the incompleteness of the KGs (see §F.3). Future work may focus on developing more comprehensive KGs to enhance
+
+this retrieval method.
+
+# 4 Conclusions
+
+In summary, our zero-shot evaluations demonstrate that larger language models exhibit robust crosslingual generalization across diverse natural language understanding tasks in classical languages, including NER, machine translation, and QA. Notably, the significant performance gains achieved when answer-bearing contexts are provided, particularly in Sanskrit QA, suggest comprehension abilities in highly inflected languages. Moreover, our contribution of a novel Sanskrit QA dataset provides a valuable resource for evaluating and benchmarking LLM performance on classical language tasks. Importantly, these models have not been explicitly instructed on Sanskrit, Latin, or Ancient Greek—evidenced by the superior performance achieved when using English prompts for Sanskrit—which indicates that their zero-shot performance is attributable solely to cross-lingual generalization.
+
+Future work will focus on expanding dataset coverage, knowledge graphs and exploring additional classical languages and tasks, further advancing our understanding of cross-lingual generalization in LLMs and its applications in digital humanities and multilingual NLP research.
+
+# Acknowledgements
+
+This research is financially supported by the Indian Knowledge Systems (IKS) Division of Ministry of Education, Govt. of India (project number AICTE/IKS/RFP1/2021-22/12). Mahesh Akavarapu received funding from Volkswagen Foundation under the Phylomilia project within the Pioneering Projects funding line. We also thank anonymous reviewers and the Area Chairs for their comments that have helped improve the paper.
+
+# Limitations
+
+While our study demonstrates robust cross-lingual generalization in large language models for classical languages, several limitations warrant discussion. First, our newly contributed Sanskrit QA dataset, although valuable, is limited in size. Our evaluation relies exclusively on zero-shot performance, as the models have not been explicitly instructed tuned on these languages; this design choice may obscure potential benefits achievable through targeted fine-tuning. Further, a few datasets we experimented were released within the models' knowledge cut-off dates raising the issue of data contamination. Among these, only Ancient Greek MT exhibits anomalously high performance, suggesting possible exposure. In general, NER, owing to its structural data should be less susceptible to contamination than MT. Furthermore, the effectiveness of our BM25-based retrieval approach depends heavily on preprocessing steps such as lemmatization, which might not optimally address all linguistic variations in highly inflected languages. Finally, our comparisons are based on a limited set of proprietary and open-source models, and future work should extend this analysis to a broader range of models and tasks to fully understand the nuances of cross-lingual generalization in classical languages.
+
+# Ethics Statement
+
+Classical Sanskrit epics hold deep cultural and religious significance in Indian traditions, and similarly, āyurveda represents a revered tradition-bound area within healthcare. We acknowledge that any research involving these subjects must be conducted with particular care. It is essential to note that, as with conventional treatment, Āyurvedic practices require professional consultation and should not be substituted by automated responses. Although our experiments indicate that
+
+paradigms like RAG produce more grounded and, hence, potentially safer outputs, there is no assurance that the responses from current LLMs in these domains meet clinical or religious safety standards. Consequently, the authors do not endorse using the datasets beyond the scope of linguistic research. These datasets are released for open-source, non-commercial use, and all annotators have been compensated at fair, standard rates.
+
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+
+# Appendix
+
+# A Prompts
+
+The Sanskrit prompts are in Devanagari script. In this appendix, we provide these prompts transliterated in IAST scheme.
+
+# A.1 Prompts for Named Entity Recognition
+
+# Prompt in English
+
+Recognize the named entities from the following sentence in {LANGUAGE}. The valid tags are {ENTITY TYPES}. Do not provide explanation and do not list out entries of 'O'. Example:
+
+```txt
+Sentence: <word_1><word_2><word_3><word_4><word_5>  
+Output: {{‘B-<entity1>': ['<word_1>','<word_4>'], 'B-<entity2>':['<word_5>]}}  
+Sentence: {INPUT}
+```
+
+Output:
+
+(The example is never a real sentence and is only provided to specify the output structure. Hence, the evaluations are strictly zero-shot.)
+
+# Prompt in Sanskrit
+
+adho datta vakyne namakrtah sattvah (named entities) abhijanhi. tadapi vivrtam ma kuru, kevalam prsta visayasya uttaram dehi. api ca 'O'-sambandhitani na deyani.
+
+sattvāh etesu vargaesu vartante - {ENTITY TYPES}. udāharanāya -
+
+vakyam: <padam_1> <padam_2> <padam_3> <padam_4> <padam_5>
+
+phalitam: {{ 'B-<sattvah1>': ['<padam_1>', <padam_4>'], 'B-<sattvah2>': ['<padam_5>]}}
+
+vakyam: {INPUT}
+
+phalitam:
+
+# Prompt in Latin
+
+Agnosce nomina propria (named entities) ex hac sententia Latina. Notae validae sunt {ENTITY TYPES}. Explanationem ne praebeas nec elementa 'O' elenca. Exemplar:
+
+Sententia: <verbum_1> <verbum_2> <verbum_3> <verbum_4> <verbum_5>
+
+Productus: {{‘B-<entitatem1>': ['<verbum_1>', <verbum_4>'], 'I-<entitatem1>': ['<verbum_2>'], 'B-<entitatem3>':['<verbum_5>']}}
+
+Sententia: {INPUT}
+
+Productus:
+
+# Prompt in Ancient Greek
+
+'Aναγνύρισον τὰ ὄνόματα (named entities) εκ τηςδε τής ἀλληνικής περιόδου. τὰ ἐγκυρα είδη εύστιν {ENTITY TYPES}.
+
+NORP  $\sigma \eta \mu \alpha \acute{\iota} \nu \varepsilon \iota$  'e  $\theta \nu \eta$  (oiov 'E  $\lambda \lambda \eta \nu \varepsilon \varsigma$ , περσαι), 'εθνωνύμα, και 'αλλας κοννωνικός 'ομάδας (oiov θρησκεντικός 'opγανώσεις).
+
+Mη παρέχου ἔξήγησων ἄν τὴν αποκρίσει μηδέ τὰ εἰς ‘O’ ἀγγεγραμεύνα παρατίθεσο. παράδενγμα:
+
+πρότασις: <λέξις_1> <λέξις_2> <λέξις_3> <λέξις_4> <λέξις_5>
+
+$\pi \alpha \rho \alpha \gamma \omega \gamma \eta : \{\{B-<O\nu \tau \acute{o} \tau \eta_{S}1>': ['<\lambda \acute{\varepsilon}\xi \iota_{S}-1>, '<\lambda \acute{\varepsilon}\xi \iota_{S}-4>'], B-<O\nu \tau \acute{o} \tau \eta_{S}2>: ['<\lambda \acute{\varepsilon}\xi \iota_{S}-5>']\}\}$
+
+$\pi \rho \acute{\sigma} \tau \alpha \sigma \iota \varsigma : \{INPUT\}$
+
+παραγωγη:
+
+# A.2 Prompts for Machine Translation
+
+# Prompt in English
+
+Translate the following sentence in {LANGUAGE} into English. Do not give any explanations.
+
+# Prompt in Sanskrit
+
+adho datta-samskrta-vakyam angle anuvadaya, tad api vivrtam maku -
+
+# Prompt in Latin
+
+Verte hanc sententiam Latinam in Anglicam. Nullam explicationem praeve.
+
+# Prompt in Ancient Greek
+
+Mεταφρασον τήνδε τήν ‘Eλληνικήν πρότασιν εἰς τήν ‘Aγγλικήν. Mηδεμίαν ‘εξήγησιν παρέχου.
+
+# (Sanskrit QA Prompts)
+
+In the following prompts, TOPIC is either 'Rāmāyana' or 'Ayurveda'.
+
+# A.3 Prompts for Closed-book QA
+
+# Prompt in English
+
+Answer the question related to {TOPIC} in the Sanskrit only. Give a single word answer if reasoning is not demanded in the answer. With regards to how-questions, answer in a short phrase, there is no single word restriction.
+
+{QUESTION} {CHOICES}
+
+# Prompt in Sanskrit
+
+tvaya samskrta-bhasayam eva vaktavyam. na tu anyasu bhasasu. adhah {TOPIC}-sambandhe prsta-prasnasya pratyuttaram dehi. tadapi ekenaiva padena yadi uttare karaanam napekshitam. katham kimartham ityadisu ekena laghu vakyena uttaram dehi atru eka-pada-niyamaḥ nasti.
+
+{QUESTION} {CHOICES}
+
+# A.4 Prompts for RAG-QA
+
+# Prompt in English
+
+Answer the following question related to {TOPIC} in Sanskrit only. Give a single word answer if reasoning is not demanded in the answer. With regards to how-questions, answer in a short phrase. Also take help from the contexts provided. The contexts may not always be relevant."
+
+contexts: { CONTEXTS}
+
+question:{QUESTION} {CHOICES}
+
+# Prompt in Sanskrit
+
+tvayā samskrta-bhāṣāyām eva vaktavyam. na tu anyāsu bhāṣāsu. adhaḥ {TOPIC}-sambandhe prṣṭa-prasnasya pratyuttaram dehi. tadapi ekenaiva padena, yāvad laghu sakyam tāvad, tam punah vivṛtam mā kuru. api ca yathā'vasyam adhaḥ datta-sandarbhebhyah ekatamāt sahāyyam grhāṇa. tattu sarvādā sādhu iti na'sti pratītiḥ.
+
+sanderbhah:{CONTEXTS}
+
+prasnah: {QUESTION} {CHOICES}
+
+# B Question Answering Dataset
+
+In this appendix, we describe the creation of Sanskrit QA dataset.
+
+We referred to two books that contain multiple-choice questions (MCQs) with answers: one comprising 1000 MCQs on Ramayana (Singh, 2009), and another featuring a collection of 2600 questions from three prominent texts of Ayurveda (Phull and Phull, 2017). The questions and options in these books are in Hindi language.
+
+We carefully selected a relevant subset of questions from these books, including all 1000 questions from Ramayana dataset and 431 from that of Ayurveda. These questions are then translated into Sanskrit with the help of experts in the language who are also familiar with the original Sanskrit texts. Further, we consulted with a specialist in Ayurveda to review and discard incorrect question-answer pairs, as well as to generate 70 new questions based on Bhavaprakasanighantu. Ultimately, the question-answering dataset consists of 1501 questions.
+
+The answers typically agree in grammatical case with the corresponding interrogative of the question. The following is a question-answer pair as an illustration<sup>1</sup>:
+
+Q: sīṭala-jalasya
+
+pānam
+
+kasmin
+
+roge
+
+nisiddham
+
+asti?
+
+A: gala-grahe
+
+Q: cold-water.gen
+
+drinking
+
+what.loc
+
+disease.loc
+
+forbidden
+
+is
+
+A: pharyngitis.loc
+
+Question: During which condition is the drinking of cold water forbidden? Answer: During pharyngitis.
+
+Most questions in the datasets have a single-word answer except a few including those in the Ramayana that fall under the category 'Origins' (Table 4). An example question-answer pair under this category that demands reasoning in the answer:
+
+Q: raja-sagarena sagarah itinama kutah praptam?
+
+"How did King Sagara obtain such a name?"
+
+A: saha tena garenaiva jatah sa sagaro 'bhavat
+
+"He was indeed born along with (sa-) the poison (gara), thus he became Sagara."
+
+For such questions (only about 50), the answers can be paraphrased variously, thereby requiring manual evaluation.
+
+The broad semantic and domain-specific categories of the questions are detailed in Tables 4 and 5.
+
+# C Retrieval Augmented Generation
+
+In the RAG paradigm, the LLM is provided with additional context that consists of top- $k$  passages retrieved from the original texts. The texts of Ramayaña and Bhāvaprakāśanighaṇṭu are obtained from GRETIL<sup>2</sup> and Sanskrit Wikisource<sup>3</sup> respectively. The texts are pre-processed following standard procedures (Manning, 2008), namely, dividing the texts into chunks, followed by lemmatization, and then building a document store. Lemmatization would not have been necessary if retrieval frameworks such as Dense Passage Retrieval (Karpukhin et al., 2020) or a vector space retrieval framework with SentenceBERT embeddings (Reimers and Gurevych, 2019) could be used. However, due to insufficient data in Sanskrit, such models cannot be trained now. Hence, we used BM25 retrieval and vector space retrieval with averaged FastText (AvgFT) (Bojanowski et al., 2017) and GloVe (Pennington et al., 2014) (AvgGV) embeddings, which are employed on lemmatized documents and queries. To achieve this, a lemmatizer for Sanskrit was built as described below.
+
+# Sanskrit Lemmatizer
+
+Seq2Seq transformer-based Sanskrit lemmatizer was trained from the words and their respective lemmas present in the DCS corpus (Hellwig, 2010-2024) $^{4}$ . During lemmatization, if a word in an input sentence is a compound word or involves Sandhi, the lemmatizer is expected to break the word into sub-words and generate their respective lemmas in the output. For example, if the input sentence is 'haridramalakam grhnati', the corresponding lemmatized output should be 'haridra amalaka grh'. Our lemmatizer achieves a mean F1-score of 0.94 across the sentences from the held-out test set (Appx. D) calculated according to Melamed et al. (2003), however with a significant standard deviation of 0.11. While the accuracy is high, future attempts for improvements should focus on minimizing the variance, which is rarely ever reported although important.
+
+The information retrieval pipelines thus formulated can be considered novel concerning Classical Sanskrit. A known earlier attempt towards building retrieval systems in Sanskrit (Sahu and Pal, 2023) focused on news corpora with much terminology consisting of borrowings from Hindi and even English. As a result, the lemmatizer trained on Classical Sanskrit and thereby, our entire retrieval pipeline may not be appropriate on such corpora and hence are not comparable.
+
+The prompts for RAG are detailed in Appx. A.4.
+
+<table><tr><td>Category</td><td>Description</td><td>#Q</td></tr><tr><td>Names</td><td>Names of various characters</td><td>97</td></tr><tr><td>Actions</td><td>Who performed certain actions?</td><td>47</td></tr><tr><td>Origins</td><td>Origin of various names</td><td>49</td></tr><tr><td>Numeric</td><td>Questions with numerical answers</td><td>79</td></tr><tr><td>Quotes</td><td>Who said to whom?</td><td>31</td></tr><tr><td>Boons and Curses</td><td>Who endowed boons / curses on whom</td><td>31</td></tr><tr><td>Weapons</td><td>Questions related to various types of weapons</td><td>59</td></tr><tr><td>Locations</td><td>Locations of important events or characters</td><td>71</td></tr><tr><td>Kinship</td><td>Questions pertaining to human kinship relationships</td><td>133</td></tr><tr><td>Slay</td><td>Who slayed whom</td><td>49</td></tr><tr><td>Kingdoms</td><td>Which king ruled which kingdom</td><td>27</td></tr><tr><td>Incarnations</td><td>Who were incarnations of which deities</td><td>27</td></tr><tr><td>MCQ</td><td>Multiple choice questions</td><td>140</td></tr><tr><td>Miscellaneous</td><td>Other questions</td><td>196</td></tr></table>
+
+<table><tr><td>Category</td><td>Description</td><td>#Q</td></tr><tr><td>Synonym</td><td>Synonyms of substances</td><td>174</td></tr><tr><td>Type</td><td>Variants or types of substances</td><td>30</td></tr><tr><td>Property</td><td>Properties of substances</td><td>20</td></tr><tr><td>Comparison</td><td>Comparison between properties of various substances or their variants</td><td>24</td></tr><tr><td>Consumption</td><td>Related to consumption of medicine including suitability, method, accompaniments etc.</td><td>23</td></tr><tr><td>Count</td><td>Counting types or properties of substances</td><td>59</td></tr><tr><td>Quantity</td><td>Quantity of substances in various procedures or methods</td><td>21</td></tr><tr><td>Time-Location</td><td>Time or location in the context of substances or methods</td><td>17</td></tr><tr><td>Effect</td><td>Effect of substances</td><td>15</td></tr><tr><td>Treatment</td><td>Diseases and treatments</td><td>23</td></tr><tr><td>Method</td><td>Methods of preparation of substances</td><td>21</td></tr><tr><td>Meta</td><td>Related to the verbatim source text, the structure of the text and external references</td><td>38</td></tr><tr><td>Multi-Concept</td><td>About more than one aforementioned concepts</td><td>11</td></tr><tr><td>Miscellaneous</td><td>Miscellaneous concepts</td><td>24</td></tr></table>
+
+Table 4: Question Categories for Rāmāyana QA Dataset Table 5: Question Categories for Āyurveda QA Dataset  
+
+<table><tr><td>Model</td><td>BLEU</td></tr><tr><td>Google Trans (Maheshwari et al., 2024)</td><td>13.9</td></tr><tr><td>IndicTrans (Maheshwari et al., 2024)</td><td>13.1</td></tr><tr><td>gpt-4o</td><td>16.5</td></tr><tr><td>llama-3.1-405b-instruct</td><td>17.1</td></tr></table>
+
+MT (san-eng) on Mann ki Baat dataset
+
+<table><tr><td>Model</td><td>Macro F1 (BI)</td></tr><tr><td>LatinBERT1 (Beersmans et al., 2023)</td><td>0.54</td></tr><tr><td>LatinBERT2 (Beersmans et al., 2023)</td><td>0.50</td></tr><tr><td>gpt-4o</td><td>0.55</td></tr><tr><td>llama-3.1-405b-instruct</td><td>0.36</td></tr></table>
+
+NER (lat) on Ars Amatoria dataset
+
+Table 6: Comparison of out of domain performances of LLMs against previously reported fine-tuned models.
+
+# D Implementation
+
+This appendix outlines the implementation details. All LLMs are operated through API calls using LangChain<sup>5</sup>. In case of Llama-3.1, we used API provided by Fireworks AI<sup>6</sup>.
+
+The lemmatizer was implemented using HuggingFace transformers (Wolf et al., 2020) upon base model T5 (Raffel et al., 2020) initiated with the model configuration of 4 layers per each encoder and decoder, 4 attention-heads, embedding of size 256, and hidden size of 1024, totaling about 100M parameters. The tokenizer trained by Akavarapu and Bhattacharya (2023) was used<sup>7</sup>. The lemmatizer was trained for 15 epochs on DCS (Hellwig, 2010-2024) data with batch size of 32, that took about 15 hours on NVIDIA RTX 2080 with 11GB graphics memory. There are total 1.04M sentences in the data, that are randomly divided into proportions  $0.675:0.075:0.15$  respectively for training, validation and testing. FastText and GloVe embeddings are trained on lemmas obtained from DCS (Hellwig, 2010-2024) with embedding size 100.
+
+# E Supplementary Results
+
+In Table 6, we compare the out-of-domain performance of our evaluated models against previously reported fine-tuned models. For MT (san-eng) on Mann ki Baat dataset (Maheshwari et al., 2024), open-source model llama-3.1-405b-instruct outperforms both Google Trans and IndicTrans, while for NER (lat) on Ovid's Ars Amatoria dataset (Beersmans et al., 2023), the performance of gpt-4o is better than that of fine-tuned LatinBERT variants. Although fine-tuned models yield superior results on in-domain data, our findings indicate that multilingual LLMs are superior in their zero-shot generalization.
+
+![](images/efa9c8ab05c65a8c30303c374fa282fb5c6616dbfb1d4c99681695235dc9b30a.jpg)  
+Figure 5: Overview of augmenting a LLM with a knowledge graph (KG) through Think-on-Graph (ToG) paradigm.
+
+Arriving at an answer by an LLM integrated with a knowledge graph (KG) through Think-on-Graph (ToG) (Sun et al., 2024) paradigm involves several prompting steps for each hop from starting entity nodes as illustrated in Fig. 5. Firstly, the LLM lists entities from the input questions further lemmatized by our lemmatizer previously described. The relationships from and to these entities are then extracted by traversing the KG. The LLM then lists relationships with relevance scores, which are further used to prune the relationships, retaining only the best three. Unexplored entities connected by these relationships are then known from the KG, which are similarly pruned to retain the three most relevant ones. The LLM then reasons whether these extracted paths suffice to answer the given question. If no, the cycle is repeated, i.e., it traverses a hop further up to a depth  $d$ . Otherwise, the LLM answers using the context from the extracted paths.
+
+The prompts for each step and an outline pseudo-code can be found respectively in Appx. F.2, Alg. 1. Technical terminology such as 'entity', 'knowledge graph', and so forth are mostly retained in English in these prompts resulting in minimal and unavoidable code-mixing. Further, the output of these prompts is often a list of elements and, hence, has to abide by a structured format.
+
+# F.1 Knowledge Graphs
+
+A knowledge graph (KG) was constructed for Rāmāyaṇa using two key references, (Ray, 1984) and (Rai, 1965). The graph was annotated with the help of two experts proficient in both Sanskrit and Rāmāyaṇa. For annotation, we used a custom deployment of Sangrahaka (Terdalkar and Bhattacharya, 2021). The resulting knowledge graph contains 867 nodes and 944 relations, encompassing entities like characters of the story including humans and divine beings, places (cities, rivers, kingdoms), and animals, and relationships such as kinship, actions, locations, and others, highlighting associations between the characters, natural features, and other elements from the text.
+
+Additionally, a work-in-progress knowledge graph for Bhāvaprāśanighaṇṭu obtained from the authors of (Terdalkar et al., 2023) was referenced. The KG currently includes 4685 nodes and 10596 relations from 12 out of 23 chapters covering substances such as grains, vegetables, meats, metals, poisons, dairy products, prepared substances and other miscellaneous medicinal substances.
+
+The knowledge graphs were loaded and accessed through Neo4j $^{8}$ . Python package, indic-transliteration $^{9}$  is used to move among transliteration schemes of Sanskrit. The pseudocode for our implementation of ToG (Sun et al., 2024) is given in Algorithm 1. The sample limit  $S$  is set to 15, depth limit  $D$  to 1 and width limit  $W$  to 3.
+
+Algorithm 1 Outline of LLM-KG i.e., ToG (Sun et al., 2024)  
+Require: Input:  $x$  LLM prompt-chains: ExtractEntities, RelationPrune, EntityExtractPrune, Reason, Answer Interface to KG: FetchRelations, FetchEntities; Depth limit:  $D$ ; Sample limit for KG:  $N$ ; Width limit for LLM:  $W$  Current Entities  $E \leftarrow$  ExtractEntities(x) Current depth  $d \leftarrow 0$  Stored Paths  $P \leftarrow []$  while  $d < D$  do  $R \leftarrow$  FetchRelations(E, N)  $R \leftarrow$  RelationPrune(R, W)  $E, P \leftarrow$  FetchEntities(E, R, P, N)  $E, P \leftarrow$  EntityExtractPrune(E, R, P, W) if Reason(x, E, P) then Answer(x, E, P) break end if  $d \leftarrow d + 1$  end while if  $d = D$  then Answer(x, E, P) end if
+
+# F.2 LLM-KG Prompts
+
+# ExtractEntities
+
+system tvam knowledge-graph-tah uttarani niskarsytum prasnat entities vindasi ca tani saha relevance-score (0-1 madhye) samarpayasi.
+
+output udāharanam (‘ramah’, 0.8), (‘sīṭa’, 0.7). tato vivrtam mā kuru.
+
+human prasnah: {QUESTION} {CHOICES}
+
+# RelationPrune
+
+system tvam datta-prasnaya uttarani knowledge-graph-tah niskarshitum knowledge-graph-tah idanim paryantam niskarṣita-sambandhebhayā avasyāni saha relevance-score (0-1 madhye) samarpayasi. output udāharanam ('IS_FATHER_OF', 0.8), ('IS_CROSSED_BY', 0.7), ..., tato vivrtam mā kuru.
+
+human prasnah: {QUESTION} {CHOICES}
+
+niskarshitani sambandhani: {RELATIONS}
+
+# EntityExtractPrune
+
+system tvam datta-prasnasya uttarani knowledge-graph-tah niskarsitum knowledge-graph-tah idanim paryantam niskarsita-sambandhebhyah avasyani nodes (lemmas) saha relevance-score (0-1 madhye) samarpayasi.
+
+output udāharanam (‘ramah’, 0.8), (‘sīṭa’, 0.7). tato vivrtam mā kuru.
+
+human prasnah: {QUESTION} {CHOICES}
+
+niskarṣitāni sambandhāni: {RELATIONS, ENTITIES}
+
+# Reason
+
+system tvam dati-prasnasya uttarani knowledge-graph-tah niskarsitum knowledge-graph-tah idanim paryantam niskarsitamyat-kincid prasnasya uttaram datum alam (1) va nalam (0) iti vaktavyam.
+
+output 1 athava 0. na anyat vadasi
+
+human prasnah: {QUESTION} {CHOICES}
+
+niskarṣitam: {PATHS}
+
+<table><tr><td>Method</td><td>gpt-4o</td><td>claude-3.5-sonnet</td><td>gemini-1.5-pro</td><td>mistral-large-2</td><td>llama-3.1-405b-instruct</td></tr><tr><td>Closed-book</td><td>0.381</td><td>0.242</td><td>0.148</td><td>0.333</td><td>0.346</td></tr><tr><td>RAG-BM25</td><td>0.478</td><td>0.521</td><td>0.459</td><td>0.434</td><td>0.323</td></tr><tr><td>LLM-KG</td><td>0.381</td><td>0.254</td><td>-</td><td>0.341</td><td>-</td></tr></table>
+
+Table 7: Exact Match (Scores) of various models (including those not part of main experiments) in Sanskrit Question-Answering task (Sanskrit Prompts) with LLM-KG paradigm compared against zero-shot and RAG-BM25 paradigms.  
+
+<table><tr><td>Method</td><td>gpt-4o</td><td>claude-3.5-sonnet</td><td>mistral-large-2</td></tr><tr><td>closed-book</td><td>0.32</td><td>0.21</td><td>0.25</td></tr><tr><td>LLM-KG</td><td>0.34</td><td>0.34</td><td>0.35</td></tr></table>
+
+(a)  
+
+<table><tr><td>Method</td><td>gpt-4o</td><td>claude-3.5-sonnet</td><td>mistral-large-2</td></tr><tr><td>closed-book</td><td>0.40</td><td>0.25</td><td>0.36</td></tr><tr><td>LLM-KG</td><td>0.39</td><td>0.23</td><td>0.34</td></tr></table>
+
+(b)
+
+Table 8: Comparison of Exact Match (EM) scores between closed-book and LLM-KG paradigms for selected questions when the answer (a) can likely be inferred from KG and (b) cannot be inferred from KG.
+
+# Answer
+
+system adhah {TOPIC}-sambandhe prṣṭa-prasnasya pratyuttaram dehi. tadapi prasnocitavibhaktau bhavet na tu pratipadika rupe. tadapi ekenaiva padena yadi uttare kāranam napeksitam. katham kimartham ityādisu ekena laghu vakyena uttaram dehi atra tu eka-pada-niyamah nasti.
+
+api ca yatha'vasyam adhah dattaih knowledge-graph-tah niskarsita-visayaih sahayyam grha. tattu sarvada sadhu iti na'sti pratiih. uttaram yavad laghu sakyam tavat laghu bhavet.
+
+human prasnah: {QUESTION} {CHOICES}
+
+niskarsitam: {PATHS}
+
+uttaram:
+
+# F.3 LLM-KG Results
+
+The LLM-KG paradigm was evaluated exclusively using Sanskrit prompts on the two QA datasets and included additional models not part of the main experiments—namely, claude-3.5-sonnet (AnthropicAI, 2024), gemini-1.5-pro (Google, 2024), and mistral-large-2 (MistralAI, 2024). Table 7 presents the results in comparison with the closed-book and RAG-BM25 paradigms. Overall, performance gains from closed-book to LLM-KG are modest and fall short of the improvements observed with RAG. This may be partly attributed to the complexity of the LLM-KG setup, which requires multi-step prompting and adherence to a structured output format. Notably, models like gemini-1.5-pro and llama-3.1 frequently fail to follow this structured format, rendering them ineffective for running ToG. The strict formatting requirements may also pose challenges for other models, particularly those less adapted to Sanskrit. Interestingly, while claude-3.5-sonnet achieves the best results with RAG-BM25, it lags behind gpt-4o and mistral-large-2 in both the closed-book and LLM-KG paradigms.
+
+Table 8 presents a breakdown of performance based on whether the question topics are covered in the current KG—specifically, the kingdoms category (27 questions) in the Ramayana dataset and the annotated chapters (299 questions) in Bhavaprakāśanighaṇṭu. For these subsets, which are likely answerable from the KG, LLM-KG shows clear improvements over the closed-book setting, indicating that access to a near-complete KG can significantly enhance performance. In contrast, for questions outside these categories or chapters, no such improvement is observed, reinforcing the hypothesis that KG completeness is crucial for the effectiveness of LLM-KG. Determining domains where knowledge graphs may outperform or be more appropriate than RAG remains an open question for future research.
+
+# G Categories for Named Entity Recognition
+
+The categories for NER in Sanskrit, Ancient Greek, and Latin, along with their rough translation and brief explanations, wherever applicable, are provided here.
+
+<table><tr><td>Entity Type</td><td>Translation</td><td>Description</td></tr><tr><td>Manusya</td><td>Human</td><td>A mortal human being</td></tr><tr><td>Deva</td><td>Deity</td><td>Divine celestial being; god or goddess</td></tr><tr><td>Gandharva</td><td>~</td><td>Heavenly musician in the service of the gods</td></tr><tr><td>Apsaras</td><td>~</td><td>Beautiful female spirits known for dance and charm</td></tr><tr><td>Yaksa</td><td>~</td><td>Guardian spirit of natural treasures.</td></tr><tr><td>Kinnara</td><td>~</td><td>Certain Semi-divine beings</td></tr><tr><td>Räksasaa</td><td>~</td><td>Malevolent being</td></tr><tr><td>Asura</td><td>Anti-god</td><td>Powerful beings opposed to the gods</td></tr><tr><td>Vānara</td><td>Monkey-being</td><td>Monkey-like humanoid</td></tr><tr><td>Bhallūka</td><td>Bear-being</td><td>Bear or Bear-like humanoid</td></tr><tr><td>Grdhra</td><td>Vulture-being</td><td>Vulture-like being</td></tr><tr><td>Rksa</td><td>Bear-being</td><td>Bear-like humanoid</td></tr><tr><td>Garuda</td><td>Eagle-being</td><td>Eagle-like being</td></tr><tr><td>Nāga</td><td>Serpent-being</td><td>Semi-divine serpent race</td></tr><tr><td>Svaraga</td><td>Heaven</td><td>Abode of the gods</td></tr><tr><td>Naraka</td><td>Hell</td><td>Realm of punishment after death</td></tr><tr><td>Nadi</td><td>River</td><td>Flowing body of freshwater</td></tr><tr><td>Sāgara</td><td>Sea</td><td>Vast saltwater body</td></tr><tr><td>Sarovara</td><td>Lake</td><td>Large inland water body</td></tr><tr><td>Kūpa</td><td>Well</td><td>Man-made water source</td></tr><tr><td>Tira</td><td>Riverbank</td><td>Edge or shore of a river</td></tr><tr><td>Dvīpa</td><td>Island</td><td>Land surrounded by water</td></tr><tr><td>Parvata</td><td>Mountain</td><td>Large natural elevation of earth</td></tr><tr><td>Nagara</td><td>City</td><td>Urban settlement or metropolis</td></tr><tr><td>Tirtha</td><td>Sacred Place</td><td>Holy pilgrimage spot, often near water</td></tr><tr><td>Grāma</td><td>Village</td><td>Small rural settlement</td></tr><tr><td>Rājya</td><td>Kingdom</td><td>Territory ruled by a king</td></tr><tr><td>Vana</td><td>Forest</td><td>Dense growth of trees; wilderness</td></tr><tr><td>Udyāna</td><td>Garden</td><td>Cultivated green space</td></tr><tr><td>Marubhūmi</td><td>Desert</td><td>Dry, arid region</td></tr><tr><td>Prāsāda</td><td>Palace</td><td>Royal residence</td></tr><tr><td>Mandira</td><td>Temple</td><td>Sacred structure for worship</td></tr><tr><td>Aśrama</td><td>Hermitage</td><td>Secluded place for spiritual practice</td></tr><tr><td>Grha</td><td>House</td><td>Dwelling or home</td></tr><tr><td>Kutira</td><td>Hut</td><td>Small and simple shelter</td></tr><tr><td>Guhā</td><td>Cave</td><td>Natural underground chamber</td></tr><tr><td>Mārga</td><td>Road</td><td>Pathway or route</td></tr><tr><td>Ratha</td><td>Chariot</td><td>Two- or four-wheeled ancient vehicle</td></tr><tr><td>Vimāna</td><td>Airborne Vehicle</td><td>Flying chariot or aircraft</td></tr><tr><td>Khadga</td><td>Sword</td><td>Bladed weapon</td></tr><tr><td>Dhanus</td><td>Bow</td><td>Weapon for shooting arrows</td></tr><tr><td>Bāna</td><td>Arrow</td><td>Projectile shot from a bow</td></tr><tr><td>Cakra</td><td>Discus</td><td>Spinning circular weapon</td></tr><tr><td>Gadā</td><td>Mace</td><td>Blunt weapon, often spiked</td></tr><tr><td>Tomara</td><td>Javelin</td><td>Thrown spear or missile</td></tr><tr><td>Śula</td><td>Spear</td><td>Long-shafted piercing weapon</td></tr><tr><td>Kavaca</td><td>Shield</td><td>Defensive armor piece</td></tr><tr><td>Kañcuka</td><td>Armor</td><td>Protective body gear</td></tr><tr><td>Paraśu</td><td>Axe</td><td>Bladed tool/weapon</td></tr><tr><td>Astra</td><td>Divine Weapon</td><td>Supernatural weapon, often invoked</td></tr><tr><td>Abharana</td><td>Ornament</td><td>Decorative jewelry</td></tr><tr><td>Sānkha</td><td>Conch</td><td>Sacred spiral shell</td></tr><tr><td>Vādya</td><td>Musical Instrument</td><td>Instrument used in music</td></tr><tr><td>Nāna</td><td>Currency</td><td>Form of money or coin</td></tr><tr><td>Kula</td><td>Clan</td><td>Extended family or lineage</td></tr><tr><td>Jāti</td><td>Species</td><td>Species/Socio-economical Group</td></tr><tr><td>Gana</td><td>Tribe / Group</td><td>Assembly or community</td></tr><tr><td>Rtu</td><td>Season</td><td>Climatic period of the year</td></tr><tr><td>Samvatsara</td><td>Year</td><td>Vedic year cycle</td></tr><tr><td>Māsa</td><td>Month</td><td>Lunar or solar month</td></tr><tr><td>Tithi</td><td>Lunar Day</td><td>Phase in the moon&#x27;s waxing/waning</td></tr><tr><td>Paksa</td><td>Fortnight</td><td>Half of a lunar month</td></tr><tr><td>Ayana</td><td>Solstice Cycle</td><td>Six-month movement of the sun</td></tr><tr><td>Yuga</td><td>Epoch</td><td>Cosmic age or era</td></tr><tr><td>Yoga</td><td>Astronomical Combination</td><td>Planetary conjunction</td></tr><tr><td>Karana</td><td>Half of Tithi</td><td>Subdivision of a lunar day</td></tr><tr><td>Muhūrta</td><td>Moment / Auspicious Time</td><td>Small unit of time (about 48 minutes)</td></tr><tr><td>Lagna</td><td>Ascendant</td><td>Zodiac rising at time of birth</td></tr><tr><td>Graha</td><td>Planet</td><td>Celestial influencer</td></tr><tr><td>Nakṣatra</td><td>Lunar Mansion</td><td>One of 27 lunar constellations</td></tr><tr><td>Rāsi</td><td>Zodiac Sign</td><td>Segment of the zodiac</td></tr><tr><td>Dhuma-ketu</td><td>Comet</td><td>Celestial object with a tail</td></tr><tr><td>Utsava</td><td>Festival</td><td>Celebratory event</td></tr><tr><td>Pūjā</td><td>Worship</td><td>Ritual offering and prayer</td></tr><tr><td>Yajna</td><td>Vedic Sacrifice</td><td>Sacred fire ritual</td></tr><tr><td>Upacāra</td><td>Ritual Offering</td><td>Ceremonial gesture or item</td></tr><tr><td>Samskāra</td><td>Life-Cycle Rite</td><td>Hindu ritual of life transition</td></tr><tr><td>Aniscita</td><td>Undecided</td><td>Something that is not yet determined</td></tr><tr><td>Vṛkṣa</td><td>Tree</td><td>Large woody plant</td></tr><tr><td>Guccha</td><td>Shrub</td><td>Small bushy plant</td></tr><tr><td>Lata</td><td>Vine</td><td>Climbing or trailing plant</td></tr><tr><td>Puspa</td><td>Flower</td><td>Blossom of a plant</td></tr><tr><td>Phala</td><td>Fruit</td><td>Edible plant product</td></tr><tr><td>Patra</td><td>Leaf</td><td>Green foliage part</td></tr><tr><td>Stambha</td><td>Stem</td><td>Main structural plant part</td></tr><tr><td>Tvak</td><td>Bark</td><td>Outer layer of tree</td></tr><tr><td>Mūla</td><td>Root</td><td>Underground part of plant</td></tr><tr><td>Paksiī</td><td>Bird</td><td>Feathered flying animal</td></tr><tr><td>Sarpa</td><td>Snake</td><td>Legless reptile</td></tr></table>
+
+<table><tr><td>Entity Type</td><td>Description</td></tr><tr><td>NORP</td><td>Ethnic groups, demonyms, schools</td></tr><tr><td>ORG</td><td>Organizations</td></tr><tr><td>GOD</td><td>Supernatural beings</td></tr><tr><td>LANGUAGE</td><td>Languages and dialects</td></tr><tr><td>LOC</td><td>Cities, empires, rivers, mountains, and so forth.</td></tr><tr><td>PERSON</td><td>Individual persons</td></tr></table>
+
+Table 10: Entity types occurring in Ancient Greek NER (Myerston, 2025). The types without descriptions—EVENT and WORK—have very few occurrences in the dataset.  
+
+<table><tr><td>Entity Type</td><td>Description</td></tr><tr><td>PER</td><td>Person</td></tr><tr><td>LOC</td><td>Locations, places</td></tr><tr><td>GRP</td><td>Other groups such as tribes</td></tr></table>
+
+Table 11: Entity types occurring in Latin NER are quite standard types.
+
+Table 9: Entity types occurring in Sanskrit NER

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2025/A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts/full.md

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+# A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts
+
+Iglika Nikolova-Stoupak†,‡ Maxime Amblard†
+
+Sophie Robert-Hayek† Frédérique Rey‡
+
+†LORIA, UMR 7503, Université de Lorraine, CNRS, Inria, 54000 Nancy, France
+
+$^{\ddagger}$ Research Centre Écritures, EA 3943, Université de Lorraine, 57000 Metz, France
+
+{firstname.surname}  $@$  univ-lorraine.fr
+
+# Abstract
+
+The current project is inscribed within the field of stemmatology or the study and/or reconstruction of textual transmission based on the relationship between the available witnesses of given texts. In particular, the variants (differences) at the word-level in manuscripts written in Biblical Hebrew are addressed. A dataset based on the Book of Ben Sira is manually annotated for the following variant categories: 'plus/minus', 'inversion', 'morphological', 'lexical' or 'unclassifiable'. A strong classifier (F1 value of 0.80) is then trained to predict these categories in collated (aligned) pairs of witnesses. The classifier is non-neural and makes use of the two words themselves as well as part-of-speech (POS) tags, hand-crafted rules per category, and additional synthetically derived data. Other models experimented with include neural ones based on the state-of-the-art model for Modern Hebrew, DictaBERT. Other features whose relevance is tested are different types of morphological information pertaining to the word pairs and the Levenshtein distance between the words within a pair. The strongest classifier as well as the used data are made publicly available. Coincidentally, the correlation between two sets of morphological labels is investigated: professionally established as per the Qumran-Digital online library and automatically derived with the sub-model DictaBERT-morph.
+
+# 1 Introduction
+
+Stemmatology, situated within the field of textual criticism, studies the genealogy of texts (Roelli, 2020). Within its framework, textual witnesses (i.e. extant versions of the same text) are aligned in a process known as 'collation' and compared to one another. In particular, it is assumed that variant differences (sometimes referred to as 'errors') associated with discrete witnesses give out important information about their relationship. If the same error is shared by two witnesses, and it is unlikely to
+
+have been made independently by the two scribes, then one of the witnesses is assumed to have been derived from the other. Stemmatology traditionally concerns academic disciplines such as classical philology and Biblical studies. It is associated with a number of 'schools', notably the 'German' one (represented by Karl Lachmann), which focuses mainly on intertextual connections, and the 'French' one (represented by Joseph Bédier), which also accords importance to a text's historical and cultural framework. More recently, the so-called 'new philology' proposes to move from the genealogical model to a study of each textual witness and its specific context (Cerquiglini, 1983; Jansen, 1990).
+
+Multidisciplinarity is crucial to the practice and reliability of stemmatology, especially in the current digital era. Computing solutions have been used within the field since as early as the 1950s, due to its clear algorithmic nature (Heikkilä, 2023). Indeed, automation can be successfully applied to a number of aspects of the discipline, such as collation, statistics related to textual variants and even the ultimate construction of genealogical trees of texts. However, expert knowledge pertaining to the concerned academic disciplines and optimal communication within collaborating teams are crucial. This project is produced by a team specialising in diverse fields such as natural language processing (NLP), stemmatology, theology and Hebrew studies. The associated work seeks to establish exemplar practices in the application of contemporary NLP techniques to the classification of ancient manuscripts. Specifically, texts in Biblical Hebrew (and in particular, the Dead Sea Scrolls, whose age is estimated as 3rd century BCE - 1st century CE) are approached. Following elaborate manual annotation, classifiers of the variants between textual witnesses are trained. The strongest classifier is a non-neural (Random Forests) one that utilises the annotated data as well as part-of-speech (POS) tags, a limited amount of synthetic data and several hand-
+
+crafted rules that increase the probability of specific categories being predicted.
+
+The long-term objective of our project is to establish a system that helps reconstruct the genealogical link between discrete manuscripts. An important step therein is to fully consider the discrepancies between them. At an atomic level, the differences between word pairs (omission/addition of a letter, replacement with a synonym, etc.) need to be not only counted but also categorised, as they may imply different levels and types of inter-textual relations. The present work focuses on this initial step, providing a classifier that achieves an F1 score of 0.80 while making use of original and synthetic data and taking into consideration the specificity of the Hebrew language.
+
+The dataset of professionally annotated variants, the derived synthetic datasets and the strongest achieved classifier model are made available at: https://gitlab.inria.fr/semagramme/sherbet/
+
+# 2 Background
+
+The following discussion will consider the linguistic features of the Hebrew language as well as existing relevant NLP tools.
+
+# 2.1 Varieties of Hebrew
+
+Hebrew is a Northwest Semitic language which is read from left to right and makes use of an abjad writing system; that is to say, only consonants are typically represented. Diacritical signs (nikkud) may be added in order to denote vowel sounds and thus facilitate reading. The language is commonly described as morpho-synctactic (Khan et al., 2013). The general meaning of a word is carried by its typically three-letter root. Prepositions and conjunctions are prefixed and possessive pronouns are suffixed to the word they modify.
+
+A common dichotomy exists between Modern and Classical (Ancient) Hebrew i.e. the language spoken in Israel today versus the language of the Hebrew Bible. Whilst morphology is the least altered aspect of the language (Taylor, 2019), its lexicon has been significantly enriched so as to include modern terms and concepts. Modern Hebrew makes use of a number of words whose roots can be traced to Biblical Hebrew but whose meaning has been adapted. For instance, the word 'ה' is a hapax legomenon found in the Book of Job, which describes the flight of an eagle; today, it means 'to fly on an airplane' (Pritz, 2016). Other notable lin
+
+guistic developments include the loss or decline of some verb forms and tenses, such as the 'consecutive tenses', the lengthened imperative and the jussive; the substitution of the conjunction  $\neg \psi \aleph$  with  $\psi$ ; and the no longer compulsory question particle  $\overline{\eta}$  (Khan et al., 2013). The majority of these developments have in fact been gradual and are traceable throughout the multiple defined sub-periods associated with the language, such as Archaic Hebrew, Classical Hebrew, Late Biblical Hebrew, Rabbinical Hebrew and Medieval Hebrew (Khan et al., 2013; Pérez Fernández and Elwolde, 1999; Schniedewind, 2013). A major development that can be traced to a specific historical point is the inclusion of diacritical signs in the writing system in the Masoretic era (7th-10th centuries CE). Conversely, unlike an older text such as one from the Dead Sea scrolls, a text from this period is likely to not include the characters  $\aleph$ ,  $\aleph$ , and  $\aleph$  for vocalisation purposes.
+
+Within the context of NLP, Biblical Hebrew may be viewed as representative of a specific genre, register or domain. It is also important to note that, due to the Hebrew Bible's limited size, Biblical Hebrew contains solely about 9000 distinct words, 1500 of which are hapax legomena (Sáenz-Badillos, 1993).
+
+# 2.2 LLMs/NLP and Hebrew
+
+Several Large Language Models (LLMs) that focus on the Hebrew language have been proposed up to date. BERT's multilingual version, mBERT, features about 2000 Hebrew tokens (Devlin et al., 2019), and more recent Hebrew-specific models often use it as a baseline when evaluating their performance. In particular, there are several BERT-based Hebrew models, whose abilities in relation to the language's morphology have been specifically emphasised. HeBERT (Chriqui and Yahav, 2021) is trained on the Wikipedia and OSCAR datasets and released along with the sentiment analysis tool HebEMO. Its performance is noted to improve when sub-word rather than word-based tokenisation is performed. AlephBERTGimmel improves on an earlier model, AlephBERT (trained on Wikipedia, Twitter and OSCAR), in a variety of NLP tasks, including morphological segmentation and POS tagging, by simply increasing its vocabulary size from 50k to 128k tokens (Gueta et al., 2023).
+
+The DictaBERT model (Shmidman et al., 2023) occupies the current state-of-the-art in a number
+
+of tasks, including morphology-related ones and sentiment analysis. It is trained on 3B words, and its authors note that the masking of only whole words rather than word segments has improved its performance significantly. DictaBERT is released along with two sub-models, DictaBERT-morph and DictaBERT-seg, which specialise in the respective tasks of morphological annotation and the segmentation of particles such as prepositions and articles from words. For the purpose of this project, it is also worth mentioning BEREL, an additional model proposed by DictaBERT's research team, which is trained on Rabbinic rather than Modern Hebrew text (as found in the  $Sefaria^2$  and  $Dicta^3$ ) online libraries. At the time of writing, the BEREL model is only available as a demo version.
+
+Other notable Hebrew-related NLP tools include a challenge set, devised and tested by DictaBERT's authors, which includes 56k professionally annotated sentences composed around 12 pairs of homographs, a frequent phenomenon within the Hebrew language that interferes with the performance of automatic analysis (Shmidman et al., 2020).
+
+# 3 Methods
+
+# 3.1 Manual annotation
+
+Within the framework of this study, manual annotation is applied to the extant manuscripts of the Book of Ben Sira, a poetically written text dating from the 2nd century BCE that features guidance concerning Jewish life and worship. The choice of text is based on several factors. First comes its presence among the Dead Sea Scrolls, which constitute the implied project's framework due to their large number and relatively recent discovery. To go further, the Book of Ben Sira has received attention not only in established but now partly outdated studies (Beentjes, 1997; Ben-Hayyim, 1973), but also in recent academic work that matches the standards of the modern digital era, notably Rey and Reymond (2024). It is also worth noting that the text has a high number of extant witnesses<sup>4</sup> and that its complex nature in terms of vocabulary, syntax and use of figurative language render its study generalisable to a large array of other Biblical Hebrew texts.
+
+Annotation is performed by professionals in the field of Biblical and Jewish Studies. Word-level
+
+annotation is initially opted for and hypothesised to be of significant importance due to the Hebrew language's especially strong morphology. The utilised texts are manually collated into word-pair variants, and the variants are assigned a defined category. Two of the categories also contain subcategories, which are indicated if the word pair can be identified with them unambiguously. Currently, differentiation between the subcategories is not used in the automatic classification process. However, the subcategories' definitions and proportions are made use of in the derivation of synthetic data. Please see Table 1 for English examples of the least intuitive categories. Appendix A provides detailed information about the meaning of each category and subcategory.
+
+Formating conventions as outlined in École Biblique et Archéologique Française de Jérusalem (1955-1982), such as superscript dots over a letter or different types of brackets, are retained to denote degrees of uncertainty about a text's interpretation.
+
+Table 1: English examples of the 'Morphological' and 'Lexical' variant categories.  
+
+<table><tr><td colspan="2">Morphological</td><td colspan="2">Lexical</td></tr><tr><td>var1</td><td>var2</td><td>var1</td><td>var2</td></tr><tr><td>cat</td><td>the cat</td><td>cat</td><td>car</td></tr><tr><td>cat</td><td>and cat</td><td>cat</td><td>Kate</td></tr><tr><td>cat</td><td>my cat</td><td>cat</td><td>qat</td></tr><tr><td>cat</td><td>cats</td><td>cat</td><td>kitten</td></tr><tr><td></td><td></td><td>cat</td><td>dog</td></tr></table>
+
+Several subcategories may be indicated for a given word pair; for example, the variants (hakol; 'the' + 'everything') and (lekol; 'to' + 'everything') belong to the category 'morphological' and the subcategories 'determination' and 'preposition'. In contrast, as the same pair may not be indicated as belonging to more than one category in the process of automatic classification, the category deemed most representative is opted for.
+
+Table 2 shows the distribution of annotated data per category and, where relevant, subcategory.
+
+# 3.2 Synthetic data
+
+Due to the described manually annotated data's limited size, data augmentation was undertaken in the face of generation of synthetic data. The issuing synthetic data is based on random words taken from the Dead Sea Scrolls and alternative witnesses of the present texts, as provided, annotated and aligned within the the Qumran-Digital library
+
+<table><tr><td>Category</td><td>Count</td></tr><tr><td>Same</td><td>1735</td></tr><tr><td>Unclassifiable</td><td>659</td></tr><tr><td>Lexical</td><td>476</td></tr><tr><td>Synonyma</td><td>104</td></tr><tr><td>Metathesis</td><td>16</td></tr><tr><td>Phonetic affinity</td><td>13</td></tr><tr><td>Antonym</td><td>9</td></tr><tr><td>Letter interchange</td><td>6</td></tr><tr><td>Misspelling</td><td>1</td></tr><tr><td>Morphological</td><td>430</td></tr><tr><td>Orthographical</td><td>145</td></tr><tr><td>Grammatical</td><td>116</td></tr><tr><td>Coordination</td><td>44</td></tr><tr><td>Suffix pronoun</td><td>44</td></tr><tr><td>Preposition</td><td>36</td></tr><tr><td>Singular/Plural</td><td>14</td></tr><tr><td>Determination</td><td>11</td></tr><tr><td>Masculine/Feminine</td><td>3</td></tr><tr><td>Plus/Minus</td><td>430</td></tr><tr><td>Inversion</td><td>28</td></tr><tr><td>Total</td><td>3758</td></tr></table>
+
+a Note that not all entries within a category that contains subcategories are assigned a subcategory.
+
+Table 2: Distribution of annotated data by category and subcategory (number of word pairs)
+
+of the Göttingen Academy of Sciences and Humanities (Akademie der Wissenschaften zu Göttingen, 2021) $^{5}$ . All text was cleaned of reconstruction signs and tokenised into words, and all words were shuffled. The randomised sample consisted of just over  $70\mathrm{k}$  words. Words were deleted from the sample upon use.
+
+Please refer to Appendix B for a description of the pipelines for data generation, which are elaborated based on each category and subcategory's definition as well as on observations derived from the annotated data, such as proportions of POS tags and average Levenshtein distances within a category. Imitation of more detailed characteristics, such as the distribution of Levenshtein distances, was opted against as robustness of the classifier models was sought. The majority of the data was de
+
+rived through the application of hand-crafted rules on words from the described randomised dataset. Occasionally, external sources, such the Hebrew dictionary  $\text{Milog}^6$  were also made use of. Finally, in the cases of the 'masculine/feminine' and 'sufixed pronoun' subcategories, a portion of the used word pairs were hard-coded.
+
+Three synthetic datasets were composed, which differ by the number and proportion of entries by category and subcategory. 'Synthetic dataset 1' is of the same size as the annotated dataset and contains the same number of entries per category. Balance is sought for subcategories, even where the original data is highly unbalanced. 'Synthetic dataset 2' is such that when it is concatenated to the annotated dataset, 1000 entries per category are achieved. Once again, balance is sought for subcategories. The general logic of 'synthetic dataset 2' is followed for the composition of 'synthetic dataset 3', which, however, includes a significantly larger number of entries. When this dataset is concatenated to the annotated one, 10k entries per category are achieved. The original proportions of entries per subcategory are maintained. The smallest number of data points, associated with the 'misspelling' subcategory, comes at 64, whilst the annotated data features only a single entry of this subcategory.
+
+# 3.3 Morphological labels
+
+Two sets of morphological labels are associated to each word from the annotated pairs for use within classifier experiments: the professionally attributed labels present in the Qumran-Digital library and retrieved with the help of an API developed for the purpose by our team (henceforth, the 'gold standard') and labels assigned automatically with the DictaBERT-morph model (henceforth, the 'silver standard'). The gold standard labels are originally composed in German and feature the following information: 'lemma', 'word class', 'short definition', 'root designation', 'verb stem', 'verb tense', 'person', 'gender', 'number', 'state', 'augment', 'suffix person' and 'suffix number'. The information present in the silver standard labels consists of each word's POS, gender, number, person, tense, prefixes and suffix. The gold standard labels include a significantly higher number of categories, some
+
+of which are particularly conceived with Classical Hebrew in mind (e.g. 'state', 'augment') and are naturally absent from the DictaBERT model, which is based on Modern Hebrew. Similarly, some of the gold standard labels within comparable categories are perceptibly more domain-specific (e.g. word class 'name of a god', consecutive tenses). The sole occasion of higher specificity associated with the silver standard annotation is that coordinating and subordinating conjunctions form separate categories.
+
+As gold standard labels are based on a limited number of professionally annotated texts, they are not derivable for a large portion of the synthetic data (and for potential future text that our variant classification may be applied to). Silver standard labels are therefore resorted to in relevant experimentation. In order to evaluate the latter's quality, we explored the derived labels<sup>9</sup> of readily mappable categories across the two standards, calculating the silver labels' accuracy with respect to the gold ones. The mapped categories were: the gold standard's 'word class' and the silver standard's 'POS'; and the two standards' 'person', 'gender' and 'number'. Please refer to Appendix E for the full mapping applied to POS tags. The 'dual' number, not present among the silver labels, was mapped to 'plural'. In turn, the '1,2,3' silver tag for 'person' was considered to always be correct. As the gold standard labels are based on a word's original context and can therefore have different values at different occurrences of the word, all possible values for a word were retrieved, and their frequencies were noted. The silver labels' accuracy was calculated in two discrete settings: a match against the most common gold label versus a match against any of the possible gold labels.
+
+Please see Table 3 for the results of the performed evaluation. As expected due to the high number of categories, accuracy was by far lowest for POS tags. The most common mistakes consisted in auxiliaries or verbs being marked as nouns or proper nouns; and nouns being marked as proper nouns. Accuracy was very high (over 0.9) for 'number' and 'gender' in the second scenario. The most common mistakes for 'number' labels were 'plural' being marked as 'singular'; for 'gender' - 'masculine' being marked as 'feminine'; and for 'person' - '2nd' being marked
+
+<table><tr><td></td><td>Ac
+(1)</td><td>Ac
+(2)</td><td># 
+g + s</td><td># 
+g</td><td># 
+s</td></tr><tr><td>POS</td><td>0.25</td><td>0.55</td><td>3079</td><td>3099</td><td>3413</td></tr><tr><td>Num</td><td>0.58</td><td>0.96</td><td>1826</td><td>2710</td><td>2803</td></tr><tr><td>Gen</td><td>0.85</td><td>0.92</td><td>1818</td><td>2706</td><td>2256</td></tr><tr><td>Per</td><td>0.75</td><td>0.84</td><td>396</td><td>857</td><td>557</td></tr></table>
+
+Table 3: The accuracy of silver standard labels as compared to gold standard labels in two scenarios: a match with the first label in terms of frequency (1); and a match with any of the possible labels (2). The number of words annotated with labels is also included. Only the words with both types of labels were evaluated. Num: number; Gen: Gender; Per: Person.; g: gold; s: silver
+
+as '3rd'.
+
+# 3.4 Classifiers
+
+A variety of multiclass classifier models are experimented with until maximal performance in terms of F1 value $^{10}$  is reached in the task of prediction of word-level variation in Biblical Hebrew text: these include Logistic Regression, Random Forests and Support Vector Machines (SVM) $^{11}$ , as well as neural models based on DictaBERT as the current state-of-the-art in the Hebrew language. For the non-neural models, multiple parameters are explored in the context of a grid search, notably including different tokenisation methods. $^{12}$  The neural classifiers make use of the Python library transformers. $^{13}$  They are trained for 7 epochs $^{14}$ , with 3 random seeds, and different train and evaluation batch sizes are tested. The experiments utilised nodes equipped with Intel Xeon Gold 5220 CPUs (2.20GHz, 18 cores) and 96 GiB of RAM, alongside two NVIDIA GeForce GTX 1080 Ti graphics cards.
+
+The manually annotated dataset described in Section 3.1 is used in the training process, and synthetic datasets 1 to 3 (see Section 3.2) are added to it in both the neural and non-neural models, whilst evaluation is only performed on annotated data. In addition to the concatenated embeddings of the
+
+two words that comprise each pair, Levenshtein distance $^{15}$  and morphological characteristics (as per both the gold and silver standards elaborated in Section 3.3 for annotated data and the silver standard when synthetic data is included) are experimented with as features within the non-neural models. POS tags and other morphological information such as phi-features $^{16}$  have proven to be beneficial for morphologically rich languages in sentence-level tasks such as syntactic parsing (Marton et al., 2010; Collins et al., 1999; Tsarfaty and Sima'an, 2007), but to our knowledge no similar evaluation has been carried out at the word level. In contrast, research shows that neural models such as BERT do not benefit from explicit morphological labels, except in rare situations where the labels are of especially high quality $^{17}$  (Klemen et al., 2023). Finally, different hand-crafted rules per category are defined and set to increase the probability of the respective categories being predicted at inference. $^{18}$  These rules pertain to the categories' definitions and statistical observations based on the annotated data. A positive boolean value is attributed to the feature 'likely inversion' if 'word 1' and 'word 2' can be encountered reversed within four indices of the current index and the two words have Levenshtein distance of at least 2. 'Likely plus/minus' is marked positively when one of the words is empty, and 'likely unclassifiable' - when at least one of the words contains at least one square bracket. 'Likely morphological' is attributed when both words are present, do not contain square brackets and have a Levenshtein distance smaller than 2. A rule related to the lexical category is not developed due to the category's highest complexity as well as the fact that it is the sole remaining category.
+
+Please refer to Figure 1 for an overview of the trained classifiers' input and output.
+
+# 4 Results
+
+Table 4 summarises the nature and performance of the key classifier models experimented with. For more detailed experimentation results, please refer to Appendix F. The strongest non-neural baseline
+
+![](images/0e1bd5414426b1dce28707d0d36fb16aa87619c5c9de180b1cbb73c27686bf9a.jpg)  
+Figure 1: Classifier Input and Output
+
+models were Random Forests $^{19}$ . The optimal tokenisation technique was revealed to be TidfVectorizer with character bigrams. Both gold and silver standard morphological labels were experimented with. In the case of the former, a setting where only the most frequent label per category was used proved to be the better choice. The highest performing non-neural model (which also performed best overall) had an F1 score of 0.80. It was trained on the annotated and 'synthetic 1' data and included silver standard POS tags and Levenshtein distance as features. The 'Unclassifiable' and 'Plus/Minus' categories presented a challenge, likely due to the fact that the former can be characterised with a missing variant and does not always include reconstruction signs at the word-level $^{20}$ . Very closely at second place came the model trained solely on annotated data and silver standard POS tags. The best non-neural models trained with the help of 'synthetic 2' and 'synthetic 3' data reached, respectively, F1 of 0.78 and 0.72. Curiously, silver standard morphological information led to consistently better performance than gold standard.
+
+The DictaBERT-based neural classifiers achieved competitive but slightly lower results, with the exception of the classifier trained with the help of 'synthetic 3' data, whose performance was higher (0.74, against 0.72 for the non-neural models). The best F1 score was achieved by the model trained solely on annotated data (0.78), followed by the ones making use of 'synthetic 2' (0.77), 'synthetic 1' (0.76) and finally 'synthetic 3'. The neural models took a significant amount of time to train: between 6 min ('annotated') and  $2\mathrm{h}45\mathrm{min}$  ('annotated' plus
+
+<table><tr><td>Model</td><td>F1</td><td>Ac</td><td>Pr</td><td>Re</td></tr><tr><td>BaseAn</td><td>0.67</td><td>0.67</td><td>0.67</td><td>0.67</td></tr><tr><td>BaseAn+S1</td><td>0.68</td><td>0.68</td><td>0.69</td><td>0.68</td></tr><tr><td>BaseAn+S2</td><td>0.67</td><td>0.65</td><td>0.70</td><td>0.75</td></tr><tr><td>BaseAn+S3</td><td>0.66</td><td>0.66</td><td>0.74</td><td>0.61</td></tr><tr><td>Mod1An</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Mod2An</td><td>0.74</td><td>0.75</td><td>0.75</td><td>0.74</td></tr><tr><td>Mod2An+S1</td><td>0.72</td><td>0.72</td><td>0.73</td><td>0.72</td></tr><tr><td>Mod2An+S2</td><td>0.68</td><td>0.67</td><td>0.70</td><td>0.67</td></tr><tr><td>Mod2An+S3</td><td>0.69</td><td>0.69</td><td>0.80</td><td>0.69</td></tr><tr><td>Mod2+LAn</td><td>0.72</td><td>0.73</td><td>0.74</td><td>0.73</td></tr><tr><td>Mod2+LAn+S2</td><td>0.76</td><td>0.76</td><td>0.77</td><td>0.76</td></tr><tr><td>Mod2+LAn+S2</td><td>0.73</td><td>0.71</td><td>0.76</td><td>0.71</td></tr><tr><td>Mod2+LAn+S3</td><td>0.71</td><td>0.68</td><td>0.79</td><td>0.68</td></tr><tr><td>Mod2+RAn</td><td>0.80</td><td>0.80</td><td>0.80</td><td>0.80</td></tr><tr><td>Mod2+L+RAn+S1</td><td>0.80</td><td>0.80</td><td>0.82</td><td>0.80</td></tr><tr><td>Mod2+L+RAn+S2</td><td>0.78</td><td>0.76</td><td>0.81</td><td>0.76</td></tr><tr><td>Mod2+L+RAn+S3</td><td>0.72</td><td>0.69</td><td>0.81</td><td>0.69</td></tr></table>
+
+Table 4: Non-neural classifiers.  
+An: annotated; S: synthetic  
+Base: Random Forests + TidfVectorizer(char bigrams)  
+Mod1: Base + gold morphological labels 'word class'  
+Mod2: Base + silver morphological labels 'POS'  
+L: Levenshtein distance  
+R:'inversion', 'plus-minus', 'unclassifiable' and 'morphological' rules  
+Values are rounded to the second digit after the decimal point. The highest results per data setting are indicated in bold.
+
+<table><tr><td>Models</td><td>F1</td><td>Ac</td><td>Pr</td><td>Re</td></tr><tr><td>NNAn.</td><td>0.80</td><td>0.80</td><td>0.80</td><td>0.80</td></tr><tr><td>NNAn.+S1</td><td>0.80</td><td>0.80</td><td>0.82</td><td>0.80</td></tr><tr><td>NNAn.+S2</td><td>0.78</td><td>0.76</td><td>0.81</td><td>0.76</td></tr><tr><td>NNAn.+S3</td><td>0.72</td><td>0.69</td><td>0.81</td><td>0.69</td></tr><tr><td>NAn.</td><td>0.78</td><td>0.78</td><td>0.79</td><td>0.78</td></tr><tr><td>NAn.+S1</td><td>0.76</td><td>0.76</td><td>0.77</td><td>0.76</td></tr><tr><td>NAn.+S2</td><td>0.77</td><td>0.77</td><td>0.78</td><td>0.77</td></tr><tr><td>NAn.+S3</td><td>0.74</td><td>0.73</td><td>0.76</td><td>0.73</td></tr></table>
+
+Table 5: Best non-neural vs neural (DictaBERT-based) classifiers per data setting. NN: non-neural; N: neural
+
+'synthetic 3'). The globally best train and evaluation batch sizes were 4 and 8, respectively. Table 5 summarises the best non-neural vs neural classifiers for each data setting.
+
+# 5 Discussion
+
+Whilst the highest performing model made use of an amount of synthetic data equal to the professionally annotated dataset, the applied data augmentation technique was not of significant benefit, in particular when neural models were concerned. Importantly, performance deteriorated perceptibly with the use of the largest synthetic dataset (10k data points per category), showing that further augmentation was unneeded. We conclude that the synthetic data failed to capture in sufficient detail the characteristics displayed by the annotated data. A hypothesis that remains to be tested is whether the models that include synthetic data have the quality of being more generalisable when different manuscripts (e.g. in terms of genre) are involved.
+
+An analysis of the neural models' performance per category revealed that whilst some of the categories' results were improved by the use of synthetic data (e.g. 'Morphohological', 'Unclassifiable'), results for the 'Inversion' category were significantly weaker, reaching an F1 value of just around 0.33 (against 0.60 for models without synthetic data). As the order of word pairs is lost upon classifier training at the word level, we conclude that inverted word pairs within the utilised manuscripts exhibit characteristics that were not captured effectively by the synthetic data i.e. by a process of random inversion combined with an analysis of POS tags and Levenshtein distances.
+
+Among the features used in non-neural classifiers, we note that silver standard POS tags performed significantly better than their gold standard counterparts, and despite a slight improvement, this remained the case even when the number of categories within the gold standard labels was reduced and they were made to match more closely the format of the silver ones. Possible explanations include a higher than expected quality of DictaBERT-based labels as well as their higher relevance to word-level analysis. The use of different combinations of morphological tags (e.g. number, gender, tense) in addition to POS tags led to varying performance that was always below that of POS tags when used in isolation. The Levenshtein distance between word pairs brought improvement of results
+
+when synthetic data was involved, but only a minor one, possibly due to redundancy with the word representations themselves. It remains unclear why such improvement was not exhibited by models based only on annotated data. The utilised manual rules for separate categories had a significant positive impact. For instance, their use of indexing in determination of the 'Inversion' category helped overcome a serious limitation posed by word-level classification.
+
+# 6 Conclusion and Future Work
+
+The current project involves the derivation of a classifier model that predicts the category of word-pair variants as found in collated manuscript witnesses. The strongest model is a non-neural (Random Forest) one that makes use of professionally annotated data based on extant manuscripts of the Book of Ben Sira as well as automatically derived synthetic data. Additional features defined as useful at the classification process include hand-crafted rules per category, Levenshtein distance and POS tags. As professionally annotated morphological labels are only available for selected texts, this study used the opportunity to compare their performance to that of automatically derived labels by the state-of-the-art DictaBERT model. Curiously, the latter helped the classifier models achieve higher results.
+
+Future plans pertaining to the authors' larger project include the use of the derived classifier to automatically annotate the word-level differences between multiple pairs of manuscripts, with a focus on the Dead Sea Scrolls. Consequently, the types and proportions of these differences are to be analysed statistically in view of their relevance in the determination of relationships between witnesses as present in established genealogical trees. Ultimately, automatisation of the process of tree generation will be sought.
+
+The derived classification model and the pipelines for synthetic data generation are readily applicable to texts in Classical Hebrew, importantly including texts that have not received high engagement and benefited from professional morphological annotation as of now, such as translations into Hebrew of Deuterocanonical books. With some modifications, the developed tools (e.g. the pipeline for synthetic data generation) are applicable to additional languages and tasks within the general field of stemmatology and NLP-based work with manuscripts.
+
+# Limitations
+
+The quality of morphological labels and, specifically, 'silver standard' ones, is not perfect, which can result in reduced performance of the trained classifiers. In turn, the derivation of synthetic data is also associated with limitations, such as the use of dictionaries that are markedly Modern-Hebrew. Also, the focus on word-level differences between textual witnesses as well as on word morphology, whilst hypothesised to serve as a reasonable proxy for the described texts' key characteristics, is not exhaustive. Alternative divisions of categories may also significantly alter the classification process; in particular, the latter would become a linear regression problem if variant differences are perceived as quantitative rather than qualitative, as they are in some sternmatological studies, e.g. Staalduine-Sulman (2005). Finally, the applied process of manual annotation based on a single text (the Book of Ben Sira) may also hold limited representativeness which, in turn, may be reflected in the developed classifier's applicability to other texts.
+
+# Ethics Statement
+
+All utilised resources (such as gold standard morphological information) are publicly available. Manual annotation was performed in controlled conditions in the involved team's university headquarters. As an ancient language is concerned by the study, its importance as cultural heritage is acknowledged. The choices of language and texts are not based on religious or political motivations, and textual interpretation is not approached.
+
+# Acknowledgements
+
+This work made major use of the manual collation and annotation of the examined witnesses of the Book of Ben Sira, performed by Davide D'Amico, postdoctoral researcher in Research Centre Écritures.
+
+# References
+
+Akademie der Wissenschaften zu Göttingen. 2021. Qumran-digital: Ein komplettes philologisches qumran-lexikon zum hebräischen und aramäischen. Accessed: 18.10.2022.  
+Pancratius C. Beentjes. 1997. Book of Ben Sira in Hebrew, volume 68 of Vetus Testamentum, Supplements. Brill.
+
+Zeev Ben-Hayyim. 1973. The Book of Ben Sira: Text, Concordance and an Analysis of the Vocabulary. Academy of the Hebrew Language and the Shrine of the Book.  
+Bernard Cerquiglini. 1983. Eloge de la variante. Languages, (69):25-35.  
+Avihay Chriqui and Inbal Yahav. 2021. Hebert and hebemo: a hebrew bert model and a tool for polarity analysis and emotion recognition. CoRR, abs/2102.01909.  
+Michael Collins, Jan Hajic, Lance Ramshaw, and Christoph Tillmann. 1999. A statistical parser for czech. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL).  
+Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186. Association for Computational Linguistics.  
+Eylon Gueta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker, and Reut Tsarfaty. 2023. Large pre-trained models with extra-large vocabularies: A contrastive analysis of hebrew bert models and a new one to outperform them all. Preprint, arXiv:2211.15199.  
+Tuomas Heikkilä. 2023. Computer-Assisted Stemmatology. Routledge.  
+Katherine L.(Ed.) Jansen. 1990. The new philology. Speculum, 65(1).  
+Geoffrey Khan, Shmuel Bologzky, Steven E. Fassberg, Gary A. Rendsburg, Aaron D. Rubin, Ora R. Schwarzwald, and Tamar Zewi, editors. 2013. Encyclopedia of Hebrew Language and Linguistics, 1 edition, volume 1-4. Brill, Leiden.  
+Matej Klemen, Luka Krsnik, and Marko Robnik-Sikonja. 2023. Enhancing deep neural networks with morphological information. Natural Language Engineering, 29(2):360-385.  
+Yuval Marton, Nizar Habash, and Owen Rambow. 2010. Improving arabic dependency parsing with lexical and inflectional morphological features. In Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages, pages 13-21.  
+Ray Pritz. 2016. Biblical hebrew and modern hebrew: How much do they understand? Journal of Biblical Text Research, 38:203-219.  
+Miguel Pérez Fernández and John F. El Wolde. 1999. An Introductory Grammar of Rabbinic Hebrew. Brill.
+
+Frédérique Michèle Rey and Eric Reymond. 2024. A Critical Edition of the Hebrew Manuscripts of Ben Sira: With Translations and Philological Notes. Brill, Leiden.  
+Philipp Roelli, editor. 2020. Handbook of Stemmatology. De Gruyter Reference. De Gruyter, Berlin/Boston.  
+William M. Schniedewind. 2013. A Social History of Hebrew: Its Origins Through the Rabbinic Period.  
+Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, and Reut Tsarfaty. 2020. A novel challenge set for hebrew morphological disambiguation and diacritics restoration. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3316-3326, Online. Association for Computational Linguistics.  
+Shaltiel Shmidman, Avi Shmidman, and Moshe Koppel. 2023. Dictabert: A state-of-the-art bert suite for modern hebrew. Preprint, arXiv:2308.16687.  
+Eveline van Staalduine-Sulman. 2005. Vowels in the trees: The role of vocalisation in stemmatology. *Aramaic Studies*, 3:215-240.  
+Angel Sáenz-Badillos. 1993. A History of the Hebrew Language. Cambridge University Press, Cambridge.  
+Angela Taylor. 2019. A contrastive analysis between biblical and modern hebrew in the context of the book of ruth. Journal of Biblical Studies.  
+Reut Tsarfaty and Khalil Sima'an. 2007. Three-dimensional parametrization for parsing morphologically rich languages. In Proceedings of the Tenth International Conference on Parsing Technologies, pages 156-167.  
+École Biblique et Archéologique Française de Jérusalem, editor. 1955-1982. Discoveries in the Judean Desert. Clarendon Press, Oxford.
+
+# A Categories and Subcategories Used in Data Annotation
+
+# Same
+
+This category, not represented in the classifier, is used to mark all word pairs in which the two items are identical or differ only in the presence or nature of diacritics or of symbols denoting a level of uncertainty about a letter's reading (e.g. //ravim; 'many' masc. pl.).
+
+# Plus/Minus
+
+As for the purpose of this study, all texts are assumed to be of the same hierarchical level, the term 'plus/minus' is opted for as opposed to the commonly used in textual criticism 'addition' and 'omission'. The category is used for cases in which one of the two variants is missing.
+
+# Inversion
+
+This category is used if 'word 1' in the given pair corresponds to 'word 2' in another pair found in close proximity in the manuscript and 'word 2' in the given pair corresponds to 'word 1' in the same closely situated pair. The corresponding words may be identical or feature minor differences, such as the addition of a coordinating conjunction or definite article.
+
+# Morphological
+
+The difference implies the words' morphological features.
+
+# Determination
+
+Only one of the variants features a definite article (e.g.  $\text{艹} \text{艹} / \text{艹}$ ; hayain/yain; 'the wine'/'wine').
+
+# Orthographical
+
+There is a spelling difference between the variants; in particular, the letters  $\mathfrak{n},\mathfrak{l},$  and  $\aleph$  may be added in one of the witnesses in order to aid vocalisation in a text that does not contain diacritical marks (e.g. yy/yy; poal; 'action').
+
+# Coordination
+
+One of the variants includes the coordinating conjunction  $\upharpoonleft$  (e.g.  $\mathfrak{A}^{\prime \prime} / \mathfrak{A}^{\prime \prime}$ ; velo/lo; 'and no'/no').
+
+# Preposition
+
+One of the variants contains a prefixed preposition  $\nexists$  (le; 'to, towards'),  $\supseteq$  (be; 'in') or  $\supseteq$  (ki; 'as, like') or the two variants contain different ones (e.g. negaf/benegaf; 'plague'/'in the plague').
+
+# Singular/Plural
+
+There is a difference in number between the variants, which may be a textbook case of singular versus plural versions of a noun or adjective (e.g. miin/mi; mitnot/matan; 'gifts'/a gift') or involve higher formal complexity, such as in the case of suffixed possessive pronouns (e.g.  $\text{日}$  /dva; dvarHa/dvarHa; 'your words'/'your word').
+
+# Masculine/Feminine
+
+There is a difference in gender between the variants (e.g.  $\frac{1}{2}$  /n; banim/banot; 'boys'/'girls'). Verb conjugations for different gender fall into the 'grammatical' rather than the 'masculine/feminine' subcategory.
+
+# SUFFIXed Pronoun
+
+Only one of the words in the pair contains a suffixed possessive or direct object pronoun (e.g. n/ny; retsono/ratson; 'his will'/will') or the two words contain different suffixed pronouns (e.g. n/la; leHa/la; 'to you'/to her').
+
+# Grammatical
+
+This is the broadest of the morphological subcategories and denotes different grammatical nature or function between the variants, such as different verb tense or form (e.g.  $\text{ルルルルルルルルルルルルルルルルルルルルルルルルルルルルルルルル化}$  mosif/yosif; 'to add' participle vs imperfect), different verb gender (e.g.  $\text{ルルルルルルル化}$  ; haya/haita; 'there was' masc. vs fem.), different part of speech (e.g.  $\text{ルルルル化}$  ; takaf/takif; 'to attack'/strong') or otherwise different words sharing the same root (e.g.  $\text{ルルル化}$  ; soHer/saHir; 'tenant'/hired worker'). A prefixed subordinating conjunction also implies this category (e.g.  $\text{ルルル化}$  ; yaHpots/sheyaHpots; 'he will desire' masc. sing. vs 'that he will desire'). Combinations of two or more grammatical differences may be involved (e.g.  $\text{ルル化}$  ; naim/noei; 'beautiful' adj. vs 'beauty, ornament' noun in construct state).
+
+# Lexical
+
+The difference between the variants is at the lexical level.
+
+# Letter Interchange
+
+There is a difference between the words in the pair pertaining to letters with high visual similarity (e.g. y/n/yn; tera/tera; 'to harm'/to know').
+
+# Phonetic Affinity
+
+The two variants are pronounced in the same or similar way (e.g. n/; mafalto/maftito; 'his defeat'/'his escape').
+
+# Metathesis
+
+The difference involves replacement of letters which may have occurred as a result of language development, such as to facilitate pronunciation (e.g. nən/na; HoHema; 'wisdom').
+
+# Misspelling
+
+There is a mistake within the spelling of one of the words in a pair. This category is generally avoided, as the fact that a word is not readily recognisable does not automatically mean that it is a misspelling of another, more intuitive word.
+
+# Synonym
+
+The two words in the pair are etymologically different but have the same or similar meaning, whether globally or in the given context (e.g. n/tn; tishkaH/timaHeh; 'she forgot'/'she erased').
+
+# Antonym
+
+The two words in a pair have opposite or contrasting meaning (e.g. n/; vetet/velakaH; 'he gave'/'he took';  $\text{日} _ { \text{日} }$  /HaHam/Hamas; wisdom'/violence').
+
+# Unclassifiable
+
+This category is used for instances where one or both of the variants are unidentifiable solely based on the given manuscript. Restored text with high uncertainty (i.e. marked with square brackets) is always attributed this category. Note that sometimes restored text encompasses multiple words, in which case square brackets are present only in the beginning and end of the group.
+
+# B Pipelines for Generation of Synthetic Data by Category and Subcategory
+
+# Plus/Minus
+
+Either 'word 1' or 'word 2' (selected at random) is populated with a random word from the randomised Qumran sample until the desired number of entries is achieved.
+
+# Inversion
+
+Random entries are generated where 'word 1' and 'word 2' with Levenshtein distance of at least 2 are taken from the randomised sample. Reversed versions of each pair are also composed. The matching entries are then organised so as to be either adjacent or nearly so following a distribution, close to the one in the manually annotated corpus.
+
+# Morphological
+
+# Determination
+
+Words with POS 'verb', 'noun', 'adverb' of 'adjective' are taken from the randomised sample, ensuring that they do not already contain a prefix with the help of the DictaBERT-seg model. Word pairs are formed with the original words as 'word 1' and the identical words as preceded by the definite article (n) as 'word 2'. The words in each pair are shuffled.
+
+# Orthographical
+
+Words are taken from the randomised sample until the desired size is met. For half of them, random existing  $\upharpoonleft$  and letters are removed. For the other half,  $\upharpoonleft$ , and  $\aleph$  (the last one in no more than  $10\%$  of cases) are added in random positions. It is ensured that initial  $\upharpoonleft$  used as a coordinating conjunction and  $\upharpoonleft$  used as a possessive pronoun are not altered. The words in each pair are shuffled.
+
+# Coordination
+
+Words are taken from the randomised sample, ensuring that no more than half of them are verbs and that they do not contain prefixed particles with the model DictaBERT-seg. Word pairs are formed from the original words and the same words preceded by the coordinating conjunction (1). The words in each pair are shuffled.
+
+# Preposition
+
+Words are taken from the randomised sample, ensuring that about half of them are nouns and 1/3 are pronouns and adpositions; and that they do not contain prefixed particles. A list of valid prepositions is defined:  $\neg ,\neg ,\neg ,\neg .$  Word pairs are formed based on each random word in one of the following five scenarios: 'word 1' contains no prepositions and 'word 2' contains one preposition; 'word 1' contains no prepositions and 'word 2' contains two prepositions; 'word 1' and 'word 2' each contain a different preposition; 'word 1' contains a coordinating conjunction and 'word 2' contains a coordinating conjunction and a preposition; 'word 1' and 'word 2' each contain a coordinating conjunction and a different preposition. The words in each pair are shuffled.
+
+# Singular/Plural
+
+Singular nouns are taken from the random sample. 'Word 1' is populated with the original words and 'word 2' with their pluralised versions (for masculine; or occasionally for feminine; any final ns are removed). The words in each pair are shuffled.
+
+# Masculine/Feminine
+
+First, the base word pairs  $\mathfrak{m}$  and  $\mathfrak{m}$  ('brother' and 'sister'),  $\mathfrak{m}$  and  $\mathfrak{m}$  ('son' and 'daughter') as well as several versions of them including pluralisation and randomly added prefixes and suffixes are taken. Then, several hard-coded common pairs
+
+of masculine/feminine nouns are added $^{21}$ . Finally, adjectives without suffixes or final  $\bar{\mathfrak{n}}^{22}$  are taken from the randomised sample and their gender is changed following hand-crafted rules: a final  $\bar{\mathfrak{n}}$  is added or removed to render a singular adjective respectively feminine or masculine; the endings  $\bar{\mathfrak{n}}$  (or  $\bar{\mathfrak{m}}\bar{\mathfrak{n}}$ ) are replaced with  $\bar{\mathfrak{n}}$  and vice versa to render plurals feminine or masculine. All derived entries as well as the words in each pair are shuffled.
+
+# SUFFIXed Pronoun
+
+As only transitive verbs can take suffixed pronouns, a number of verbs are taken from the sample and the transitive ones are filtered with the help of expert knowledge and  $\mathrm{ChatGPT}^{23}$ . Then, lists of all possible combinations of particles and pronouns suffixed to them are composed and pairs of them accounting for  $10\%$  of the desired subsample are added. For the rest of the entries, nouns are taken from the randomised sample and pairs are formed with the word as 'word 1' and the word plus a random pronoun[24] as 'word 2'; in  $20\%$  of cases, different random pronouns are added to both words. All derived entries as well as the words in each pair are shuffled.
+
+# Grammatical
+
+Verbs are taken from the random sample to populate 'word 1'. For the population of 'word 2', the Modern Hebrew congregation website *Pealim* $^{25}$ , is web-crawled. In a first scenario, a different random conjugation of the same verb is taken. In a second scenario, a conjugation of an etymologically related word is used instead. In  $20\%$  of cases, 'word 1' is also changed with a related word, ensuring a variety in parts of speech. The words in each pair are shuffled.
+
+# Lexical
+
+# Letter Interchange
+
+A dictionary of visually similar Hebrew letters is defined[26] and random words are sought that contain at least one of the implied letters. To form the word pairs, a random relevant letter is swapped
+
+from each word based on the dictionary. The words in each pair are shuffled.
+
+# Phonetic Affinity
+
+A dictionary of phonetically similar Hebrew letters is defined[27] and random words are sought that contain at least one of the letters. To form the word pairs, a random relevant letter is swapped from each word based on the dictionary. The two words in each pair are shuffled.
+
+# Metathesis
+
+Words are taken from the randomised sample, ensuring that at least  $20\%$  of them contain adjacent  $\neg$  and  $\neg$  letters. For the words containing said letters, their places are reversed. For the rest of the words, two random adjacent letters are swapped. The two words in each pair are shuffled.
+
+# Misspelling
+
+Words are taken from the randomised sample. To form pairs, they are modified in one of the following scenarios: one of the word's letters is replaced by a random letter; two of the word's letters are replaced by random letters; one of the word's letters is deleted. In the case of words starting with a particle, the particle is not altered. The words in each pair are shuffled.
+
+# Synonyms and Antonyms
+
+The Modern Hebrew online dictionary  $\text{Milog}^{28}$  is crawled using the Python library  $\text{requests}^{29}$ . Random one-word synonyms and antonyms of tokens from the randomised sample are taken until a defined number of entries are found for which at least either one synonym or one antonym exists. As diacritic signs are used in the dictionary but typically not in the Qumran texts, they are removed from a portion of the derived data. The words in each pair are shuffled. Antonyms were retrieved for about 1/4 of the sought random words, and synonyms - for a little over a half of them.
+
+# Unclassifiable
+
+Words are taken from the randomised sample and square brackets are added at random positions within them in the following ways: either  $[ \cdot ]$  or  $\cdot \cdot \cdot$  is added to word 2' (2/3 of cases); both  $[ \cdot ]$  and  $\cdot \cdot \cdot$  (in this order) are added to word 1' (1/6 of cases);
+
+$\left[\right], \left[\right]$  or both symbols are added to both words (1/6 of cases). The words in each pair are shuffled.
+
+# C Gold Standard Labels, Encountered in the Annotated Dataset
+
+# Lemma
+
+The basic form or forms associated with a word. It may consist in the removal of signs denoting manuscript reconstruction and vocalisations; a division of the root and affixes; and the reduction to a default form in terms of number (singular), gender (masculine) or person (third).
+
+# Word class
+
+The categories correspond roughly to conventional POS tags but involve higher specificity. For instance, pronouns and proper nouns are divided into subcategories (personal, question; name of a person, of a group of people, of a god, of a place). For relevant parts of speech, the different numbers, genders and persons form separate labels. 'Letter' is also a defined category. Some categories used in Universal Dependencies $^{30}$  (UD), such as 'punctuation', are not present.
+
+# Short definition
+
+Translation or definition of the word in German.
+
+# Root designation
+
+May take values of 'I', 'II', 'III', 'IV' or 'V'. It is related to the context-specific meaning of the root as indexed in the dictionary associated with the Qumran-Digital project<sup>31</sup>.
+
+# Verb stem
+
+The type or group of Hebrew verb implied (e.g. hif'il, nif'al, pi'el), which is often indicative of the verb's general meaning or aspect.
+
+# Verb tense
+
+A verb's tense. May be 'imperfect', 'participle', 'perfect', 'imperative', 'construct infinitive', 'consecutive imperfect', 'consecutive perfect' or 'cohortative'.
+
+# Person
+
+Used for applicable parts of speech. May be '1', '2' or '3'.
+
+# Gender
+
+Used for applicable parts of speech. May be 'masculine', 'feminine' or 'common'.
+
+# Number
+
+Used for applicable parts of speech. May be 'singular', 'plural' or 'dual'.
+
+# State
+
+Used for applicable parts of speech. May be 'absolute', 'construct' (i.e. forming a genitive construction) or 'determination' (i.e. it includes a definite article or demonstrative pronoun).
+
+# Augment
+
+Emphasises the subject's relationship to the action. The only detected value is 'energetic'
+
+# Suffix person
+
+Designates the person implied by the suffix. May be 'singular' or 'plural'.
+
+# Suffix number
+
+Designates the number implied by the suffix. May be '1', '2' or '3'.
+
+# D Silver Standard Labels, Encountered in the Annotated Dataset
+
+# POS
+
+POS tags corresponding to UD conventions: ADP (adposition; preposition or postposition), ADV (adverb), AUX (auxiliary verb), CCONJ (coordinating conjunction), DET (determiner), INTJ (interjection), NOUN (common noun), NUM (numerical), PRON (pronoun), PROPN (proper noun), PUNCT (punctuation), SCONJ (subordinating conjunction), VERB (verb), X (not classified).
+
+# Gender
+
+Used for applicable parts of speech. May be 'masculine', 'feminine' or 'masculine and feminine'.
+
+# Number
+
+Used for applicable parts of speech. May be 'singular' or 'plural'.
+
+# Person
+
+Used for applicable parts of speech. May be '1',
+
+'2', '3' or '1, 2, 3'.
+
+# Tense
+
+A verb's tense. May be 'future', 'past', 'present' or 'imperfect'.
+
+# Prefixes
+
+Features a list value of the POS tags of any prefixes that the word contains.
+
+# Suffix
+
+Features the POS tag of any suffixes that the word contains. Combinations of POS tags appear as a single predefined value (e.g. ADP_PRON).
+
+# E Gold vs Silver Standard POS Tags
+
+<table><tr><td>Silver</td><td>Gold</td></tr><tr><td>VERB</td><td>verb</td></tr><tr><td>AUX</td><td>verb</td></tr><tr><td>NOUN</td><td>noun; noun masculc; noun fem;common noun</td></tr><tr><td>ADP</td><td>preposition; object marker</td></tr><tr><td>CCONJ</td><td>conjunction</td></tr><tr><td>SCONJ</td><td>conjunction; relative particle</td></tr><tr><td>INTJ</td><td>negation; interjection</td></tr><tr><td>ADV</td><td>adverbial particle</td></tr><tr><td>PROPN</td><td>name of god; name of person; name of group; name of place; name of month; name of region</td></tr><tr><td>PRON</td><td>question pronoun, person; question pronoun, thing; demonstrative pronoun, masculc sing; demonstrative pronoun, common plural; personal pronoun, 3 masculc sing; personal pronoun, 2 masculc sing; personal pronoun, 2 fem sing; personal pronoun, 3 fem sing; personal pronoun, 1 common sing; question pronoun, place</td></tr><tr><td>X</td><td>letter</td></tr><tr><td>ADJ</td><td>None</td></tr><tr><td>DET</td><td>None</td></tr><tr><td>NUM</td><td>None</td></tr><tr><td>PUNCT</td><td>None</td></tr></table>
+
+Table 6: Mapping of silver to gold standard POS tags.
+
+F Detailed Classifier Results  
+
+<table><tr><td>Model</td><td>Data</td><td>F1</td><td>Ac</td><td>Pr</td><td>Re</td></tr><tr><td>Base</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.67</td><td>0.67</td><td>0.67</td><td>0.67</td></tr><tr><td></td><td>An + S1</td><td>0.68</td><td>0.68</td><td>0.69</td><td>0.68</td></tr><tr><td></td><td>An + S2</td><td>0.67</td><td>0.65</td><td>0.70</td><td>0.75</td></tr><tr><td></td><td>An + S3</td><td>0.66</td><td>0.66</td><td>0.74</td><td>0.61</td></tr><tr><td>Mod1 (1) (all)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.66</td><td>0.66</td><td>0.67</td><td>0.66</td></tr><tr><td>Mod1 (2) (all)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.66</td><td>0.67</td><td>0.67</td><td>0.66</td></tr><tr><td>Mod1 (2) (all but ‘verb tense’)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.67</td><td>0.68</td><td>0.68</td><td>0.68</td></tr><tr><td>Mod1 (2) (&#x27;word class&#x27;, &#x27;number&#x27;, &#x27;verb stem&#x27;, &#x27;gender&#x27; and &#x27;suffix-person&#x27;)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.67</td><td>0.67</td><td>0.67</td><td>0.67</td></tr><tr><td>Mod1 (2) (&#x27;word class&#x27;, &#x27;number&#x27;)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.66</td><td>0.67</td><td>0.67</td><td>0.67</td></tr><tr><td>Mod1 (2) (&#x27;word class&#x27;)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.69</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Mod1 (2) &#x27;word class, simplified&#x27;)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Mod2 (all)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.63</td><td>0.64</td><td>0.65</td><td>0.64</td></tr><tr><td>Mod2 (all but ‘gender’)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.71</td><td>0.72</td><td>0.73</td><td>0.72</td></tr><tr><td></td><td>An + S1</td><td>0.70</td><td>0.71</td><td>0.72</td><td>0.70</td></tr><tr><td></td><td>An + S2</td><td>0.69</td><td>0.69</td><td>0.72</td><td>0.69</td></tr><tr><td></td><td>An + S3</td><td>0.68</td><td>0.67</td><td>0.76</td><td>0.67</td></tr><tr><td>Mod2 (&#x27;POS&#x27;)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.74</td><td>0.75</td><td>0.75</td><td>0.74</td></tr><tr><td></td><td>An + S1</td><td>0.72</td><td>0.72</td><td>0.73</td><td>0.72</td></tr><tr><td></td><td>An + S2</td><td>0.68</td><td>0.67</td><td>0.70</td><td>0.67</td></tr><tr><td></td><td>An + S3</td><td>0.69</td><td>0.69</td><td>0.80</td><td>0.69</td></tr><tr><td>Mod2 (&#x27;POS&#x27;) + L</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>An</td><td>0.72</td><td>0.73</td><td>0.74</td><td>0.73</td></tr><tr><td></td><td>An + S1</td><td>0.76</td><td>0.76</td><td>0.77</td><td>0.76</td></tr><tr><td></td><td>An + S2</td><td>0.73</td><td>0.71</td><td>0.76</td><td>0.71</td></tr><tr><td></td><td>An + S3</td><td>0.71</td><td>0.68</td><td>0.79</td><td>0.68</td></tr></table>
+
+Mod2('POS') + R ('inversion')  
+
+<table><tr><td></td><td>An</td><td>0.75</td><td>0.75</td><td>0.76</td><td>0.75</td></tr><tr><td colspan="6">Mod2 (&#x27;POS&#x27;) + R (&#x27;plus-minus&#x27;)</td></tr><tr><td></td><td>An</td><td>0.75</td><td>0.76</td><td>0.78</td><td>0.76</td></tr><tr><td colspan="6">Mod2 (&#x27;POS&#x27;) + R (&#x27;inversion&#x27;, &#x27;plus-minus&#x27;)</td></tr><tr><td></td><td>An</td><td>0.75</td><td>0.76</td><td>0.78</td><td>0.76</td></tr><tr><td colspan="6">Mod2 (&#x27;POS&#x27;) + R (&#x27;inversion&#x27;, &#x27;plus-minus&#x27;, &#x27;unclassifiable&#x27;)</td></tr><tr><td></td><td>An</td><td>0.79</td><td>0.79</td><td>0.80</td><td>0.80</td></tr><tr><td colspan="6">Mod2 (&#x27;POS&#x27;) + R (&#x27;inversion&#x27;, &#x27;plus-minus&#x27;, &#x27;unclassifiable&#x27;, &#x27;morphological&#x27;)</td></tr><tr><td></td><td>An</td><td>0.80</td><td>0.80</td><td>0.80</td><td>0.80</td></tr><tr><td colspan="6">Mod2 (&#x27;POS&#x27;) + L + R (&#x27;inversion&#x27;, &#x27;plus-minus&#x27;, &#x27;unclassifiable&#x27;, &#x27;morphological&#x27;)</td></tr><tr><td></td><td>An + S1</td><td>0.80</td><td>0.80</td><td>0.82</td><td>0.80</td></tr><tr><td></td><td>An + S2</td><td>0.78</td><td>0.76</td><td>0.81</td><td>0.76</td></tr><tr><td></td><td>An + S3</td><td>0.72</td><td>0.69</td><td>0.81</td><td>0.69</td></tr><tr><td colspan="6">N</td></tr><tr><td></td><td>An</td><td>0.78</td><td>0.78</td><td>0.79</td><td>0.78</td></tr><tr><td></td><td>An + S1</td><td>0.76</td><td>0.76</td><td>0.77</td><td>0.76</td></tr><tr><td></td><td>An + S2</td><td>0.77</td><td>0.77</td><td>0.78</td><td>0.77</td></tr><tr><td></td><td>An + S3</td><td>0.74</td><td>0.73</td><td>0.76</td><td>0.73</td></tr></table>
+
+Table 7: Trained Classifiers  
+An: annotated; S: synthetic  
+Base: Random Forests + TfidfVectorizer (char bigrams)  
+Mod1: Base + gold morphological labels  
+Mod1 (1): all gold labels per feature considered  
+Mod1 (2): only the most frequent gold label per feature considered  
+Mod2: Base + silver morphological labels  
+L: Levenshtein distance  
+R: hand-crafted rules N: neural (DictaBERT-based) model  
+Values are rounded to the second digit after the decimal point.
+
+# G Sample Label Predictions
+
+<table><tr><td>Word 1</td><td>Word 2</td><td>Real</td><td>Predicted</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td></td><td></td><td>Plus_Minus</td><td>Unclassifiable</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Morphological</td><td>Morphological</td></tr><tr><td></td><td></td><td>Plus_Minus</td><td>Plus_Minus</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Morphological</td><td>Morphological</td></tr><tr><td></td><td></td><td>Lexical</td><td>Lexical</td></tr><tr><td>&quot; &quot;</td><td></td><td>Plus_Minus</td><td>Plus_Minus</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td></td><td></td><td>Plus_Minus</td><td>Plus_Minus</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Morphological</td><td>Morphological</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td>&quot; &quot;</td><td>( &quot; &quot; )</td><td>Lexical</td><td>Unclassifiable</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Unclassifiable</td><td>Unclassifiable</td></tr><tr><td></td><td></td><td>Plus_Minus</td><td>Plus_Minus</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Lexical</td><td>Lexical</td></tr><tr><td>&quot; &quot;</td><td>&quot; &quot;</td><td>Unclassifiable</td><td>Unclassifiable</td></tr><tr><td></td><td></td><td>Unclassifiable</td><td>Plus_Minus</td></tr></table>
+
+Table 8: The real vs predicted labels of a sample of annotated word pairs as per the strongest achieved classifier model.

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2025/A Cognitive Writing Perspective for Constrained Long-Form Text Generation/5246566f-1276-4d18-ae6f-d056318061c7_content_list.json

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+        "type": "text",
+        "text": "A Cognitive Writing Perspective for Constrained Long-Form Text Generation",
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+        "text": "Kaiyang Wan $^{1}$ , Honglin Mu $^{1}$ , Rui Hao $^{2}$ , Haoran Luo $^{3}$ , Tianle Gu $^{1}$ , Xiuying Chen $^{1*}$",
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+        "type": "text",
+        "text": "$^{1}$ MBZUAI,  $^{2}$ University of Chinese Academy of Sciences,  $^{3}$ Nanyang Technological University {Kaiyang.Wan, Xiuying.Chen} @mbzuai.ac.ae",
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+        "text": "Abstract",
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+        "type": "text",
+        "text": "Like humans, Large Language Models (LLMs) struggle to generate high-quality long-form text that adheres to strict requirements in a single pass. This challenge is unsurprising, as successful human writing, according to the Cognitive Writing Theory, is a complex cognitive process involving iterative planning, translating, reviewing, and monitoring. Motivated by these cognitive principles, we aim to equip LLMs with human-like cognitive writing capabilities through CogWriter, a novel training-free framework that transforms LLM constrained long-form text generation into a systematic cognitive writing paradigm. Our framework consists of two key modules: (1) a Planning Agent that performs hierarchical planning to decompose the task, and (2) multiple Generation Agents that execute these plans in parallel. The system maintains quality via continuous monitoring and reviewing mechanisms, which evaluate outputs against specified requirements and trigger necessary revisions. CogWriter demonstrates exceptional performance on LongGenBench, a benchmark for complex constrained long-form text generation. Even when using Qwen-2.5-14B as its backbone, CogWriter surpasses GPT-4o by  $22\\%$  in complex instruction completion accuracy while reliably generating texts exceeding 10,000 words. We hope this cognitive science-inspired approach provides a paradigm for LLM writing advancements:  $\\mathbb{O}$  CogWriter.",
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+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
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+        "text": "LLMs like ChatGPT (Achiam et al., 2023) have begun to mirror human-like capabilities across diverse natural language processing tasks (Xi et al., 2023; Luo et al., 2024). From crafting concise summaries (Chen et al., 2025b, 2024a) to composing structured reports (Schmidgall et al., 2025; Wang et al., 2024d), these models can generate coherent text in a single pass (Rasheed et al., 2025;",
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+        "text": "Minaee et al., 2024) with a fluency that often rivals human writers. Recent advances have led to models with expanded context windows of up to 128K tokens (Pawar et al., 2024), theoretically enabling the generation of extensive documents (Bai et al., 2024). However, these models face significant challenges when tasked with generating constrained long-form text under complex constraints, such as following detailed instructions over 10,000 words (Wu et al., 2024a). This limitation poses a crucial barrier for applications requiring extended (Shi et al., 2024), well-structured content, including creative design proposals, technical documentation, and comprehensive research reports.",
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+    {
+        "type": "text",
+        "text": "To understand the disparity between LLMs and human writers, we refer to Cognitive Writing Theory (Flower, 1981), which emphasizes how humans succeed in writing through a recursive activity that dynamically integrates multiple cognitive processes. As shown in the top part of Figure 1, these processes include planning, where writers establish high-level goals and develop structural outlines; translating, where writers transform abstract ideas into coherent text; and reviewing, where writers continuously evaluate and refine their generated content. Crucially, writers control these components through continuous monitoring, allowing them to assess and adjust text to better align with evolving objectives throughout the writing process.",
+        "bbox": [
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+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Current LLMs excel at generating fluent text, effectively performing the translating function of converting internal token vectors into textual content. However, they fundamentally conflict with key cognitive principles in three ways, as shown in the bottom part of Figure 1: 1) They treat long-form text generation merely as an end-to-end task, overlooking the crucial hierarchical planning process that should guide content generation; 2) Their autoregressive architecture renders generated tokens as immutable context, preventing the reviewing and restructuring capabilities essential to hu",
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+    },
+    {
+        "type": "page_footnote",
+        "text": "*Corresponding author.",
+        "bbox": [
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "page_number",
+        "text": "9832",
+        "bbox": [
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+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: ACL 2025, pages 9832-9844 July 27 - August 1, 2025 ©2025 Association for Computational Linguistics",
+        "bbox": [
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+            945,
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+        "page_idx": 0
+    },
+    {
+        "type": "image",
+        "img_path": "images/c209e2c76607b16f4fd39e6faee6ad8504829746a7bd70cf432f77b89f0168a8.jpg",
+        "image_caption": [
+            "Figure 1: Comparison of human cognitive writing processes and single LLM text generation paradigm."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "man writing; and 3) Unlike human writers who actively monitor their progress against both local and global objectives, LLMs lack explicit evaluation mechanisms, leading to potential divergence from intended goals in extended generations.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "To address the limitations of single-pass generation, we introduce CogWriter, a novel training-free framework that aligns LLM-based text generation with cognitive writing paradigm. At its core, CogWriter employs a Planning Agent that decomposes complex requirements into manageable subtasks, providing explicit guidance for content generation. Based on sub-plans and the initial goal, multiple Generation Agents work in parallel to produce text segments, enabling both efficient generation and quality control that ensures consistent alignment with requirements. Crucially, both the planning and generation processes support iterative reviewing through feedback from external monitoring functions and LLM-based evaluation, thus enabling dynamic plan adjustment and content revision.",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "We evaluate CogWriter on LongGenBench-16K (Wu et al., 2024a), a benchmark designed to test a language model's ability to generate instruction-aligned content about 16K tokens. Empirical results demonstrate that our paradigm is effective for both closed-source and open-source LLMs of various sizes. Specifically, even when using Qwen-2.5-14B as its backbone, CogWriter achieves a  $22\\%$  higher instruction completion accuracy rate compared to GPT-4o, while reliably generating texts exceeding 10,000 words. These results demonstrate the effectiveness of cognitive science-inspired approaches in advancing LLM writing capabilities, particularly for complex constrained long-form text generation. We hope CogWriter's systematic cognitive writing paradigm will inspire future research in LLM writing advancement.",
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+        "page_idx": 1
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+    {
+        "type": "text",
+        "text": "Our contributions can be summarized as follows:",
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+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- We provide a cognitive science perspective on the shortcomings of single-pass LLM generation, highlighting how it diverges from established successful human writing processes.",
+            "- We propose CogWriter, a cognitive writing framework that equips LLMs with human writing strategies using multiple LLM-based agents with external monitoring functions.",
+            "- We demonstrate that CogWriter remarkably enhances LLMs' ability to produce long-form, instruction-compliant texts without requiring additional training or reinforcement learning."
+        ],
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "2 A Cognitive Writing Perspective",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "The challenge of constrained long-form text generation extends far beyond simply producing more words. Just as a novelist crafts an intricate narrative or an architect designs a towering structure, long text generation requires the coordination of multiple cognitive processes working together. Through the lens of cognitive writing theory, three fundamental processes emerge: hierarchical planning, continuous monitoring, and dynamic reviewing (Flower, 1981), as illustrated in Figure 1.",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Hierarchical Planning Long-form writing requires a delicate cognitive balance between maintaining local coherence and global structure. Human writers cope with this constraint, as working memory cannot simultaneously retain every detail of a complex narrative (Kellogg, 2013). Skilled writers manage this limitation through hierarchical decomposition, systematically structuring the writing process into multiple levels (e.g., chapters, sections, and paragraphs). This approach enables",
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+        "page_idx": 1
+    },
+    {
+        "type": "page_number",
+        "text": "9833",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "them to alternate between top-down thematic planning and bottom-up content development, ensuring alignment with high-level objectives while refining details (Hayes and Flower, 2016).",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "LLMs encounter a similar limitation: they generate text in a linear, autoregressive manner without an independent planning module to iteratively refine outlines or adapt strategies in real time (Xie et al., 2023). Consequently, their direct prompt-to-text generation process often struggles with complex, multi-threaded narratives. Without structured guidance, LLMs are prone to losing coherence over long spans, as their finite computational capacity quickly becomes overwhelmed (Hu et al., 2024).",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Continuous Monitoring Effective planning in writing requires continuous oversight. Human writers naturally monitor their work, acting like their own editors. They pay attention to both small details—such as word choice and sentence flow—and the larger structure, ensuring the text maintains a clear theme and purpose (Kellogg, 2013).",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "In contrast, current mainstream LLMs generate text in a linear, close-loop manner, without the ability to review or refine their output. They lack a built-in system to check their progress against the intended goals, making it difficult to spot and correct issues during generation. Without external monitoring, LLMs struggle to detect when the content drifts off-topic, when the style becomes inconsistent, or when repetition occurs—problems that are especially common in extended long-form writing (Wang et al., 2024c; Ping et al., 2025).",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Dynamic Reviewing While monitoring continuously tracks the writing process by detecting small errors, inconsistencies, or deviations, reviewing takes this feedback and applies it to make necessary adjustments, such as reorganizing content or improving logical flow. Human writers naturally engage in this iterative reviewing process, refining their work by revisiting earlier content and making adjustments (Bereiter and Scardamalia, 2013).",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "However, LLMs lack this ability due to their left-to-right, single-pass generation (Yao et al., 2023; Wu et al., 2024b). Without the ability to revisit or reorganize previous content, LLMs struggle with global revisions, such as restructuring sections or ensuring consistency across distant parts of the text (Bae and Kim, 2024; Cheng et al., 2024a,b). This absence of dynamic reviewing often results in long-form outputs with accumulated errors, inconsistencies, or redundant content.",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3 Problem Formulation",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Based on the analysis in Section 2, successfully generating long-form text requires addressing key deficiencies in current LLMs. We propose a new paradigm that equips LLMs with essential abilities to handle long, complex, and instruction-driven text generation. To achieve this, we formally define the constrained long-form text generation task, specifying the types of instructions and requirements the model must meet.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Following Wu et al. (2024a), we formally define constrained long-form generation as the task of generating a sequence of interrelated text segments  $\\mathcal{D} = \\{D_1, D_2, \\ldots, D_n\\}$ , where each  $D_i$  represents a coherent unit of text that must satisfy certain constraints. Each segment  $D_i$  must achieve a target  $L$  words and adhere to a set of instructions  $\\mathcal{T}$ . The instructions  $\\mathcal{T}$  guide the generation process and are classified into three types: 1. Single Instruction (SI): This instruction specifies content that must appear at exact, predefined positions. It is denoted as  $\\mathcal{T}_S = \\{T_{s1}, T_{s2}, \\ldots\\}$ , where each  $T_{si}$  indicates specific content that must be placed in a precise position within the generated descriptions. 2. Range Instruction (RI): This instruction specifies the content that must be included in each description within a designated range. It is represented as  $\\mathcal{T}_R = \\{T_i, T_{i+1}, \\ldots, T_{i+j}\\}$ , ensuring that the specified content is sequentially assigned within the range  $[i, i+j]$ . 3. Periodic Instruction (PI): This instruction mandates the periodic repetition of specific content at regular intervals. It is defined as  $\\mathcal{T}_P = \\{T_n, T_{2n}, \\ldots, T_{m·n}\\}$ , where  $n$  is the interval length and  $m$  specifies the number of repetitions. These instructions are unified into a comprehensive Check Set:  $\\mathcal{T} = \\{\\mathcal{T}_S, \\mathcal{T}_R, \\mathcal{T}_P\\}$ .",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "The versatility of this framework extends to various practical applications. For example, in architectural planning for a 100-floor building, Single Instructions determine specific facilities like a medical center on the 20th floor, Range Instructions define functional zones like corporate offices spanning floors 5-12, and Periodic Instructions maintain consistent amenities such as security checkpoints on every fifth floor. Each floor description must meet a target length of 200 words.",
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+    },
+    {
+        "type": "text",
+        "text": "4 Methodology",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Drawing upon our analysis of cognitive writing processes and the identified limitations of single-pass generation approaches, in this section, we propose",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "9834",
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "image",
+        "img_path": "images/90f2ca4d3c712c7c8940f81a2e33ea456885634f969d32a3be1fc56009944be2.jpg",
+        "image_caption": [
+            "Figure 2: Overview of the CogWriter Framework. The framework consists of two key modules: the Planning Agent and the Generation Agents. The Planning Agent generates and refines an initial plan, guiding the structure and flow of the document. The Generation Agents collaborate to generate, revise, and finalize document segments, ensuring consistency in content and narrative coherence across the entire document."
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "CogWriter, a training-free framework that equips LLM with cognitive writing capabilities and enables LLMs to tackle complex constrained long-form generation with human-like strategic thinking.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "4.1 Framework Overview",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "As shown in Figure 2, CogWriter is designed to bridge the gap between current LLMs and human-like writing processes by integrating planning, monitoring, and reviewing mechanisms into the generation workflow. At its core, CogWriter employs a specialized Planning Agent that hierarchically decomposes the task and create structured plans, breaking down complex writing tasks into manageable components while maintaining their intricate relationships. Generation Agents execute these plans while monitoring mechanisms continuously evaluate the output to detect deviations in content, structure, or requirements. When issues are identified by monitor or LLM, a review process is triggered to revise and refine the output, ensuring overall coherence and adherence to instructions.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "4.2 Planning Agent",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "The Planning Agent serves as the strategic brain of the system. Similar to how an experienced writer begins with a detailed outline, this agent analyzes",
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "task requirements and generates a structured initial plan  $\\mathcal{P}_{\\mathrm{initial}}$  under strict format constraints:",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {P} _ {\\text {i n i t i a l}} \\leftarrow \\operatorname {G e n e r a t e I n i t i a l P l a n} \\left(p _ {\\text {p l a n}}\\right),\n$$\n",
+        "text_format": "latex",
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+    },
+    {
+        "type": "text",
+        "text": "where  $p_{plan}$  is the task-specific prompt incorporating instruction descriptions  $\\mathcal{T}$ . The target plan is hierarchical, comprising unit plans:  $\\mathcal{P}_{\\mathrm{initial}} = \\{P_{\\mathrm{initial}_1},\\dots,P_{\\mathrm{initial}_n}\\}$ .",
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+    },
+    {
+        "type": "text",
+        "text": "After generating the initial plan, the monitoring mechanism supervises the process and relays signals to the reviewing mechanism for evaluation and validation. The reviewing mechanism evaluates the plan through two key checks: First, it verifies if the generated content satisfies the task-specific constraints  $\\mathcal{T}$ . Second, it checks the plan's structure for any syntax errors and applies necessary corrections. If any issues are detected, a revision process is triggered to refine the plan:",
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+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {P} _ {\\text {r e v i s e d}} \\leftarrow \\operatorname {P l a n R e v i s e} \\left(p _ {\\text {r e v i s e}}, \\mathcal {P} _ {\\text {i n i t i a l}}\\right), \\tag {1}\n$$\n",
+        "text_format": "latex",
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+        "page_idx": 3
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathcal {P} \\leftarrow \\text {F o r m a t R e v i s e} \\left(\\mathcal {P} _ {\\text {r e v i s e d}}\\right), \\tag {2}\n$$\n",
+        "text_format": "latex",
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "where  $p_{\\mathrm{revised}}$  includes the revision prompt for the task instructions  $\\mathcal{T}$ . This iterative refinement ensures that the final plan is not only of high quality but also optimally structured to guide robust and effective content generation.",
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "9835",
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Algorithm 1 CogWriter Algorithm",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Require: Prompts  $p_*$  including task instruction  $\\mathcal{T}$",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Ensure: Final text  $\\mathcal{D} = \\{D_1, \\ldots, D_n\\}$",
+        "bbox": [
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+        "page_idx": 4
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+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "1: function PLANNINGAGENT  $(p_{*})$",
+            "2:  $\\mathcal{P}_{\\mathrm{initial}} \\gets \\operatorname{GenerateInitialPlan}(p_{\\mathrm{plan}})$",
+            "3:  $\\mathcal{P}_{\\mathrm{revised}} \\gets \\mathrm{PlanRevise}(p_{\\mathrm{revise}}, \\mathcal{P}_{\\mathrm{initial}})$",
+            "4:  $\\mathcal{P} \\gets$  FormatRevise( $\\mathcal{P}_{\\text{revised}}$ )",
+            "5: return  $\\mathcal{P}$"
+        ],
+        "bbox": [
+            126,
+            136,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "6: end function",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "7: function GENERATIONAGENTS  $(p_{*},\\mathcal{P})$",
+            "8: Initialize empty document collection  $\\mathcal{D}$",
+            "9: for each  $P_{i}$  in  $\\mathcal{P}$  do",
+            "0:  $P_{i}^{\\prime}\\gets \\mathrm{PlanAdjust}(p_{\\mathrm{adjust}_{i}},P_{i})$",
+            "11:  $D_{\\mathrm{initial}_i} \\gets \\mathrm{Generate}(p_{\\mathrm{write}}, P_i')$",
+            "12:  $D_{i}\\gets \\mathrm{LengthRevise}(p_{\\mathrm{length}},D_{\\mathrm{initial}_{i}})$",
+            "13: end for",
+            "14:  $\\mathcal{D}\\gets \\mathcal{D}\\cup D_i$",
+            "15: return  $\\mathcal{D}$",
+            "16: end function"
+        ],
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "4.3 Generation Agents",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Once the global plan  $\\mathcal{P} = \\{P_1, \\dots, P_n\\}$  is finalized by the Planning Agent, multiple Generation Agents take over, each responsible for generating content for a specific description task  $D_i$ . The process begins with validating and refining the local plan  $P_i$ , through monitoring and reviewing similar to the Planning Agent to ensure it aligns with the instruction requirements  $\\mathcal{T}$ . Concretely, if discrepancies are detected, adjustments are applied to update the plan, as shown in the following equation:",
+        "bbox": [
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+        "page_idx": 4
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nP _ {i} ^ {\\prime} \\leftarrow \\operatorname {P l a n A d j u s t} \\left(p _ {\\text {a d j u s t} _ {i}}, P _ {i}\\right), \\tag {3}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "where  $p_{\\mathrm{adjust}_i}$  encompasses the specialized prompt designed for reviewing each local plan  $P_i$  against the residual informing from  $\\mathcal{T}$ .",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Upon validation of  $P_{i}^{\\prime}$ , the agent generates content by executing the plan:",
+        "bbox": [
+            112,
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nD _ {\\text {i n i t i a l} _ {i}} \\leftarrow \\operatorname {G e n e r a t e} \\left(p _ {\\text {w r i t e}}, P _ {i} ^ {\\prime}\\right), \\tag {4}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
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+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "where  $p_{\\mathrm{write}}$  is the prompt to generate content following the guidance of the plan  $\\mathcal{P}_i'$ . Based on our preliminary study, this process generally produces content that meets most instruction criteria. However, length constraints may still require further refinement due to the limitations of most current LLMs in controlling output length precisely. To address this, a revision function adjusts the content to meet the specified length  $L$ :",
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+        "page_idx": 4
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\nD _ {i} \\leftarrow \\operatorname {L e n g t h R e v i s e} \\left(p _ {\\text {l e n g t h}}, D _ {\\text {i n i t i a l} _ {i}}\\right), \\tag {5}\n$$\n",
+        "text_format": "latex",
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+    },
+    {
+        "type": "text",
+        "text": "where  $p_{\\mathrm{length}}$  is the prompt used to adjust the content length to  $L$  by expanding or compressing the generated text while preserving key details, semantic integrity, and overall coherence.",
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+    {
+        "type": "text",
+        "text": "By following this process, each segment  $D_{i}$  seamlessly integrates with the overall narrative structure, ensuring both local coherence and global thematic consistency.",
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+    {
+        "type": "text",
+        "text": "5 Experiments",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "5.1 Experimental Setup",
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+        "text": "Dataset We evaluated CogWriter using Long-GenBench-16K (Wu et al., 2024a), a benchmark specifically designed for assessing a model's complex constrained long-form text generation capabilities. The dataset features four scenarios, each requiring approximately 16,000 tokens: (1) Diary Writing and (2) Menu Design assess temporal consistency by requiring coherent content organization across weeks of a year, while (3) Skyscraper Design and (4) Urban Planning evaluate spatial reasoning through detailed facility arrangements across floors or city blocks. The benchmark includes 400 test instances, with 100 instances per scenario. Each scenario involves three instruction types (defined in Section 3): single instructions, range instructions, and periodic instructions. For temporal tasks, Diary Writing and Menu Design require at least 200 words per weekly entry, totaling 10,400 words (52 weeks  $\\times$  200 words). For spatial tasks, Skyscraper Design and Urban Planning mandate 15,000 words (100 units  $\\times$  150 words).",
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+        "text": "Evaluation Metrics We evaluate model performance using three key metrics from LongGen-Bench. Main Task Completion Rate (Comp. Rate) assesses whether all designated subtasks are completed in sequence (e.g., generating entries for every week in a diary without omissions). Instruction Following Accuracy measures adherence to single (Acc. Once), range (Acc. Range), and periodic (Acc. Periodic) instructions, with their average reported as Avg. Acc. We utilize the official evaluation scripts to ensure consistency with reported benchmarks. Additionally, we track Word Count, ensuring a minimum average threshold of 12,700 words to meet the combined task requirements.",
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+    {
+        "type": "text",
+        "text": "Experimental Setup We evaluate our approach across three categories of models and methods. First, we establish baseline performance using several single-pass generation models from the official",
+        "bbox": [
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+    },
+    {
+        "type": "page_number",
+        "text": "9836",
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+    },
+    {
+        "type": "table",
+        "img_path": "images/4e5cb8032a174dce46157ab9caa876d11da42b1b12a55a901aaf25368a5be97d.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Comp. Rate</td><td>Acc. Once</td><td>Acc. Range</td><td>Acc. Periodic</td><td>Avg. Acc.</td><td>Words (Req. ≥12700)</td></tr><tr><td>LongWriter-Llama3.1-8B</td><td>0.46</td><td>0.36</td><td>0.56</td><td>0.17</td><td>0.36</td><td>11036</td></tr><tr><td>Llama-3.1-8B-Instruct</td><td>0.94</td><td>0.36</td><td>0.49</td><td>0.17</td><td>0.34</td><td>8804</td></tr><tr><td>Llama-3.1-70B-Instruct</td><td>0.79</td><td>0.50</td><td>0.51</td><td>0.18</td><td>0.39</td><td>8055</td></tr><tr><td>Mixtral-8x7B-Instruct-v0.1</td><td>0.83</td><td>0.42</td><td>0.45</td><td>0.24</td><td>0.37</td><td>8113</td></tr><tr><td>Qwen-2-72B-Instruct</td><td>0.94</td><td>0.42</td><td>0.44</td><td>0.14</td><td>0.33</td><td>8013</td></tr><tr><td>GPT-4o-mini</td><td>0.97</td><td>0.54</td><td>0.48</td><td>0.16</td><td>0.39</td><td>8940</td></tr><tr><td>+ SELF-REFINE</td><td>0.84</td><td>0.57</td><td>0.32</td><td>0.20</td><td>0.36</td><td>8154</td></tr><tr><td>+ CoT</td><td>0.93</td><td>0.59</td><td>0.48</td><td>0.18</td><td>0.42</td><td>10137</td></tr><tr><td>+CogWriter (Ours)</td><td>1.00 (↑0.03)</td><td>0.74 (↑0.20)</td><td>0.61 (↑0.13)</td><td>0.31 (↑0.15)</td><td>0.55 (↑0.16)</td><td>12484 (↑3544)</td></tr><tr><td>Qwen-2.5-14B-Instruct</td><td>0.29</td><td>0.53</td><td>0.54</td><td>0.24</td><td>0.44</td><td>1817</td></tr><tr><td>+ SELF-REFINE</td><td>0.17</td><td>0.45</td><td>0.63</td><td>0.21</td><td>0.43</td><td>1122</td></tr><tr><td>+ CoT</td><td>0.30</td><td>0.46</td><td>0.20</td><td>0.16</td><td>0.27</td><td>1619</td></tr><tr><td>+CogWriter (Ours)</td><td>0.79 (↑0.51)</td><td>0.70 (↑0.17)</td><td>0.65 (↑0.11)</td><td>0.47 (↑0.23)</td><td>0.61 (↑0.17)</td><td>10091 (↑8274)</td></tr><tr><td>Llama-3.3-70B-Instruct</td><td>0.99</td><td>0.59</td><td>0.63</td><td>0.21</td><td>0.48</td><td>9431</td></tr><tr><td>+ SELF-REFINE</td><td>0.93</td><td>0.59</td><td>0.64</td><td>0.28</td><td>0.50</td><td>8491</td></tr><tr><td>+ CoT</td><td>1.00</td><td>0.62</td><td>0.62</td><td>0.21</td><td>0.48</td><td>9302</td></tr><tr><td>+CogWriter (Ours)</td><td>1.00 (↑0.01)</td><td>0.76 (↑0.17)</td><td>0.79 (↑0.16)</td><td>0.55 (↑0.34)</td><td>0.70 (↑0.22)</td><td>12051 (↑2620)</td></tr><tr><td>GPT-4o</td><td>0.63</td><td>0.63</td><td>0.60</td><td>0.17</td><td>0.47</td><td>9055</td></tr><tr><td>+ SELF-REFINE</td><td>0.66</td><td>0.67</td><td>0.62</td><td>0.33</td><td>0.54</td><td>4641</td></tr><tr><td>+ CoT</td><td>0.40</td><td>0.58</td><td>0.63</td><td>0.32</td><td>0.51</td><td>4482</td></tr><tr><td>+CogWriter (Ours)</td><td>0.91 (↑0.29)</td><td>0.80 (↑0.17)</td><td>0.76 (↑0.16)</td><td>0.67 (↑0.50)</td><td>0.74 (↑0.27)</td><td>11618 (↑2563)</td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "Table 1: Model Performance Comparison and the Improvement Brought by CogWriter (values in parentheses indicate the improvement relative to the base model).",
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+    {
+        "type": "text",
+        "text": "LongGenBench repository, including LongWriterLlama3.1-8B (Bai et al., 2024), Llama-3.1-8B-Instruct, Mixtral-8x7B-Instruct-v0.1 (Jiang et al., 2023), Llama-3.1-70B (Grattafiori and et al., 2024), Qwen-2-72B-Instruct (Qwen et al., 2025), as well as GPT-4o and GPT-4o-mini. Second, we compare against two prominent enhancement methods: SELF-REFINE (Madaan et al., 2023) and Chain-of-Thought (CoT) prompting (Wei et al., 2022). These methods are applied to four representative foundation models to ensure comprehensive evaluation across different model capabilities and architectures. Finally, to demonstrate the effectiveness of our CogWriter paradigm, we apply it to the same four foundation models: GPT-4o-mini-2024-07-18, GPT-4o-2024-08-06, Qwen-2.5-14B (Team, 2024), and Llama-3.3-70B (Touvron et al., 2024). This selection encompasses closed-source and open-source models with varying parameter scales, enabling us to evaluate CogWriter's generalizability. For fair comparison, we implement SELF-REFINE and CoT baselines on these same models alongside our proposed framework.",
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+    {
+        "type": "text",
+        "text": "Implementation Details We deployed our experiments across local computational resources and cloud-based APIs. For open-source models (Qwen-2.5-14B and Llama-3.3-70B), we leveraged vLLM (Kwon et al., 2023) for its efficient inference",
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "acceleration while maintaining the default temperature and sampling parameters as specified in the official Hugging Face implementations. These experiments were conducted on 4 NVIDIA A100-SXM4-80GB GPUs running CUDA 12.8. For closed-source models (GPT-4o and GPT-4o-mini), we utilized their respective official API.",
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+    {
+        "type": "text",
+        "text": "5.2 Main Results",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Table 1 highlights the main performance outcomes of our experiments. Firstly, our results reveal that LongWriter-Llama3.1-8B, despite being specifically designed and trained from Llama-3.1-8B-Instruct for long-form generation, struggles considerably, achieving only a 0.46 completion rate. Similarly, even advanced models with substantial parameter counts, such as Llama-3.1-70B-Instruct and Qwen-2-72B-Instruct, fail to reach the target length of 12,700 tokens in their generated outputs. Secondly, alternative enhancement methods also exhibit limited effectiveness. Chain-of-Thought prompting results in a modest improvement in instruction-following accuracy (from 0.39 to 0.42 using GPT-4o-mini), while SELF-REFINE achieves reasonable completion rates. However, both approaches fall short in meeting length requirements and maintaining instruction adherence.",
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+    {
+        "type": "text",
+        "text": "In contrast, CogWriter demonstrates remarkable improvements across all evaluation metrics.",
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+    },
+    {
+        "type": "page_number",
+        "text": "9837",
+        "bbox": [
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+    },
+    {
+        "type": "table",
+        "img_path": "images/728e2feffddbd38210d46e893e0323d566e36c41b672b8eff51da328914fadb0.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Model</td><td>Comp. Rate</td><td>Acc. Once</td><td>Acc. Range</td><td>Acc. Periodic</td><td>Avg. Acc.</td><td>Words (Req. ≥12700)</td></tr><tr><td>GPT-4o-mini + CogWriter</td><td>1.00</td><td>0.74</td><td>0.61</td><td>0.31</td><td>0.55</td><td>12484</td></tr><tr><td>- w/o PlanRevise</td><td>0.99</td><td>0.73</td><td>0.45</td><td>0.33</td><td>0.50</td><td>12472</td></tr><tr><td>- w/o PlanAdjust</td><td>1.00</td><td>0.63</td><td>0.46</td><td>0.27</td><td>0.45</td><td>12341</td></tr><tr><td>- w/o LengthReview</td><td>1.00</td><td>0.73</td><td>0.61</td><td>0.30</td><td>0.54</td><td>11549</td></tr></table>",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "When using Qwen-2.5-14B-Instruct as its backbone, it boosts the completion rate by 0.51 and improves average accuracy by 0.17. For Llama3.3-70B-Instruct and GPT-4o, CogWriter achieves near-perfect completion rates while consistently enhancing instruction-following accuracy, excelling at handling complex periodic instructions.",
+        "bbox": [
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+        "page_idx": 6
+    },
+    {
+        "type": "table",
+        "img_path": "images/ff4838dcebecad179d39e56f8d1f34e8469ee7b21893da000118a1dab31fec7e.jpg",
+        "table_caption": [
+            "Table 2: Ablation study on the effectiveness of CogWriter's key components using GPT-4o-mini as the base model."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>Plan</td><td>Decomp.</td><td>Monit.</td><td>Rev.</td></tr><tr><td>Human Writer</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>CoT</td><td>✓</td><td>×</td><td>×</td><td>×</td></tr><tr><td>SELF-REFINE</td><td>×</td><td>×</td><td>×</td><td>✓</td></tr><tr><td>Single-pass LLMs</td><td>×</td><td>×</td><td>×</td><td>×</td></tr><tr><td>CogWriter</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></table>",
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+    },
+    {
+        "type": "text",
+        "text": "Table 3: Comparison of different writing approaches. Plan: planning the writing structure; Decomp.: decomposing complex tasks into manageable components; Monit.: monitoring progress during generation; Rev.: reviewing and refining generated content.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Advantages of Cognitive Structure We provide a comparison of the cognitive capabilities of the baselines, our proposed paradigm, and human writers in Table 3, to analyze the strong performance of our approach. It can be seen that human writers naturally employ all four cognitive processes—planning, decomposition, monitoring, and reviewing—while existing computational methods implement only subsets of these capabilities. CoT primarily focuses on planning, and SELF-REFINE incorporates only reviewing. In contrast,CogWriter mirrors the complete human writing process by integrating all four capabilities, which may help explain its superior effectiveness in complex long-form generation tasks.",
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+    {
+        "type": "text",
+        "text": "Correlation with Model Internal Ability We next discuss the relationship between performance improvements and the model's capabilities. When applying our framework to Llama-3.1-8B-Instruct, we observed a clear limitation: the model struggled to generate coherent and structured plans essential for CogWriter's method. In contrast, for stronger LLMs such as GPT-4o, CogWriter achieved sig",
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+    {
+        "type": "text",
+        "text": "nificant improvements, including a 0.29 increase in completion rate and a 0.50 increase in periodic instruction accuracy. This suggests that models with more advanced internal cognitive abilities are better at utilizing CogWriter's coordination of cognitive processes, while weaker models, lacking robust instruction-following skills, fail to fully replicate this process. This limitation shows that CogWriter's effectiveness depends on the model's internal abilities, with advancing LLMs enabling more human-like reasoning and problem-solving.",
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+    {
+        "type": "text",
+        "text": "6 Discussion",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "Ablation Study We conduct an ablation study to evaluate the impact of different components in our proposed CogWriter framework, as shown in Table 2. Removing the PlanRevise module resulted in a noticeable performance drop across key metrics, with the average accuracy decreasing from 0.55 to 0.50. This demonstrates that refining the initial plan through iterative revisions is crucial for maintaining effective task decomposition and alignment with task-specific constraints. Disabling the PlanAdjust mechanism further impacted performance, reducing the average accuracy to 0.45, particularly affecting Acc. Once and Acc. Range. Finally, removing the LengthReview module led to a drop in content generation quality due to unmet length constraints, highlighting its role in finetuning the output to meet requirements. Overall, the results emphasize the importance of each component, with PlanRevise and PlanAdjust playing key roles in ensuring task decomposition, plan refinement, and overall accuracy of generation.",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Length Control Performance As specified in Section 3, each description  $D_{i}$  must achieve a target word count of  $L$ . To evaluate compliance with this requirement, we conducted an analysis of word count distributions across different models. Taking the Diary Writing task as an example, Figure 3 illustrates the performance of LLama-3.3-70B-Instruct and Qwen-2.5-14B-Instruct. The box plot reveals that these base models struggle to meet",
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+    },
+    {
+        "type": "page_number",
+        "text": "9838",
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+        "page_idx": 6
+    },
+    {
+        "type": "image",
+        "img_path": "images/3d2842cefd7293efa27171ae862a6a60f1efd451546c3d44ac5dfc03081f98fe.jpg",
+        "image_caption": [
+            "Figure 3: Comparison of Length Control Ability."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            157,
+            84,
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "the word count requirement, with high variance and frequent deviations from the target length. In contrast, CogWriter achieves superior length control, as shown by its tighter, more stable distribution of word counts. The explicit monitoring mechanism within CogWriter effectively reduces variance and ensures consistent compliance with the length requirement. We provide further analysis results of other models and tasks in Appendix A.1.",
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+    },
+    {
+        "type": "text",
+        "text": "Challenges in Handling Complex Instructions",
+        "text_level": 1,
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "As shown in Figure 4, our experiments reveal that for all baselines and our model, the average performance follows a consistent ranking: Single Instructions (SI) outperform Range Instructions (RI), while Periodic Instructions (PI) show the lowest success rate. This indicates that, despite task decomposition simplifying the overall process, LLMs still face difficulties in understanding and executing complex instructions. One major issue is instruction overload—as the number of instructions increases, the model's accuracy drops due to the difficulty in managing multiple constraints simultaneously. Additionally, instruction complexity plays a significant role: Single Instructions are easier as they target fixed positions, Range Instructions involve more positional flexibility, and Periodic Instructions require tracking repetitions across intervals, making them the most challenging to execute correctly. To improve performance in real-world application, it is advisable to limit the number of instructions and manually simplify complex or overlapping instructions where possible.",
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+    {
+        "type": "text",
+        "text": "7 Related Work",
+        "text_level": 1,
+        "bbox": [
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+        ],
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+    },
+    {
+        "type": "text",
+        "text": "Long-form Text Generation Recent advances in long-form generation have focused on improving models through architectural enhancements and specialized training techniques (Salemi et al., 2025a; Que et al., 2024; Liu et al., 2023; Li et al., 2023). Approaches like Re3 (Yang et al., 2022)",
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/91123c4efd604d2ff54f40a1b7f38fe4c87573ecf783168bd9ab708efe32d795.jpg",
+        "image_caption": [
+            "Figure 4: Comparison of Instruction Type Performance."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            515,
+            84,
+            877,
+            198
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "use recursive reprompting for extended story generation, while DOC (Yang et al., 2023) and hierarchical outlining (Wang et al., 2024c) improve narrative coherence through structured task decomposition. Personalized long-form generation has also gained attention (Salemi et al., 2025a; Wang et al., 2024a), with methods like LongLaMP (Kumar et al., 2024) and reasoning-enhanced techniques (Salemi et al., 2025b) adapting models to meet user-specific needs. Similarly, long-form question answering focuses on producing detailed responses to complex queries (Dasigi et al., 2021; Stelmakh et al., 2022; Lee et al., 2023; Tan et al., 2024). While these methods have improved generation capabilities (Wu et al., 2024a; Que et al., 2024), our work addresses a critical gap by examining long-form generation through the lens of cognitive writing theory.",
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+    {
+        "type": "text",
+        "text": "Multi-agent Writing Multi-agent writing has made notable progress in recent years (Guo et al., 2024; Liu et al., 2024; Song et al., 2024), showing how agents can collaborate on diverse writing tasks (Wang et al., 2024b; Hong et al., 2024). Research has explored heterogeneous agent integration (Chen et al., 2025a) and educational applications (Shahzad et al., 2024). In academic writing, frameworks like SciAgents (Ghafarollahi and Buehler, 2024) demonstrate collaboration among specialized agents for complex writing tasks (Wang et al., 2024d; D'Arcy et al., 2024; Su et al., 2024), while the Agents' Room approach (Huot et al., 2024) highlights the value of task decomposition in narrative writing. Beyond academic contexts, multi-agent methods have been applied to creative and informational writing, such as Wikipedia-style articles (Shao et al., 2024) and poetry (Zhang and Eger, 2024; Chen et al., 2024b). While these methods focus on collaboration, our work applies cognitive writing principles with agents for planning, monitoring, and revisions, enabling flexible adaptation without task-specific training.",
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+        "text": "9839",
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+        "type": "text",
+        "text": "8 Conclusion and Future Work",
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+        "text": "In this paper, we analyzed the challenges of constrained long-form text generation from a cognitive writing perspective. Building on these insights and empirical observations, we proposed CogWriter, a novel writing framework that transforms LLM constrained long-form text generation into a systematic cognitive paradigm. CogWriter bridges the gap between human writing cognition and LLM capabilities, leading to substantial and consistent improvements in both instruction completion and generation length across different LLMs, as demonstrated through extensive experiments on LongGen-Bench. Looking forward, we plan to optimize agent communication cost and develop specialized models that better align with the unique requirements of each cognitive stage in the writing process.",
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+        "type": "text",
+        "text": "Limitations",
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+        "type": "text",
+        "text": "While demonstrating superior performance, CogWriter exhibits two primary limitations. First, while our approach achieves higher quality output, it necessitates more computational resources. As detailed in Appendix A.2, this additional cost stems from multiple rounds of planning, generation, and reviewing. Second, our current implementation utilizes a single LLM across all cognitive writing stages (planning, generation, and reviewing). This uniform approach may not fully leverage the model's capabilities, as each stage only activates specific aspects of the model's knowledge and abilities. Future research directions include exploring specialized models for different cognitive stages and investigating Mixture-of-Experts architectures to enhance both domain expertise and parameter efficiency in the cognitive writing process.",
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+        "type": "text",
+        "text": "Ethical Considerations",
+        "text_level": 1,
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+        "type": "text",
+        "text": "Like other LLMs, our CogWriter framework may inherit biases from training data. It may generate inaccurate content despite its enhanced control mechanisms, emphasizing the need for human oversight in practical applications. While the multi-step cognitive process increases computational costs, the structured planning approach improves efficiency and could be further optimized for sustainability. As with any advanced text generation system, CogWriter could potentially be misused for generating deceptive content, highlighting the importance of responsible deployment and appropriate safeguards in real-world applications.",
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+    {
+        "type": "text",
+        "text": "References",
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+    {
+        "type": "text",
+        "text": "A Appendix",
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+        "text": "A.1 Further Length Control Performance",
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+        "type": "text",
+        "text": "To comprehensively demonstrate CogWriter's length control capabilities across different scenarios, we present the generated length distribution of LLama-3.3-70B-Instruct, Qwen-2.5-14B-Instruct, GPT-4o, and GPT-4o-mini in Figures 5a-5d. We evaluate two distinct task types: spatial tasks (150 words) and temporal tasks (200 words). Spatial tasks, such as Skyscraper Design and Urban Planning, require detailed facility arrangements across floors or city blocks, with a target length of 150 words per unit. In contrast, temporal tasks, including Diary Writing and Menu Design, emphasize temporal consistency across weeks of a year and require 200 words per weekly entry. Figures 5a and 5c illustrate model performance on spatial tasks, while Figures 5b and 5d present results for temporal tasks, highlighting the models' ability to adhere to different length constraints across varying task structures.",
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+        "type": "text",
+        "text": "A.2 Inference Time and Token Consumption Analysis",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "To evaluate and analyze the computational efficiency of CogWriter, we conducted comprehensive",
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+    {
+        "type": "text",
+        "text": "experiments examining inference time and token consumption amount.",
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+        "type": "text",
+        "text": "Inference Time For ensure reliable evaluation, we used LLaMA-3.3-70B as our test model, as Qwen exhibited incomplete text generation issues and GPT's API calls were subject to network latency variations. All experiments were performed on 4 NVIDIA A100 GPUs, with each condition tested three times to ensure reliable results. The experiments were structured as follows: 1) Single text condition: One randomly sampled writing task and 2) 4-example condition: One randomly sampled example from each of the four tasks. We leveraged vLLM for inference acceleration while maintaining default temperature and sampling parameters from official Hugging Face implementations. To ensure a fair comparison, we only considered outputs achieving  $100\\%$  completion rate. Figure 6 illustrates the inference time comparison between CogWriter and the baseline model across different batch sizes.",
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+    {
+        "type": "text",
+        "text": "Through the implementation of multi-generation agents for parallel processing, our approach demonstrates a significant reduction in generation time, achieving approximately  $50\\%$  faster processing compared to the baseline model.",
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+        "type": "text",
+        "text": "Token Consumption Our analysis reveals that CogWriter consumes approximately 2.8 times more output tokens and 10 times more total tokens compared to baseline methods. The observed increase in token utilization can be attributed to two primary factors:",
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+        "text": "1. While CogWriter ensures comprehensive output generation, baseline models frequently produce responses that are incomplete in quality and length. Notably, baseline models such as GPT-4o often acknowledge their limitations with responses like \"I'm sorry, but creating an entire year's worth of weekly diary entries with detailed narratives is beyond my capabilities in a single response,\" resulting in artificially lower token consumption metrics.",
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+        "text": "2. CogWriter employs an iterative approach involving multiple rounds of plan evaluation against the original prompt, analogous to the human writing process where additional cognitive effort correlates with enhanced document quality and comprehensiveness, thereby increasing token usage.",
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+        "type": "text",
+        "text": "Despite these considerations, it is noteworthy",
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+        "image_caption": [
+            "(a) Llama and Qwen on Spatial Tasks"
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+            "(b) Llama and Qwen on Temporal Tasks"
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+        "img_path": "images/983ddebcfd6863ff2b6a9dac71e43381e27fceee570745cf1d9870eb3079d912.jpg",
+        "image_caption": [
+            "(c) GPT-4o and GPT-4o-mini on Spatial Tasks"
+        ],
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+        "img_path": "images/75e8eebba97cd19c554401cca6209723583002d11a8dcdd75f906a78b60bec77.jpg",
+        "image_caption": [
+            "(d) GPT-4o and GPT-4o-mini on Temporal Tasks"
+        ],
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+        "img_path": "images/fbcfe71b0ad1e5cb7528de484c6db5bb52cbc246334c64ca00f0eb29217819d6.jpg",
+        "image_caption": [
+            "Figure 5: Length Control Performance Across Different Models and Task Types. (a) and (c) show performance on spatial tasks requiring 150 words per unit, while (b) and (d) present results for temporal tasks with 200-word requirements.",
+            "Figure 6: Inference Time Comparison."
+        ],
+        "image_footnote": [],
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+    {
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+        "text": "that while GPT-4o's API pricing is 16.67 times higher than GPT-4o-mini<sup>1</sup>, it achieves only a marginal improvement in Average Accuracy (0.08), as demonstrated in Table 1. In contrast, CogWriter demonstrates a more substantial improvement of 0.16 in Average Accuracy over GPT-4o-mini. Furthermore, our framework can be implemented with lightweight closed-source models such as Qwen2.5-14B-Instruct, enabling local deployment. This capability is particularly valuable for applications prioritizing output quality and data privacy, includ",
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+    {
+        "type": "text",
+        "text": "ing professional content creation, academic writing, and technical documentation.",
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+    {
+        "type": "text",
+        "text": "Our research primarily focuses on transcending the limitations inherent in conventional single-pass generation approaches, aiming to achieve text quality that surpasses the capabilities of individual LLMs, including advanced models like GPT-4o. Much like professional writing practices, where quality content necessitates extended development time and thinking compared to preliminary drafts, CogWriter's increased resource utilization reflects the sophistication of its cognitive processing mechanisms.",
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+    {
+        "type": "text",
+        "text": "While acknowledging the additional computational overhead, we identify several promising directions for future research, including the development of memory optimization techniques and the exploration of specialized writing models with enhanced parameter efficiency for specific cognitive processes in the generation pipeline.",
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+    {
+        "type": "page_footnote",
+        "text": "1https://openai.com/api/pricing/",
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+    },
+    {
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+        "text": "9844",
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+]