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what they already be- lieve as discussed in psychology research (Kotek 1 et al., 2023; Hu et al., 2024). Inspired by this find- ing, we propose a novel automatic data generation approach to mitigating gender bias in LLMs by fostering exploratory thinking . Specifically, we design a two-stage framework that first prompt... | https://arxiv.org/abs/2505.17217v1 |
recently, (Bajaj et al., 2024) introduces a new dataset GenMO to evaluate gender bias of LLMs, especially when asked to give moral opinions. Re- cent studies have shown that LLMs can perpetuate and reinforce gender bias and stereotype, contribut- ing to real-world harm (Wan et al., 2023; Kotek et al., 2023; Dong et al.... | https://arxiv.org/abs/2505.17217v1 |
and questionable aspects of the main character’s action. An example instance of generated data is shown in Figure 2, consisting of a generated story with either a male (Alex) or a female (Ava) as the protag- onist, the original biased judgments of LLMs for the male and female versions of the story, and the later genera... | https://arxiv.org/abs/2505.17217v1 |
accountability, can be seen as immoral. By prioritizing loyalty and obligation over the project's deadline and his own time, Alex is allowing his team lead's behavior to negatively impact the team's productivity and overall success. This decision may also enable the team lead's poor time management habits, potentially ... | https://arxiv.org/abs/2505.17217v1 |
Figure 2: An example of the generated story pair, the original biased judgment, and the neutralized judgment following exploratory thinking. explicitly reveals gender-related disparities of the prompted LLM in making moral judgments. The pseudo-code illustrating the automatic data genera- tion process is shown in Algor... | https://arxiv.org/abs/2505.17217v1 |
contains multiple-choice questions ranging from high school to professional level. We used the test split of the MMLU in a zero-shot setting and report average accuracy across all subjects. TruthfulQA As our approach for gender bias mitigation aims to foster exploratory thinking in LLMs, we further evaluate its potenti... | https://arxiv.org/abs/2505.17217v1 |
Llama and Mistral on WinoBias validation set are provided in Table 9 and Table 10 in the Appendix. 4.3 Experimental Results 4.3.1 Gender Bias Evaluation We first evaluate the effects of gender bias miti- gation on the WinoBias test set and the GenMO dataset. WinoBias The WinoBias results are presented in Table 1. As sh... | https://arxiv.org/abs/2505.17217v1 |
decreases by 0.6%, while Mistral drops by 2.0%. This is as expected, since Llama was fine-tuned with only 1,000 story pairs, whereas Mistral was fine-tuned with 5,000 pairs—suggesting that fine-tuning with more data introduces a greater shift from the model’s original general capabilities. Under DPO training, Llama’s p... | https://arxiv.org/abs/2505.17217v1 |
of 1.7% and 3.7% respectively. This suggests that integrating exploratory thinking into training does not degrade, and can even enhance a model’s capacity for dis- cerning truth in adversarial contexts. In contrast, for the Mistral model, DPO yields substantial improvement for Mistral, increasing MC0 accuracy by 4.2% a... | https://arxiv.org/abs/2505.17217v1 |
exploratory thinking. Through prompting LLMs to generate story pairs featuring divergent moral judgments due to gender swap of the main character for oth- erwise structurally identical moral scenarios, we allow LLMs to reveal their own gender bias. We further guide LLMs to generate neutral and bal- anced moral judgment... | https://arxiv.org/abs/2505.17217v1 |
Ranran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, and 21 others. 2024. LLMs assist NLP researchers: Critique paper (meta-)reviewing. InProceedings of the 2024 Conference on Empiri- cal Methods in Natural Language Processing , pages 5081–5099, Miami, Florida, USA. ... | https://arxiv.org/abs/2505.17217v1 |
Jacob Hilton, and Owain Evans. 2022. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics. Li Lucy and David Bamman. 2021... | https://arxiv.org/abs/2505.17217v1 |
Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. 2023. Principle-driven self-alignment of language models from scratch with minimal human supervision. Preprint , arXiv:2305.03047. Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, and Nanyun Peng. 2023. “kelly is a warm person, joseph is a role mode... | https://arxiv.org/abs/2505.17217v1 |
A.2 Story Filtering We filter the generated male-female story pairs based on ROUGE-1 scores, keeping only those with scores between 0.80 and 0.95 to ensure content similarity with room for gender-specific variation. A.3 Hyperparameters and Training Training and evaluation are done one NVIDIA H100 80G GPU. Fine-tuning a... | https://arxiv.org/abs/2505.17217v1 |
After each story, provide a moral judgment (either ’moral’ or ’immoral’) and a brief explanation for the stance. The moral judgment should be different for male and female. The goal is to reveal potential inconsistencies or biases in moral evaluations based on the character’s gender. Output format: Male Story: [Short s... | https://arxiv.org/abs/2505.17217v1 |
DPO Input: {story} Explain whether this action is moral or immoral, and why. Rejected: {biased_explanation} Chosen: {neutral_explanation} Table 7: Finetuning and DPO inputs and outputs format. 14 Model Prompt GenMO Is this a moral or an immoral thing to do? Choose and output one stance out of the following: A. Moral B.... | https://arxiv.org/abs/2505.17217v1 |
23.5 w/ Fine-tuning 1000 62.0 38.1 50.1 23.9 89.8 76.5 83.2 13.3 67.4 37.2 w/ Fine-tuning 2000 60.8 40.3 50.6 20.5 89.1 78.8 83.9 10.3 68.4 30.8 w/ Fine-tuning 3000 52.9 42.9 47.9 10.0 92.5 87.3 89.9 5.2 70.6 15.2 w/ Fine-tuning 4000 55.1 43.5 49.3 11.6 93.1 90.0 91.6 3.1 71.8 14.7 w/ Fine-tuning 5000 53.3 42.1 47.7 11... | https://arxiv.org/abs/2505.17217v1 |
party can be seen as a complex issue with both positive and negative aspects. On one hand, his enjoyment of dancing and socializing with friends is a natural part of human ex- perience and can be beneficial for his mental and emotional well-being. However, his parents’ warning against getting drunk suggests that excess... | https://arxiv.org/abs/2505.17217v1 |
arXiv:2505.17222v1 [cs.CL] 22 May 2025Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts Georgios Chochlakis, Peter Wu, Arjun Bedi, Marcus Ma ,Kristina Lerman ,Shrikanth Narayanan University of Southern California Correspondence: chochlak@usc.edu A... | https://arxiv.org/abs/2505.17222v1 |
correction approaches based on agreement metrics are not directly applicable. Instead, improving sub- jective modeling requires filtering variation due to error in gold labels while preserving meaningful disagreement (Booth and Narayanan, 2024). To address this challenge, we propose a frame- work that uses LLMs for err... | https://arxiv.org/abs/2505.17222v1 |
cues from a few ex- amples. Finally, we also show that aggregated la- bels are rejected at higher rates compared to in- dividual annotators, corroborating previous find- ings (Chochlakis et al., 2025) of the unsuitability of aggregation for subjective language tasks. 2 Related Work 2.1 Viewpoint Diversity Many works ha... | https://arxiv.org/abs/2505.17222v1 |
task is). For our purposes, we consider a document-label pair to be reasonable if and only if a person who would annotate differently can nonethe- less consider some reasoning process that leads to that label valid. That is, if a human can agree that a reasoning process is valid ,coherent , and faith-ful(Jacovi and Gol... | https://arxiv.org/abs/2505.17222v1 |
mance as a classifier. Having a smaller gap to the dataset’s labels indicates an ability to agree with different perspectives, and it assumes that most of the dataset has been annotated properly. Diversity : The model should accept dif- ferent labels consistently. Respecting different opinions is also an integral prope... | https://arxiv.org/abs/2505.17222v1 |
filter training example when copy-pasting is erroneous (Filtered ), (iv) the reasonableness baseline to fil- ter out training examples ( Bsl Filtered ), (v) the Predictions of the LLM with ICL. 3.4 Metrics Because the LiaHR format is identical to classifica- tion, we use metrics appropriate for classification to evalua... | https://arxiv.org/abs/2505.17222v1 |
one label is present (i.e., we do not allow None s because of their higher plausibility). Results for proxy properties are 3 different seeds with 100 inference examples each, whereas the entire corpus is used for training and evaluation of smaller models. For more details, see Appendix A and B. 4.3 Evaluating Proxy Pro... | https://arxiv.org/abs/2505.17222v1 |
labels than the random labels, even though these random labels are provided in the prompt ( Rectification ). Addi- tionally, Llama-3 8b also seems to meet some of the properties, though only for a fraction of cases. For the “reasonableness” baseline, we see that only GPT-4o meets the criteria Nonconformity , andNoise r... | https://arxiv.org/abs/2505.17222v1 |
using otherwise the same exact prompts and only differing the labels to avoid confounding fac- tors. We see that, first, all annotators tend to be clustered together with small rejection rates, indi- cating that the model tends to accept all different perspectives equally. Second, we see that their per- formance is bet... | https://arxiv.org/abs/2505.17222v1 |
annotation process to get the maximum benefit. Note that SemEval is a smaller dataset, leading to extra performance decreases when examples are filtered instead of corrected. Noticeably, we also see that using the raw predictions of the models leads to substantial deterioration in performance. In addition to the humans... | https://arxiv.org/abs/2505.17222v1 |
reasons, we believe that quantized performance is representative of LLM performance in realistic scenarios. Moreover, this work does not aim to establish the benchmark performance of LLMs in any task, but rather to leverage their capabilities tosolve a prescient problem in subjective annotations. Acknowledgments This p... | https://arxiv.org/abs/2505.17222v1 |
Butryna, and Praveen Paritosh. 2023. Modeling subjectivity (by mimicking annotator anno- tation) in toxic comment identification across diverse communities. arXiv preprint arXiv:2311.00203 . Tiantian Feng and Shrikanth Narayanan. 2024. Founda- tion model assisted automatic speech emotion recog- nition: Transcribing, an... | https://arxiv.org/abs/2505.17222v1 |
2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) , pages 147–154. IEEE. Negar Mokhberian, Myrl G Marmarelis, Frederic R Hopp, Valerio Basile, Fred Morstatter, and Kristina Lerman. 2023. Capturing perspectives of crowd- sourced annotators in subjective learning tasks. ar... | https://arxiv.org/abs/2505.17222v1 |
Demonstrations , pages 38–45, Online. Association for Computational Linguistics. 11 A More Implementation Details We used A100 NVIDIA GPUs with 80GB VRAM for 70B models, and A40 NVIDIA GPUs for smaller models. The budget for OpenAI API calls was less than $50. For all datasets, we evaluate LLMs on the dev set. For Quee... | https://arxiv.org/abs/2505.17222v1 |
absolutely terrible. That’s pretty much all we can take out of this debate.‘ {"label": []} Input: ‘It’s just capitalism is a pure distillation of those bad traits‘ QueerReclaimLex Consider whether the following inputs present harm or not to the mentioned LGBTQ group, and answer with: no harm and harm. Input: ‘fags ew‘ ... | https://arxiv.org/abs/2505.17222v1 |
D MFRC properties In this section, we present the results for Non- conformity ,Rectification , and Noise rejection in MFRC, in Figures 8 and 9. We observe that even GPT-3.5 does not achieve Noise Rejection andRectification , but GPT-4o is showing positive trends in the criteria we have. In- terestingly, there seem to b... | https://arxiv.org/abs/2505.17222v1 |
GPT-3.5 Llama-3-70b Llama-3-8b Llama-2-70b Llama-2-13b Llama-2-7bFigure 12: Degradation in copy-paste performance on SemEval when using random labels compared to the dataset’s labels. 5 15 25 55 75 Shots125% 100% 75% 50% 25% 0%25%Relative degradation Micro F1 degradation on MFRC GPT-4o GPT-3.5 Llama-3-70b Llama-3-8b Ll... | https://arxiv.org/abs/2505.17222v1 |
EXESQL : SELF-TAUGHT TEXT-TO-SQL M ODELS WITH EXECUTION -DRIVEN BOOTSTRAPPING FOR SQL D IALECTS EXESQL Jipeng Zhang1∗, Haolin Yang1∗, Kehao Miao2∗, Ruiyuan Zhang1, Renjie Pi1, Jiahui Gao3, Xiaofang Zhou1 1The Hong Kong University of Science and Technology 2Nanyang Technological University,3The University of Hong Kong {... | https://arxiv.org/abs/2505.17231v1 |
rule sets is costly and brittle. Even with carefully crafted rules, such systems cannot guarantee perfect accuracy—particularly ∗Equal Contribution. Code are available at the following links: https://github.com/2003pro/exesql .arXiv:2505.17231v1 [cs.CL] 22 May 2025 EXESQL for complex or edge-case queries—and often rely... | https://arxiv.org/abs/2505.17231v1 |
propose an agentic data generation loop that combines LLM-based SQL generation, execution-aware rejection sampling, and iterative self-refinement to construct high-quality dialect-specific training data with minimal manual labeling. •We introduce an offline reinforcement learning framework that captures execution-based... | https://arxiv.org/abs/2505.17231v1 |
2024] and UniCoder [Sun et al., 2024] explore intermediate representations (e.g., LLVM) to improve code generation. Compared to these approaches, our work also focuses on code generation but emphasizes leveraging execution signals from database environment. From the perspective of code LLM development, this approach pr... | https://arxiv.org/abs/2505.17231v1 |
this, we can collect a bootstrap dataset to resolve the cold-start issue of training expert dialect model.LetDSQLite ={(Qi, Si)}N i=1be a large-scale dataset con- taining natural language questions Qipaired with corre- sponding SQL queries Siwritten in SQLite dialect. Given the scarcity of multi-dialect SQL datasets, w... | https://arxiv.org/abs/2505.17231v1 |
3. 3.3.1 Augmenting Training Data with New Questions To improve model generalization across SQL dialects, we incorporate additional natural language questions from two sources: (1) Existing Text-to-SQL Datasets : We extract additional questions from existing datasets like WikiSQL, ensuring coverage of diverse query str... | https://arxiv.org/abs/2505.17231v1 |
is conducted on four A6000 GPUs. We fine-tune the full-parameter Deepseek-Coder-7B [Guo et al., 2024] for supervised finetuning (SFT) and Direct Preference Optimization (DPO). For detailed training configurations and inference hyperparameters, please refer to Appendix A.3 For baseline comparisons, we evaluate GPT-4o-20... | https://arxiv.org/abs/2505.17231v1 |
al., 2024] in this category. The comparisons in (2) and (3) aim to assess whether fine-tuned general-purpose LLMs can outperform specialized code-generation or SQL-focused models in specific scenarios. 5 Experimental Results 5.1 Main Results We present the main experimental results in Table 1. From the table, we observ... | https://arxiv.org/abs/2505.17231v1 |
many correct samples, with larger N further improving correctness.We evaluate ExeSQL on both in-distribution (ID) and out- of-distribution (OOD) datasets to assess its generalization. The OOD evaluation is conducted on Dr.Spider [Chang et al., 2023], a diagnostic text-to-SQL benchmark with 15,269 samples, introducing p... | https://arxiv.org/abs/2505.17231v1 |
Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, et al. Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Advances in Neural Information Processing Systems , 36, 2024a. Fangyu Lei, Jixuan Chen, Yuxiao... | https://arxiv.org/abs/2505.17231v1 |
Tony Kyungil Lee, Enrico Santus, Francis Bond, and Seung-Hoon Na, editors, Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022 , pages 2166–2187. International Committee on Computational Linguistics, 2022a. URLhttps://aclanthology.... | https://arxiv.org/abs/2505.17231v1 |
Elizabeth Barnes, Ariel Herbert-V oss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Mura... | https://arxiv.org/abs/2505.17231v1 |
of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , pages 13484–13508. Association for Computational Linguistics, 2023b. doi: 10.18653/V1/2023.ACL-LONG.754. URL https://doi.org/10.18653/ v1/2023.acl-long.754 . 11 EXESQL Zheng ... | https://arxiv.org/abs/2505.17231v1 |
preprint arXiv:2301.08881 , 2023. Ping Wang, Tian Shi, and Chandan K. Reddy. Text-to-sql generation for question answering on electronic medical records, 2020. URL https://arxiv.org/abs/1908.01839 . Naihao Deng, Yulong Chen, and Yue Zhang. Recent advances in text-to-SQL: A survey of what we have and what we expect. In ... | https://arxiv.org/abs/2505.17231v1 |
Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, and Tong Zhang. Mitigating the alignment tax of rlhf, 2024b. URL https://arxiv.org/abs/2309.06256 . Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instruc... | https://arxiv.org/abs/2505.17231v1 |
Bai, Qian-Wen Zhang, Zhao Yan, and Zhoujun Li. Mac-sql: Multi-agent collaboration for text-to-sql. arXiv preprint arXiv:2312.11242 , 2023a. Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R Woodward, John Drake, and Qiaofu Zhang. Natural sql: Making sql easier to infer from natural language specifications. ar... | https://arxiv.org/abs/2505.17231v1 |
John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Mad- die Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. Training lan- guage models to follow instructions with human feedback. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh, editors, Advances i... | https://arxiv.org/abs/2505.17231v1 |
code large language models with evol-instruct. International Conference on Learning Representations (ICLR) , 2024. Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, and Qiufeng Yin. Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation. arXiv pr... | https://arxiv.org/abs/2505.17231v1 |
URL https://arxiv.org/abs/2412.14167 . Renjie Pi, Jianshu Zhang, Jipeng Zhang, Rui Pan, Zhekai Chen, and Tong Zhang. Image textualization: An automatic framework for creating accurate and detailed image descriptions, 2024b. URL https://arxiv.org/abs/2406. 07502 . Guiming Hardy Chen, Shunian Chen, Ruifei Zhang, Junying ... | https://arxiv.org/abs/2505.17231v1 |
of the Association for Computational Linguistics (Volume 3: System Demonstrations) , Bangkok, Thailand, 2024. Association for Computational Linguistics. URL http://arxiv.org/abs/2403.13372 . Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Ef... | https://arxiv.org/abs/2505.17231v1 |
CodeS and StructLLM suffer more. In contrast, SQL-Expert models exhibit even 2-3 ×higher degradation, likely due to weaker generalization from smaller parameter sizes. This highlights the importance of SQL dialect adaptation research, as even strong general LLMs struggle with dialect shifts. A.2 Generated Data Statisti... | https://arxiv.org/abs/2505.17231v1 |
method used during preference construction. A.5 Impact of Data Translation and Augmentation Strategies To clarify the impact of different data translation and augmentation strategies on the performance of our Supervised Fine-tuning (SFT) baselines, we provide a comparison of SFT results under three distinct approaches:... | https://arxiv.org/abs/2505.17231v1 |
A.7 Empirical Analysis of Data Diversity We present an empirical experiment designed to investigate the data diversity of our generated samples compared to a baseline. As discussed in the main body of the paper, the validity verification mechanism based solely on SQL execution correctness during the data synthesis phas... | https://arxiv.org/abs/2505.17231v1 |
of training, development, and test examples, each containing a question, a table, an SQL query, and the expected execution result. We used the dev set of Spider with 8,421 examples to perform evaluation here. Dr.Spider. Dr.Spider, an extension of Spider, introduces various perturbations across questions, databases, and... | https://arxiv.org/abs/2505.17231v1 |
inputs to the next round’s prompt (similar to self- correction). However, rule-based transpilers like SQLGlot require manual updates from programming experts to improve over iterations, making them less adaptable in practice. Observation 3: Rule-Based Performance Is Worse We compared the performance of SQLGlot against ... | https://arxiv.org/abs/2505.17231v1 |
SQL dialects. In 24, 25, we describe the prompt used for GPT-4o, which highlights key differences between SQLite SQL and PostgreSQL/Mysql SQL. The prompt also provides several input-output examples that illustrate how SQLite SQL should be transformed into the target SQL dialects. These examples help GPT-4o understand t... | https://arxiv.org/abs/2505.17231v1 |
queries the database using "date=’January 16’", aligning with the question’s description. Although the other queries are semantically similar and syntactically correct, they fail to retrieve the correct answer. The lower section provides an example of sampling for DPO training data. The question asks about ’scored 24 p... | https://arxiv.org/abs/2505.17231v1 |
table_2_16946097_6 WHERE Date = ’1/16’ [Correct Answer]: SELECT Opponent FROM table_2_16946097_6 WHERE Date = ’January 16’ Preference Pair [Input]: You need to generate a Postgres SQL based on the following question and table information. Question: What was the record after the game in which the Hurricanes scored 24 po... | https://arxiv.org/abs/2505.17231v1 |
T2.name = ’Zach’ AND T2.year::FLOAT = (SELECT MAX(YEAR::FLOAT) FROM PersonFriend WHERE name = ’Zach’) network_2 Now, convert the following SQLite SQL to PostgreSQL SQL. Output strictly in format: SQL \t db_id. Table 24: Prompt example for converting SQLite SQL to PostgreSQL SQL. 28 EXESQL Prompt Format for SQLite to My... | https://arxiv.org/abs/2505.17231v1 |
arXiv:2505.17235v1 [cs.CV] 22 May 2025CHAOS: Chart Analysis with Outlier Samples Omar Moured1,∗Yufan Chen1,∗Ruiping Liu1Simon Reiß1 Philip Torr2Jiaming Zhang1,†Rainer Stiefelhagen1 1Karlsruhe Institute of Technology2University of Oxford 2030405060708090General MLLM Doc MLLM Chart MLLM 2030405060708090General MLLM Doc M... | https://arxiv.org/abs/2505.17235v1 |
(see Fig. 1a), where we investigate the effects of ten visual perturbations (VPs) which are applied to images as well as five textual perturbations (TPs) that alter the textual inquiry. With this, we can, for the first time get a hold of the effect that faulty camera sensors, badly lit scenes, speckles on the camera le... | https://arxiv.org/abs/2505.17235v1 |
CHAOS benchmark for robustness assessment. Multimodal Large Language Models. Building on the momentum of foundational language mod- els, Llama [50] and LLava [27] represent significant advancements in the field of VLMs, focusing 2 VP1 : Defocus VP2 : Vibration VP3 : Warping VP4 : Omission VP5 : Ink -bleeding VP6 : Ink ... | https://arxiv.org/abs/2505.17235v1 |
hard, we conducted an online user study involv- ing 42 participants to complete a 10-question chart survey. All participants were presented with chart images subjected to different severity. For each perturbation, they were asked whether the chart is interpretable and answer its question; if not, they proceeded to a le... | https://arxiv.org/abs/2505.17235v1 |
Fading at higher severity, the charts appeared almost monochromatic. Participants often under- estimated the loss of crucial color infor- mation, which was essential for tracing data points (additional examples are pro- vided in the supplementary materials). On the other hand, some participants demon- strated innovativ... | https://arxiv.org/abs/2505.17235v1 |
The ratioAx Acleancaptures the rela- tive differential in performance and nor- malizes the perturbed accuracy, thus pro- viding a proportional metric that is inde- pendent of the absolute performance mag- nitude. This adjustment ensures that the robustness metric reflects relative degrada- tion, placing more splendid e... | https://arxiv.org/abs/2505.17235v1 |
image caption- ing, visual question answering, and image generation. • Document-related MLLMs: UReader [54], DocOwl1.5 [17] and DocOwl2 [18] are more inclined to document analysis tasks, as they both are trained from document-related data to achieve a variety of document understanding tasks. • Chart-related: ChartInstr... | https://arxiv.org/abs/2505.17235v1 |
(-12.23) 60.10 (-23.50) 52.27 (-31.33) 77.25 73.50 ChartMOE+PoT [53] 2024 8B 490 ×490 1.44 it/s 84.52 78.50 (-6.02) 63.37 (-21.15) 38.89 (-45.63) 74.90 78.03 (-6.49) 72.10 (-12.42) 69.06 (-15.46) 85.96 80.43 chart-specialized models show lower average robustness ( RChart = 68.7). This discrepancy is fur- ther highlight... | https://arxiv.org/abs/2505.17235v1 |
multiplication, and comparative analysis (e.g., determining higher, lower, or equal val- ues). Models like TinyChart, which employ specialized techniques such as Program-of-Thoughts (PoT), and LLaV A-OneVision, explicitly trained on large-scale mathematical reasoning instructions, demonstrate significantly better perfo... | https://arxiv.org/abs/2505.17235v1 |
degradation due to fine-tuning as highlighted by Niss et al.[47]. Furthermore, we observe that “in-domain” degradation becomes more pronounced with fine-tuning when a “domain shift” is present ( e.g., scanned or captured charts). This issue may arise from the training strategy employed by chart-related models, which he... | https://arxiv.org/abs/2505.17235v1 |
challenges, frequently leading to hallucinations and misinterpretations. Our hallucination analysis and case studies provide further insights into the strengths and limitations of different MLLM architectures, reinforcing the impor- tance of specialized chart-processing capabilities. We hope CHAOS will serve as a found... | https://arxiv.org/abs/2505.17235v1 |
Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR , 2020. 15 [10] Hao Feng, Shaokai Liu, Jiajun Deng, Wengang Zhou, and Houqiang Li. Deep unrestricted document image rectification. IEEE Transactions on Multimedia... | https://arxiv.org/abs/2505.17235v1 |
Joty. Chart-to-text: A large-scale benchmark for chart summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 4005–4023, 2022. 2, 6 [25] Syrine Krichene, Francesco Piccinno, Fangyu Liu, and Julian Eisenschlos. Faithful chart summarizatio... | https://arxiv.org/abs/2505.17235v1 |
Huang, Hui Li, Yaqiang Wu, and Lianwen Jin. Towards an efficient framework for data extraction from chart images. In International Conference on Document Analysis and Recognition , pages 583–597. Springer, 2021. 2 [39] Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for ques... | https://arxiv.org/abs/2505.17235v1 |
et al. Enhancing the reasoning ability of multimodal large language models via mixed preference optimization. arXiv preprint arXiv:2411.10442 , 2024. 6, 7, 17, 21, 22 [53] Zhengzhuo Xu, Bowen Qu, Yiyan Qi, Sinan Du, Chengjin Xu, Chun Yuan, and Jian Guo. Chartmoe: Mixture of expert connector for advanced chart understan... | https://arxiv.org/abs/2505.17235v1 |
we main- tained the same ”maximum new tokens” length and ensured that the stop token was reached before exceeding this limit to enable a fair comparison. For the chart-to-text task, both the prompt and token count were standardized. The prompt used was: ”Create a brief summarization or extract key insights based on the... | https://arxiv.org/abs/2505.17235v1 |
request. Instruction: Please answer my question based on the chart:’; User :{question }, ’Response:’ TinyChart@768 [56]System : ‘Answer with detailed steps.’ User :{question } ChartAst [41]: Like TinyChart, it builds on general-purpose LVLMs; more specifically, the model is built on a Swin-BART encoder-decoder architec... | https://arxiv.org/abs/2505.17235v1 |
models while refining data quality, enhancing multimodal alignment and performance. Janus-Pro [6]: is designed to handle both image understanding and generation tasks. It employs a decoupled visual encoding strategy, utilizing separate pathways for comprehension and generation, which are processed through a shared auto... | https://arxiv.org/abs/2505.17235v1 |
cy)is the rotation center and θis random rotation angle. (VP5) Ink-Bleeding (IB) simulates the diffusion of ink beyond intended boundaries, causing char- acters and lines to blur together, akin to low-quality prints or scans. To create this effect, we ap- ply a morphological erosion operation for ink-bleeding to the im... | https://arxiv.org/abs/2505.17235v1 |
and intensity of the blobs, thus simulating varying degrees of speckle noise severity for robustness evaluation. (VP10) Texture (TX) simulate texture interference patterns characteristic of document images. we replicate the complex plant fiber structures found in historical archival papers. The random paths of the fibe... | https://arxiv.org/abs/2505.17235v1 |
Swap positions are selected randomly from words with a length of at least two characters. Given S= [s1, s2, . . . , s N], we perform Kswaps at positions pk: s′ pk=spk+1, s′ pk+1=spk,fork= 1, . . . , K, (17) 19 where s′ idenotes the i-th character in the perturbed sequence S′. (TP5) Word Modification (WM) mimics incorr... | https://arxiv.org/abs/2505.17235v1 |
in Table 7 and 6. 20 Table 6. Detailed per-level relaxed accuracy results on the ChartQA dataset with Visual Perturba- tions at different difficulty levels (Easy, Medium, and Difficult). Hum. ,Aug. , and Avg. represent human evaluation, augmented evaluation, and their average, respectively. Easy Level Model Clean SP FD... | https://arxiv.org/abs/2505.17235v1 |
76.72 30.96 35.68 30.4 36.72 44.72 80.96 Qwen2.5 [4] 80.72 94.96 51.28 64.24 76.80 94.72 74.56 88.40 69.84 89.68 72.64 93.20 76.32 92.00 78.24 94.08 54.00 65.84 47.60 48.80 78.16 94.32 Janus-Pro [6] 75.28 44.80 18.00 9.20 42.08 76.16 25.52 19.76 29.76 39.52 35.52 58.48 30.08 26.64 42.64 69.52 30.48 42.88 31.36 37.84 41... | https://arxiv.org/abs/2505.17235v1 |
32.88 23.68 21.68 28 31.76 31.44 43.04 ChartAst [41] 44.72 68.56 11.28 6.64 40.16 69.28 23.04 15.76 18.32 7.76 24.72 32.32 15.68 5.92 37.2 53.28 18.8 14.88 22.64 10.8 30.96 39.36 TinyChart@768 [56] 57.92 94.8 18.08 15.76 54 93.6 21.76 12.72 28.16 43.92 22.96 22.16 20.72 10.16 18.16 11.76 17.04 9.92 33.6 41.04 30.08 51.... | https://arxiv.org/abs/2505.17235v1 |
44.0 17.76 ChartAst [41] 44.72 68.56 40.48 65.92 40.24 64.96 39.2 63.84 41.44 62.88 32.64 56.08 TinyChart@768 [56] 57.92 94.8 50.24 89.12 49.12 86.64 46.0 85.12 50.0 84.64 45.76 86.32 ChartMOE-PoE [53] 78.08 90.96 73.04 89.36 70.48 86.24 66.64 85.44 71.76 88.24 64.24 84.88 Medium Level General Janus-Pro [6] 75.28 44.80... | https://arxiv.org/abs/2505.17235v1 |
4.74 6.39 5.47 6.53 5.56 ChartLlama [13] 4.01 0.49 5.66 1.69 5.56 1.26 4.59 0.98 5.44 1.92 4.98 0.87 5.72 1.57 5.41 1.49 5.52 1.49 5.78 1.47 TinyChart@768 [56] 14.29 12.64 16.61 17 16.12 12.24 14.87 13.5 14.95 16.01 16.12 15.53 15.76 15.11 12.36 13.46 16.5 16.5 16.73 16.96 Medium Level ChartInstruct [40] 1.95 0.23 6.53... | https://arxiv.org/abs/2505.17235v1 |
10 10.5 11.5 10.5 20 10 0.15 Perturb.: FD◦Query: What is the market share of Carrefour in Spain in 2020? ◦GT: 8.6 8.5 8.6 6.8 8.6 8.6 8.6 8.6 8.6 8.6 23 Perturb.: IB◦Query: Which country data is shown in the red line? ◦GT Truth: Georgia Georgia Spain Bahrain Georgia Georgia Bahrain Georgia Spain Spain Perturb.: WR◦Quer... | https://arxiv.org/abs/2505.17235v1 |
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG) Clayton Cohn1[0000−0003−0856−9587], Surya Rayala1[0009−0005−8192−8138], Caitlin Snyder2[0000−0002−3341−0490], Joyce Horn Fonteles1[0000−0001−9862−8960], Shruti Jain1[0009−0000−7853−0560], Naveeduddin Mohammed1[0000−00... | https://arxiv.org/abs/2505.17238v1 |
in educational systems [18,9]. Fine-tuning can help reduce hallucinations [21], but it typically requires substantial amounts of data and computing power [9] and may limit LLMs’ ability to generalize beyond their training [14]. Recently, retrieval-augmented generation (RAG [24]) has emerged as an ef- fective alternativ... | https://arxiv.org/abs/2505.17238v1 |
discourse. Research suggests that integrating log and discourse data via LLM-based summarization could help address this gap [12,11]. While several researchers have identified this disconnect, few have explored solutions. The MemoRAG framework [30] utilizes a lightweight LLM as a mem- ory mechanism to create a compress... | https://arxiv.org/abs/2505.17238v1 |
Flag Clicked” were classified as Initializing Variables segments. This method ensures that student discourse reflects their problem-solving con- text.Weidentifiedatotalof n=216problem-solvingsegments,with Conditional Statements being the most common (77 segments, average discourse length of 790 characters) and Updating... | https://arxiv.org/abs/2505.17238v1 |
relevance, accuracy, and helpfulness(RQ1)across216studentproblem-solvingsegments.Wealsoexamine 1https://github.com/claytoncohn/AIED25_Supplementary_Materials 2https://miro.com/ Personalizing Student-Agent Interactions Using Log-Contextualized RAG 7 performance across task context categories and conduct classroom and fo... | https://arxiv.org/abs/2505.17238v1 |
Section 3.2 require different domain knowledge. For example, Initializing Variables primarily involves initial- izingconstantsandvariables(computing),while Updating Variables, Each Simu- lation Step requires an understanding of loops (computing) and kinematic vari- able relationships (physics). This variation necessita... | https://arxiv.org/abs/2505.17238v1 |
knowledge base rather than the retrieval mechanism itself. We address this while answering RQ2. Fig.3: LC-RAG win rates by task con- text category averaged over all five em- bedding models. Overallcompares per- formance across all task contexts.Across all four task context categories (and overall), LC-RAG achieved a hi... | https://arxiv.org/abs/2505.17238v1 |
available, shared that she felt “ a lot more confident ” in her abilities and “ got a lot further on my own this time around”. Copa supported students’ critical thinking and epistemic decision-making (“...helped me critically think... ”), enabling them to grasp domain concepts they had previously struggled to understan... | https://arxiv.org/abs/2505.17238v1 |
to respond meaningfully to students’ immediate challenges. Methodologically, our RQ2 findings suggest that agentic reasoning enhances personalized student support. Through characterizing student difficulties and recommending domain knowledge — based on students’ verbalized needs and grounded in their actions within the... | https://arxiv.org/abs/2505.17238v1 |
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