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Cited by 11Natural Language Processing TechniquesTopic ModelingArtificial Intelligence in Law

Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation

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Lee, Dongryeol, Hwang, Yerin, Kim, Yongil, Park, Joonsuk, Jung, Kyomin·2025·Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
DOI 10.18653/v1/2025.naacl-long.452
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Dongryeol Lee, Yerin Hwang, Yongil Kim, Joonsuk Park, Kyomin Jung. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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Research works printed but not linked: 57

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. · arXiv
  2. Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021. A general language assistant as a laboratory for align- ment. arXiv preprint arXiv:2112.00861. · arXiv
  3. Austin S Babrow, Chris R Kasch, and Leigh A Ford. 1998. The many meanings of uncertainty in illness: Toward a systematic accounting. Health communica- tion, 10(1):1-23.
  4. Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, and Salman Avestimehr. 2024. Mars: Meaning- aware response scoring for uncertainty estimation in generative llms. arXiv preprint arXiv:2402.11756. · arXiv
  5. Anya Belz, Craig Thomson, Ehud Reiter, Gavin Aber- crombie, Jose M Alonso-Moral, Mohammad Ar- van, Anouck Braggaar, Mark Cieliebak, Elizabeth Clark, Kees van Deemter, et al. 2023. Missing in- formation, unresponsive authors, experimental flaws: The impossibility of assessing the reproducibility of previous human evaluations in nlp. arXiv preprint arXiv:2305.01633. · arXiv
  6. Nitzan Bitton-Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, and Roy Schwartz. 2023. Breaking common sense: Whoops! a vision-and-language benchmark of syn- thetic and compositional images. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision, pages 2616-2627.
  7. Dale E Brashers, Judith L Neidig, Stephen M Haas, Linda K Dobbs, Linda W Cardillo, and Jane A Rus- sell. 2000. Communication in the management of uncertainty: The case of persons living with hiv or aids. Communications Monographs, 67(1):63-84.
  8. Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A sur- vey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3):1-45.
  9. Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, and Lichao Sun. 2024a. Mllm- as-a-judge: Assessing multimodal llm-as-a-judge with vision-language benchmark. arXiv preprint arXiv:2402.04788. · arXiv
  10. Guiming Hardy Chen, Shunian Chen, Ziche Liu, Feng Jiang, and Benyou Wang. 2024b. Humans or llms as the judge? a study on judgement biases. arXiv preprint arXiv:2402.10669. · arXiv
  11. Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological mea- surement, 20(1):37-46.
  12. Shehzaad Dhuliawala, Vilém Zouhar, Mennatallah El- Assady, and Mrinmaya Sachan. 2023. A diachronic perspective on user trust in ai under uncertainty. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5567-5580.
  13. Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2023. Shifting attention to relevance: To- wards the uncertainty estimation of large language models. arXiv preprint arXiv:2307.01379. · arXiv
  14. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783. · arXiv
  15. Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy S Liang, and Tatsunori B Hashimoto. 2024. Alpacafarm: A simulation framework for methods that learn from human feedback. Advances in Neural Information Processing Systems, 36.
  16. Owain Evans, Owen Cotton-Barratt, Lukas Finnve- den, Adam Bales, Avital Balwit, Peter Wills, Luca Righetti, and William Saunders. 2021. Truthful ai: Developing and governing ai that does not lie. arXiv preprint arXiv:2110.06674. · arXiv
  17. Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. 2024. Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 13418- 13427.
  18. Ken Hyland. 2005. Stance and engagement: A model of interaction in academic discourse. Discourse studies, 7(2):173-192.
  19. Ken Hyland. 2014. Disciplinary discourses: Writer stance in research articles. In Writing: Texts, pro- cesses and practices, pages 99-121. Routledge.
  20. Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. 2023. Llm-blender: Ensembling large language models with pairwise ranking and generative fusion. In Pro- ceedings of the 61st Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers), pages 14165-14178.
  21. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehen- sion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 1601-1611.
  22. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221. · arXiv
  23. Jean Kaddour, Joshua Harris, Maximilian Mozes, Her- bie Bradley, Roberta Raileanu, and Robert McHardy. 2023. Challenges and applications of large language models. arXiv preprint arXiv:2307.10169. · arXiv
  24. Ehsan Kamalloo, Nouha Dziri, Charles Clarke, and Davood Rafiei. 2023. Evaluating open-domain ques- tion answering in the era of large language models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5591-5606.
  25. Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, and Kyomin Jung. 2024a. Life- tox: Unveiling implicit toxicity in life advice. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 688-698.
  26. Minbeom Kim, Hwanhee Lee, Joonsuk Park, Hwaran Lee, and Kyomin Jung. 2024b. Advisorqa: Towards helpful and harmless advice-seeking question an- swering with collective intelligence. arXiv preprint arXiv:2404.11826. · arXiv
  27. Ryan Koo, Minhwa Lee, Vipul Raheja, Jong Inn Park, Zae Myung Kim, and Dongyeop Kang. 2023. Bench- marking cognitive biases in large language models as evaluators. arXiv preprint arXiv:2309.17012. · arXiv
  28. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Red- field, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Ken- ton Lee, et al. 2019. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453- 466.
  29. George Lakoff. 1973. Hedges: A study in meaning criteria and the logic of fuzzy concepts. Journal of philosophical logic, 2(4):458-508.
  30. J Richard Landis and Gary G Koch. 1977. An appli- cation of hierarchical kappa-type statistics in the as- sessment of majority agreement among multiple ob- servers. Biometrics, pages 363-374.
  31. Dongryeol Lee, Minwoo Lee, Kyungmin Min, Joonsuk Park, and Kyomin Jung. 2025. Return of EM: Entity- driven answer set expansion for QA evaluation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 11218-11234, Abu Dhabi, UAE. Association for Computational Linguis- tics.
  32. Kang-il Lee, Minbeom Kim, Seunghyun Yoon, Minsung Kim, Dongryeol Lee, Hyukhun Koh, and Kyomin Jung. 2024. Vlind-bench: Measuring language pri- ors in large vision-language models. arXiv preprint arXiv:2406.08702. · arXiv
  33. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334. · arXiv
  34. Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2023. Generating with confidence: Uncertainty quantifi- cation for black-box large language models. arXiv preprint arXiv:2305.19187. · arXiv
  35. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024. Visual instruction tuning. Advances in neural information processing systems, 36.
  36. Kevin Liu, Stephen Casper, Dylan Hadfield-Menell, and Jacob Andreas. 2023a. Cognitive dissonance: Why do language model outputs disagree with internal rep- resentations of truthfulness? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4791-4797.
  37. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b. G-eval: Nlg evaluation using gpt-4 with better human align- ment. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511-2522.
  38. Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li. 2023c. Trust- worthy llms: a survey and guideline for evaluating large language models' alignment. arXiv preprint arXiv:2308.05374. · arXiv
  39. Adian Liusie, Potsawee Manakul, and Mark Gales. 2024. Llm comparative assessment: Zero-shot nlg evalua- tion through pairwise comparisons using large lan- guage models. In Proceedings of the 18th Confer- ence of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 139-151.
  40. Mary L McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276-282.
  41. Kyungmin Min, Minbeom Kim, Kang-il Lee, Don- gryeol Lee, and Kyomin Jung. 2024. Mitigat- ing hallucinations in large vision-language mod- els via summary-guided decoding. arXiv preprint arXiv:2410.13321. OpenAI. 2023. Gpt-3.5 · arXiv
  42. Nicholas Pangakis, Samuel Wolken, and Neil Fasching. 2023. Automated annotation with generative ai re- quires validation. arXiv preprint arXiv:2306.00176. · arXiv
  43. Vyas Raina, Adian Liusie, and Mark Gales. 2024. Is llm-as-a-judge robust? investigating universal adver- sarial attacks on zero-shot llm assessment. arXiv preprint arXiv:2402.14016. · arXiv
  44. Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally, Sankaran Vaidyanathan, and Dieuwke Hupkes. 2024. Judging the judges: Evaluating align- ment and vulnerabilities in llms-as-judges. arXiv preprint arXiv:2406.12624. · arXiv
  45. Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023. Just ask for cali- bration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5433-5442.
  46. Cunxiang Wang, Sirui Cheng, Qipeng Guo, Yuanhao Yue, Bowen Ding, Zhikun Xu, Yidong Wang, Xi- angkun Hu, Zheng Zhang, and Yue Zhang. 2024. Evaluating open-qa evaluation. Advances in Neural Information Processing Systems, 36.
  47. Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023a. Large language models are not fair evaluators. arXiv preprint arXiv:2305.17926. · arXiv
  48. Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xing- shan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023b. Aligning large lan- guage models with human: A survey. arXiv preprint arXiv:2307.12966. · arXiv
  49. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits rea- soning in large language models. Advances in neural information processing systems, 35:24824-24837.
  50. Minghao Wu and Alham Fikri Aji. 2023. Style over sub- stance: Evaluation biases for large language models. arXiv preprint arXiv:2307.03025. · arXiv
  51. Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv preprint arXiv:2306.13063. · arXiv
  52. Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neu- big, and Pengfei Liu. 2023. Alignment for honesty. arXiv preprint arXiv:2312.07000. · arXiv
  53. Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, and Danqi Chen. 2023. Evaluating large language models at evaluating instruction following. arXiv preprint arXiv:2310.07641. · arXiv
  54. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36:46595-46623.
  55. Kaitlyn Zhou, Jena D Hwang, Xiang Ren, and Maarten Sap. 2024. Relying on the unreliable: The impact of language models' reluctance to express uncertainty. arXiv preprint arXiv:2401.06730. · arXiv
  56. Kankan Zhou, Eason Lai, Wei Bin Au Yeong, Kyriakos Mouratidis, and Jing Jiang. 2023. Rome: Evaluat- ing pre-trained vision-language models on reason- ing beyond visual common sense. arXiv preprint arXiv:2310.19301. · arXiv
  57. Lianghui Zhu, Xinggang Wang, and Xinlong Wang. 2023. Judgelm: Fine-tuned large language models are scalable judges. arXiv preprint arXiv:2310.17631. · arXiv

Cited by

Sorted by citations

Authors
Lee, D., Hwang, Y., Kim, Y., Park, J., & Jung, K.
Affiliations
Seoul National University (KR)
ORCID
Jung, Kyomin
Venue
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2025
Pages
8962-8984
DOI
10.18653/v1/2025.naacl-long.452
OpenAlex
W4411119343
Cited by
11
References
1
Citations by year
2026: 4 · 2025: 7
FWCI
7.73
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • Seoul National University
  • Ministry of Science and ICT, South Korea (2021-II212068, RS-2021-II212068)
  • Institute for Information and Communications Technology Promotion (RS-2022-II220184, 2022-, RS‐2021‐II212068)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
Keywords
LLM-based evaluation, epistemic markers, uncertainty expression, robustness, large language models, automated assessment

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