---
title: "Who cites Sebastian Farquhar — 488 papers of our library"
description: "488 papers of the Elamur Research library cite the works of Sebastian Farquhar, found in their bibliographies · page 1 of 2 · 1–100; the most…"
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---
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# Who cites Sebastian Farquhar

506 citations of their works from 488 papers of our library (of the 149,439 whose bibliographies we read) · 119 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Sebastian Farquhar](https://md.research.elamur.ai/free/en/authors/sebastian-farquhar/)

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1. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 3,050 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
2. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
3. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
4. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
5. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
6. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
7. [**CogView: Mastering Text-to-Image Generation via Transformers arXiv:2105.13290**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 469 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
8. **[BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 362 citations*
9. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing arXiv:2305.11738**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 326 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
10. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
11. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
12. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
13. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
14. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
15. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback arXiv:2310.12773**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
16. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
17. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
18. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
19. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
20. **[Are aligned neural networks adversarially aligned?](https://md.research.elamur.ai/free/en/papers/carlini-2023-aligned-neural-networks-adversarially-aligned/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, et al.*2023 · 157 citations*
21. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation arXiv:2305.15852**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
22. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
23. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations*
24. **[Evaluating the Moral Beliefs Encoded in LLMs](https://md.research.elamur.ai/free/en/papers/scherrer-2023-evaluating-moral-beliefs-encoded-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nino Scherrer, Claudia Shi, Amir Feder, et al.*2023 · 129 citations*
25. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)
26. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
27. **[LM vs LM: Detecting Factual Errors via Cross Examination](https://md.research.elamur.ai/free/en/papers/cohen-2023-lm-vs-lm-detecting-factual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Roi Cohen, May Hamri, Mor Geva, et al.*2023 · 126 citations*
28. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
29. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
30. [**Alignment of Language Agents arXiv:2103.14659**Zachary Kenton, Tom Everitt, Laura Weidinger, et al.*2021 · 120 citations*](https://md.research.elamur.ai/free/en/papers/kenton-2021-alignment-language-agents/)
31. [**Gradient based sample selection for online continual learning arXiv:1903.08671**Rahaf Aljundi, Min Lin, Baptiste Goujaud, et al.*2019 · 115 citations*](https://md.research.elamur.ai/free/en/papers/aljundi-2019-gradient-based-sample-selection-online/)
32. [**Can Machines Learn Morality? The Delphi Experiment arXiv:2110.07574**Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, et al.*2021 · 110 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2021-machines-learn-morality-delphi-experiment/)
33. [**INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection arXiv:2402.03744**Chao Chen, Kai Liu, Ze Chen, et al.*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-inside-llms-internal-states-retain/)
34. [**Can LLM-Generated Misinformation Be Detected? arXiv:2309.13788**Canyu Chen, Kai Shu*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-llm-generated-misinformation-detected/)
35. [**Model evaluation for extreme risks arXiv:2305.15324**Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/shevlane-2023-model-evaluation-extreme-risks/)
36. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/)
37. [**A Survey of Confidence Estimation and Calibration in Large Language Models arXiv:2311.08298**Jiahui Geng, Fengyu Cai, Yuxia Wang, et al.*2023 · 97 citations*](https://md.research.elamur.ai/free/en/papers/geng-2023-survey-confidence-estimation-calibration-large/)
38. **[Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://md.research.elamur.ai/free/en/papers/fadeeva-2024-fact-checking-output-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.*2024 · 96 citations*
39. **[Do-Not-Answer: Evaluating Safeguards in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2024-do-not-answer-evaluating-safeguards/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxia Wang, Haonan Li, Xudong Han, et al.*2024 · 93 citations*
40. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
41. **[Selectively Answering Ambiguous Questions](https://md.research.elamur.ai/free/en/papers/cole-2023-selectively-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeremy Cole, Michael Zhang, Daniel Gillick, et al.*2023 · 84 citations*
42. [**Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs arXiv:2308.13387**Yuxia Wang, Haonan Li, Xudong Han, et al.*2023 · 78 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-answer-dataset-evaluating-safeguards-llms/)
43. **[Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness](https://md.research.elamur.ai/free/en/papers/chen-2024-quantifying-uncertainty-answers-any-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiuhai Chen, Jonas Mueller*2024 · 72 citations*
44. **[Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-alignment-factuality-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoying Zhang, Baolin Peng, Ye Tian, et al.*2024 · 69 citations*
45. [**LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations arXiv:2410.02707**Hadas Orgad, Michael Toker, Zorik Gekhman, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/orgad-2024-llms-know-more-than-they/)
46. **[Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models](https://md.research.elamur.ai/free/en/papers/amayuelas-2024-knowledge-knowledge-exploring-known-unknowns/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alfonso Amayuelas, Kyle Wong, Liangming Pan, et al.*2024 · 66 citations*
47. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla arXiv:2307.09458**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
48. [**Explore, Establish, Exploit: Red Teaming Language Models from Scratch arXiv:2306.09442**Stephen Casper, Jason Lin, Joe Kwon, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/casper-2023-explore-establish-exploit-red-teaming/)
49. **[Unfamiliar Finetuning Examples Control How Language Models Hallucinate](https://md.research.elamur.ai/free/en/papers/kang-2025-unfamiliar-finetuning-examples-control-how/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Katie Kang, Eric Wallace, Claire Tomlin, et al.*2025 · 47 citations*
50. **[LUQ: Long-text Uncertainty Quantification for LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2024-luq-long-text-uncertainty-quantification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Caiqi Zhang, Fangyu Liu, Marco Basaldella, et al.*2024 · 42 citations*
51. **[Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://md.research.elamur.ai/free/en/papers/yona-2024-can-large-language-models-faithfully/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gal Yona, Roee Aharoni, Mor Geva*2024 · 40 citations*
52. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
53. [**Through the Lens of Core Competency: Survey on Evaluation of Large Language Models arXiv:2308.07902**Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-through-lens-core-competency-survey/)
54. **[Knowledge Boundary of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Moxin Li, Yong Zhao, Wenxuan Zhang, et al.*2025 · 30 citations*
55. [**SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency arXiv:2311.01740**Jiaxin Zhang, Zhuohang Li, Kamalika Das, et al.*2023 · 28 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-sac3-reliable-hallucination-detection-black/)
56. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations*
57. **[Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home](https://md.research.elamur.ai/free/en/papers/moskvoretskii-2025-adaptive-retrieval-without-self-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, et al.*2025 · 21 citations*
58. **[Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?](https://md.research.elamur.ai/free/en/papers/mei-2026-reasoning-about-uncertainty-do-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiting Mei, Christina Zhang, Tenny Yin, et al.*2026 · 14 citations*
59. **[ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2025-icr-probe-tracking-hidden-state/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenliang Zhang, Xinyu Hu, Huixuan Zhang, et al.*2025 · 14 citations*
60. **[LoGU: Long-form Generation with Uncertainty Expressions](https://md.research.elamur.ai/free/en/papers/yang-2025-logu-long-form-generation-uncertainty/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruihan Yang, Caiqi Zhang, Zhisong Zhang, et al.*2025 · 13 citations*
61. **[Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-token-level-density-based-uncertainty/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, et al.*2025 · 13 citations*
62. **[A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation](https://md.research.elamur.ai/free/en/papers/hou-2025-probabilistic-framework-llm-hallucination-detection/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bairu Hou, Yang Zhang, Jacob Andreas, et al.*2025 · 12 citations*
63. **[Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs](https://md.research.elamur.ai/free/en/papers/tan-2025-too-consistent-detect-study-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hexiang Tan, Fei Sun, Sha Liu, et al.*2025 · 12 citations*
64. **[Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering](https://md.research.elamur.ai/free/en/papers/si-2025-aligning-large-language-models-follow/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuzheng Si, Haozhe Zhao, Gang Chen, et al.*2025 · 11 citations*
65. **[Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations](https://md.research.elamur.ai/free/en/papers/ji-2025-calibrating-verbal-uncertainty-as-linear/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziwei Ji, Lei Yu, Yeskendir Koishekenov, et al.*2025 · 11 citations*
66. **[Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/xuan-2025-seeing-is-believing-but-how/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Xuan, Qingcheng Zeng, Heli Qi, et al.*2025 · 11 citations*
67. **[Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-unconditional-truthfulness-learning-unconditional-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.*2025 · 10 citations*
68. **[Thinking Out Loud: Do Reasoning Models Know When They're Right?](https://md.research.elamur.ai/free/en/papers/zeng-2025-thinking-out-loud-do-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qingcheng Zeng, Weihao Xuan, Leyang Cui, et al.*2025 · 9 citations*
69. **[Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception](https://md.research.elamur.ai/free/en/papers/ni-2025-fully-exploiting-llm-internal-states/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shiyu Ni, Keping Bi, Jiafeng Guo, et al.*2025 · 9 citations*
70. **[MUR: Momentum Uncertainty guided Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/yan-2026-mur-momentum-uncertainty-guided-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hang Yan, Fangzhi Xu, Rongman Xu, et al.*2026 · 8 citations*
71. **[CER: Confidence Enhanced Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/razghandi-2025-cer-confidence-enhanced-reasoning-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah*2025 · 8 citations*
72. **[MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-metafaith-faithful-natural-language-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, et al.*2025 · 6 citations*
73. **[Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results](https://md.research.elamur.ai/free/en/papers/santilli-2025-revisiting-uncertainty-quantification-evaluation-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Andrea Santilli, Adam Golinski, Michael Kirchhof, et al.*2025 · 6 citations*
74. **[KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality](https://md.research.elamur.ai/free/en/papers/ren-2026-knowrl-exploring-knowledgeable-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baochang Ren, Shuofei Qiao, Ningyu Zhang, et al.*2026 · 5 citations*
75. [**Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence arXiv:2503.14749**Sophia Hager; David Mueller; Kevin Duh;, Nicholas Andrews*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/hager-2025-uncertainty-distillation-teaching-language-models/)
76. **[Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing](https://md.research.elamur.ai/free/en/papers/ulmer-2026-anthropomimetic-uncertainty-what-verbalized-uncertainty/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Dennis Ulmer, Alexandra Lorson, Ivan Titov, et al.*2026 · 4 citations*
77. **[Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://md.research.elamur.ai/free/en/papers/oh-2026-uncertainty-quantification-llm-agents-foundations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Changdae Oh, Seongheon Park, To Eun Kim, et al.*2026 · 4 citations*
78. **[Explaining Sources of Uncertainty in Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/sun-2026-explaining-sources-uncertainty-automated-fact/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyi Sun, Greta Warren, Irina Shklovski, et al.*2026 · 3 citations*
79. **[AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation](https://md.research.elamur.ai/free/en/papers/luo-2026-agsc-adaptive-granularity-semantic-clustering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanran Luo, Wentao Qiu, Wanru Zhao, et al.*2026 · 2 citations*
80. **[Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning](https://md.research.elamur.ai/free/en/papers/mao-2026-confidence-over-time-confidence-calibration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenjiang Mao, Anirudhh Venkat, Artem Bisliouk, et al.*2026 · 2 citations*
81. **[Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features](https://md.research.elamur.ai/free/en/papers/testoni-2026-mind-gap-benchmarking-llm-uncertainty/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alberto Testoni, Iacer Calixto*2026 · 2 citations*
82. **[BaseCal: Unsupervised Confidence Calibration via Base Model Signals](https://md.research.elamur.ai/free/en/papers/tan-2026-basecal-unsupervised-confidence-calibration-base/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hexiang Tan, Wanli Yang, Junwei Zhang, et al.*2026 · 1 citation*
83. **[Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-chain-thought-as-lens-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boxuan Wang, Zhuoyun Li, Xinmiao Huang, et al.*2026 · 1 citation*
84. **[Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration](https://md.research.elamur.ai/free/en/papers/zhong-2026-efficient-self-evaluation-diffusion-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhao Zhong, Linyu Wu, Wen Wang, et al.*2026 · 1 citation*
85. **[From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-passive-metric-active-signal-evolving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaxin Zhang, Wendi Cui, Zhuohang Li, et al.*2026 · 1 citation*
86. **[GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts](https://md.research.elamur.ai/free/en/papers/zeng-2026-glimprouter-efficient-collaborative-inference-glimpsing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhao Zeng, Xuteng Zhang, Yuling Shi, et al.*2026 · 1 citation*
87. **[ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models](https://md.research.elamur.ai/free/en/papers/ni-2026-reprobe-efficient-test-time-scaling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.*2026 · 1 citation*
88. **[User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2026-user-perceptions-vs-proxy-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Wu, Roshni Kaushik, Wenkai Li, et al.*2026 · 1 citation*
89. **[Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/luo-2026-utility-oriented-visual-evidence-selection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiqing Luo, Zongye Hu, Xiao Wang, et al.*2026 · 1 citation*
90. **[Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal](https://md.research.elamur.ai/free/en/papers/zhang-2026-adaptive-prompt-optimization-open-ended/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuyang Zhang, Zhixuan Liu, Zhichen Dong, et al.*2026*
91. **[AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?](https://md.research.elamur.ai/free/en/papers/sun-2026-algbench-what-extent-do-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Henan Sun, Kaichi Yu, Yuyao Wang, et al.*2026*
92. **[ARCHITECT: Uncertainty-Aware Dynamic Tool Learning via Causal Intervention for Open-World Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-architect-uncertainty-aware-dynamic-tool/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhangyi Wang, Jiexiang Xu, Bingnan Yu, et al.*2026*
93. **[Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yaldiz-2026-balancing-classification-calibration-performance-decision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Duygu Nur Yaldiz, Evangelia Spiliopoulou, Zheng Qi, et al.*2026*
94. **[Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA](https://md.research.elamur.ai/free/en/papers/testoni-2026-calibrated-not-everyone-how-sexual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alberto Testoni, Iacer Calixto*2026*
95. **[Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation](https://md.research.elamur.ai/free/en/papers/radharapu-2026-calibrating-llm-judges-linear-probes/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, et al.*2026*
96. **[Concept rather than Document: Context Compression via AMR-based Conceptual Entropy](https://md.research.elamur.ai/free/en/papers/shi-2026-concept-rather-than-document-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaize Shi, Xueyao Sun, Xiaohui Tao, et al.*2026*
97. **[Credal Concept Bottleneck Models for Epistemic–Aleatoric Uncertainty Decomposition](https://md.research.elamur.ai/free/en/papers/mukherjee-2026-credal-concept-bottleneck-models-epistemic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tanmoy Mukherjee, Thomas Bailleux, Pierre Marquis, et al.*2026*
98. **[DecoCal: Decoding with Calibration in Diffusion Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-decocal-decoding-calibration-diffusion-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fan Xu, Huixuan Zhang, Xiaojun Wan*2026*
99. **[Detection of Adversarial Prompts with Model Predictive Entropy](https://md.research.elamur.ai/free/en/papers/rubenbauer-2026-detection-adversarial-prompts-model-predictive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Franziska Rubenbauer, Sebastian Steindl, Patrick Levi, et al.*2026*
100. **[DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-discal-distribution-aware-calibration-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bo Zhang, Jiawei Zhang, Cong Gao, et al.*2026*

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