---
title: "Who cites Semantic Uncertainty: Linguistic — 381 citing papers"
description: "381 papers of the Elamur Research library cite «Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language…"
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---
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# Who cites «Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation»

381 papers of our library cite it (of the 149,439 whose bibliographies we read) · 88 with a page here, the most cited first · [← back to «Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation»](https://md.research.elamur.ai/free/en/papers/kuhn-2023-semantic-uncertainty-linguistic-invariances-uncertainty/)

1. [**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/)
2. [**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/)
3. **[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*
4. [**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/)
5. **[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*
6. **[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*
7. [**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/)
8. **[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*
9. [**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/)
10. [**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/)
11. **[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*
12. [**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/)
13. **[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*
14. **[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*
15. **[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*
16. [**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/)
17. **[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*
18. [**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/)
19. **[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*
20. **[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*
21. **[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*
22. [**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/)
23. **[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*
24. [**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/)
25. **[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*
26. **[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*
27. **[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*
28. **[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*
29. **[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*
30. **[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*
31. **[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*
32. **[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*
33. **[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*
34. **[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*
35. **[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*
36. **[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*
37. **[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*
38. **[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*
39. **[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*
40. **[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*
41. **[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*
42. **[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*
43. **[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*
44. [**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/)
45. **[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*
46. **[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*
47. **[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*
48. **[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*
49. **[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*
50. **[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*
51. **[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*
52. **[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*
53. **[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*
54. **[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*
55. **[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*
56. **[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*
57. **[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*
58. **[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*
59. **[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*
60. **[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*
61. **[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*
62. **[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*
63. **[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*
64. **[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*
65. **[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*
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