| Challenge: | Existing methods for quantifying uncertainty in large language models do not account for the effects of the semantics of sampled answers. |
| Approach: | They propose to incorporate the semantics of sampled answers to estimate entropy by incorporating the semantic of sample answers into the estimation methods. |
| Outcome: | The proposed method significantly improves uncertainty quantification performance. |
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Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) generate long one-sentence responses that are less effective because they overlook two crucial factors: intra-cluster similarity and inter-c cluster similarity. |
| Approach: | They propose a method that generalizes semantic entropy and uses token probabilities to quantify uncertainty in large language models. |
| Outcome: | The proposed method can be extended to white-box settings by incorporating token probabilities. |
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty (2025.findings-naacl)
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| Challenge: | despite advances in large language models, they still produce false but incorrect responses. |
| Approach: | They propose a new benchmark for large language models that requires more than two unambiguous answers . they also assess 5 different uncertainty quantification methods in the presence of data uncertainty. |
| Outcome: | The proposed method fails in multi-answer question answering tasks compared to single-answered questions . entropy- and consistency-based methods effectively estimate model uncertainty, the authors show . |
Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for uncertainty quantification in large language models rely on indirect signals, such as entropy across sampled generations, which can be difficult to interpret and do not fully leverage the model’s ability to assess its own uncertainty. |
| Approach: | They propose a method that groups sampled generations into semantically distinct clusters and uses the probability assigned by the LLM to each option as a confidence estimate. |
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Uncertainty Quantification for Large Language Models (2025.acl-tutorials)
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| Challenge: | Large language models (LLMs) produce hallucinations, which undermine user trust and reliability. |
| Approach: | This tutorial offers the first systematic introduction to uncertainty quantification (UQ) for LLMs in text generation tasks. |
| Outcome: | The proposed framework provides tools for communicating the reliability of a model answer. |
Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models (2026.eacl-short)
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| Challenge: | Large language models have limited truthfulness and tendency toward overconfidence constrain reliability in factual tasks. |
| Approach: | They propose an efficient method that leverages semantic information encoded in LLMs to quantify uncertainty. |
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Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)
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| Challenge: | Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment. |
| Approach: | They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error. |
| Outcome: | The proposed method provides theoretical guarantees and empirical gains for reliability. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
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Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models (2024.acl-long)
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Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
| Challenge: | Large Language Models (LLMs) show promising results in language generation but often “hallucinate”, making their outputs less reliable. |
| Approach: | They propose to shift attention to more relevant components at token- and sentence-levels for better UQ. |
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Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification (2025.emnlp-main)
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| Challenge: | Prior work on calibration and uncertainty quantification focuses on individual models, overlooking the potential of model diversity. |
| Approach: | They propose a method that uses Jensen-Shannon Divergence to identify and aggregate well-calibrated subsets of large language models (LLMs) to improve calibration. |
| Outcome: | The proposed method improves accuracy on binary prediction tasks compared to single-model and naive ensemble baselines. |
Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) exhibit excessive, random, and uninformative uncertainty rendering them unsuitable for decision-making in human-computer interactions. |
| Approach: | They propose an uncertainty-aware instruction tuning method that aligns LLMs’ perception with the probabilistic uncertainty of the generation. |
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