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Cited by 53Adversarial Robustness in Machine LearningTopic ModelingNatural Language Processing Techniques

Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey

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Liu, Xiaoou, Chen, Tiejin, Da, Longchao, Chen, Chacha, Lin, Zhen, Wei, Hua·2025·Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
DOI 10.1145/3711896.3736569
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Abstract

Uncertainty quantification (UQ) enhances the reliability of Large Language Models (LLMs) by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding inconsistencies. Moreover, LLMs introduce unique uncertainty sources, such as input ambiguity, reasoning path divergence, and decoding stochasticity, that extend beyond classical aleatoric and epistemic uncertainty. To address this, we introduce a new taxonomy that categorizes UQ methods based on computational efficiency and uncertainty dimensions, including input, reasoning, parameter, and prediction uncertainty. We evaluate existing techniques, summarize existing benchmarks and metrics for UQ, assess their real-world applicability, and identify open challenges, emphasizing the need for scalable, interpretable, and robust UQ approaches to enhance LLM reliability.

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  • The introduction of uncertainty quantification and confidence calibration techniques enables models to provide confidence scores or intervals alongside their outputs, allowing clinicians to make risk-stratified, human-in-the-loop decisions.

    “Second, the introduction of UQ and confidence calibration techniques enables models to provide confidence scores or intervals alongside their outputs, allowing clinicians to make risk-stratified, human-in-the-loop decisions [143].”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [143]

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Cited by

Sorted by citations

Authors
Liu, X., Chen, T., Da, L., Chen, C., Lin, Z., & Wei, H.
Affiliations
  • Arizona State University (US)
  • University of Chicago (US)
  • University of Illinois Urbana-Champaign (US)
ORCID
Venue
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Publisher
ACM
Published
2025-08-03
Pages
6107-6117
DOI
10.1145/3711896.3736569
OpenAlex
W4412875472
Cited by
53
References
19
Citations by year
2026: 45 · 2025: 8
FWCI
50.53
Type
conference paper
Language
en
Open access
Locations
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Science Foundation (2421839)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
uncertainty quantification, large language models, confidence calibration, aleatoric uncertainty, epistemic uncertainty, uncertainty taxonomy, uncertainty measures, LLM reliability

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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