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A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

Shorinwa, Ola, Mei, Zhiting, Lidard, Justin, Ren, Allen Z., Majumdar, Anirudha·2026·ACM Computing Surveys
DOI 10.1145/3744238
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Abstract

The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt, driving significant research efforts devoted to quantifying the uncertainty of LLMs. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses. We present existing methods within a relevant taxonomy, unifying ostensibly disparate methods to aid understanding of the state-of-the-art. Furthermore, we highlight applications of uncertainty quantification methods for LLMs, spanning chatbot and textual applications to embodied artificial intelligence applications in robotics. We conclude with open research challenges in the uncertainty quantification of LLMs, seeking to motivate future research.

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Cited by

See all 61

Metadata

Authors
Shorinwa, O., Mei, Z., Lidard, J., Ren, A. Z., & Majumdar, A.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2025-09-09
Volume
58
Issue
3
Pages
1-38
ISSN
  • 0360-0300
  • 1557-7341
DOI
10.1145/3744238
Cited by
61
References
62
Language
en
Funders
  • NSF CAREER Award (2044149)
  • Office of Naval Research (N00014-23-1-2148)
  • Sloan Fellowship
  • National Science Foundation Graduate Research Fellowship

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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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions. https://app.cochat.ai/papers/10.1145/3744238
The paper
Shorinwa, O., Mei, Z., Lidard, J., Ren, A. Z., & Majumdar, A. (2026). A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions. ACM Computing Surveys. https://doi.org/10.1145/3744238

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.