Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

[2504.18346] Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review Skip to main content Search Submit Donate Log in Computer Science > Computation and Language arXiv:2504.18346 (cs) [Submitted on 25 Apr 2025 ( v1 ), last revised 24 Aug 2026 (this version, v4)] Title: Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review Authors: Toghrul Abbasli , Kentaroh Toyoda , Yuan Wang , Leon Witt , Muhammad Asif Ali , Yukai Miao , Dan Li , Qingsong Wei View PDF HTML (experimental) Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect information, remains one of the leading challenges for LLMs. This raises the question of how to accurately assess and quantify the uncertainty of LLMs. Extensive literature on traditional models has explored Uncertainty Quantification (UQ) to measure uncertainty and employed calibration techniques to address the misalignment between uncertainty and accuracy. While some of these methods have been adapted for LLMs, the literature lacks an in-depth analysis of their effectiveness and does not offer a comprehensive benchmark to enable insightful comparison among existing solutions. In this work, we fill this gap via a systematic survey of representative prior works on UQ and calibration for LLMs and introduce a rigorous benchmark. Using three widely used reliability datasets, we empirically evaluate seven related methods, which justify the significant findings of our review. Finally, we provide outlooks for key future directions and outline open challenges. To the best of our knowledge, this survey is one of the first dedicated studies to review the calibration methods and relevant metrics for LLMs. Comments: Accepted to IEEE Transactions on Neural Networks and Learning Systems Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2504.18346 [cs.CL] (or arXiv:2504.18346v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2504.18346 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Toghrul Abbasli [ view email ] [v1] Fri, 25 Apr 2025 13:34:40 UTC (2,005 KB) [v2] Fri, 26 Sep 2025 10:08:32 UTC (332 KB) [v3] Wed, 18 Mar 2026 06:24:40 UTC (183 KB) [v4] Mon, 24 Aug 2026 18:05:26 UTC (280 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL < prev | next > new | recent | 2025-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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