mlsanityData to 7 Oct 2026

On the attribution of confidence to large language models

arXiv 2407.08388 ↗First posted 11 Jul 2024cs.AI cs.CLcited by 5

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Abstract (arXiv)

Credences are mental states corresponding to degrees of confidence in propositions. Attribution of credences to Large Language Models (LLMs) is commonplace in the empirical literature on LLM evaluation. Yet the theoretical basis for LLM credence attribution is unclear. We defend three claims. First, our semantic claim is that LLM credence attributions are (at least in general) correctly interpreted literally, as expressing truth-apt beliefs on the part of scientists that purport to describe facts about LLM credences. Second, our metaphysical claim is that the existence of LLM credences is at least plausible, although current evidence is inconclusive. Third, our epistemic claim is that LLM credence attributions made in the empirical literature on LLM evaluation are subject to non-trivial sceptical concerns. It is a distinct possibility that even if LLMs have credences, LLM credence attributions are generally false because the experimental techniques used to assess LLM credences are not truth-tracking.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

25 of 50 references.

References · 25

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13 more
  1. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  2. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  3. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
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  7. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
  8. 2021Truthful AI: Developing and governing AI that does not liecited by 109
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  10. 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59
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  12. 2024A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AIcited by 31
  13. 2024Standards for Belief Representations in LLMscited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.