mlsanityData to 14 Oct 2026

Are LLM Belief Updates Consistent with Bayes' Theorem?

arXiv 2507.17951 ↗First posted 23 Jul 2025cs.CL cs.AIcited by 9

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Abstract (arXiv)

Do larger and more capable language models learn to update their "beliefs" about propositions more consistently with Bayes' theorem when presented with evidence in-context? To test this, we formulate a Bayesian Coherence Coefficient (BCC) metric and generate a dataset with which to measure the BCC. We measure BCC for multiple pre-trained-only language models across five model families, comparing against the number of model parameters, the amount of training data, and model scores on common benchmarks. Our results provide evidence for our hypothesis that larger and more capable pre-trained language models assign credences that are more coherent with Bayes' theorem. These results have important implications for our understanding and governance of LLMs.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 23 Jul 2025.

Builds on

17 of 23 references.

References · 17

  1. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  2. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
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5 more
  1. 2023Evaluating Superhuman Models with Consistency Checkscited by 47
  2. 2024Consistency Checks for Language Model Forecasterscited by 27
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  4. 2025Constrained belief updates explain geometric structures in transformer representationscited by 15
  5. 2025Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systemscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 8.

9 papers.