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Model Evaluation

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Friday, 28 August 2026

LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs’ Probabilistic Beliefs

Apple Machine Learning Research 1 month ago 16 ● 2 sources

The article proposes treating large language models as information processing rules and measuring how far their belief updates deviate from Bayes updates. It introduces the “information processing gap” as the quantitative way to track these internal probabilistic (in)consistencies. As a result, the work frames LLM uncertainty behavior in terms of measurable Bayes-update errors rather than assuming consistent Bayesian reasoning.

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