Incoherent Probability Judgments in Large Language Models
Jian-Qiao Zhu Thomas L. Griffiths
Autoregressive Large Language Models (LLMs) trained for next-word prediction
have demonstrated remarkable proficiency at producing coherent text. But are
they equally adept at forming coherent probability judgments? We use
probabilistic identities and repeated judgments to assess the coherence of
probability judgments made by LLMs. Our results show that the judgments
produced by these models are often incoherent, displaying human-like systematic
deviations from the rules of probability theory. Moreover, when prompted to
judge the same event, the mean-variance relationship of probability judgments
produced by LLMs shows an inverted-U-shaped like that seen in humans. We
propose that these deviations from rationality can be explained by linking
autoregressive LLMs to implicit Bayesian inference and drawing parallels with
the Bayesian Sampler model of human probability judgments.