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.

翻译：自回归大型语言模型（LLMs）经过下一词预测训练，在生成连贯文本方面表现出卓越能力。但它们在形成一致的概率判断方面是否同样熟练？我们利用概率恒等式和重复判断来评估LLMs所作概率判断的一致性。结果表明，这些模型产生的判断往往不一致，呈现出类似人类的系统性偏离概率理论规则的特征。此外，当被要求判断同一事件时，LLMs产生的概率判断中均值-方差关系呈现出与人类相似的倒U形模式。我们提出，这些偏离理性的现象可通过将自回归LLMs与隐式贝叶斯推理相联系，并借鉴人类概率判断的贝叶斯采样器模型来解释。