cs.AI updates on arXiv.org 08月13日
Prospect Theory Fails for LLMs: Revealing Instability of Decision-Making under Epistemic Uncertainty
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本文通过经济问卷实验,探讨预测理论在大型语言模型决策中的应用及其与不确定性表达(如‘可能’)的关系,发现模型决策与语言表达的不确定性形式相关。

arXiv:2508.08992v1 Announce Type: new Abstract: Prospect Theory (PT) models human decision-making under uncertainty, while epistemic markers (e.g., maybe) serve to express uncertainty in language. However, it remains largely unexplored whether Prospect Theory applies to contemporary Large Language Models and whether epistemic markers, which express human uncertainty, affect their decision-making behaviour. To address these research gaps, we design a three-stage experiment based on economic questionnaires. We propose a more general and precise evaluation framework to model LLMs' decision-making behaviour under PT, introducing uncertainty through the empirical probability values associated with commonly used epistemic markers in comparable contexts. We then incorporate epistemic markers into the evaluation framework based on their corresponding probability values to examine their influence on LLM decision-making behaviours. Our findings suggest that modelling LLMs' decision-making with PT is not consistently reliable, particularly when uncertainty is expressed in diverse linguistic forms. Our code is released in https://github.com/HKUST-KnowComp/MarPT.

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预测理论 大型语言模型 决策行为 不确定性表达
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