当代理人言辞与行为不一致时：从LLMs中验证提取的信念
When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs
AI总结：
本文提出一种决策理论框架，通过提取概率判断和决策并检验其一致性，验证LLMs的信念是否合理，发现最强模型的不一致程度较小。
摘要：
大型语言模型（LLMs）越来越多地应用于高风险领域，其中良好的决策需要对未知结果的概率形成信念。然而，不清楚LLMs在决策时是否像持有一致信念一样运作，或者如何验证模型对这些信念的报告。我们提出一个决策理论框架，从代理中提取概率判断和决策，并测试它们的一致性。形式上，我们的方法确定是否可以由一个“近理性”的决策者产生动作，该决策者持有提取的概率作为真实信念。我们证明，令人惊讶的是，这种形式化即使在不假设代理效用函数的情况下，也隐含了可经验检验的条件。将我们的框架应用于简化临床诊断任务，我们发现模型报告的信念是其决策中揭示信息的不完美总结，但最强模型的不一致程度较小。
英文摘要：
Large language models (LLMs) are increasingly deployed in high-stakes settings where good decisions require forming beliefs over the probability of unknown outcomes. However, it is unclear whether LLMs act as if they hold coherent beliefs when making decisions, or if so, how we could validate models' reports of such beliefs. We propose a decision-theoretic framework that elicits both probability judgments and decisions from an agent and tests their mutual consistency. Formally, our methods characterize whether it is possible for the actions to be produced by a ``near-rational" decision maker who holds the elicited probability as their true belief. We show that, perhaps surprisingly, this formalization implies empirically testable conditions even without any assumption about the agent's utility function. Applying our framework to stylized clinical diagnosis tasks, we find that models' reported beliefs are demonstrably imperfect summaries of the information revealed in their decisions, but that the discrepancies are small for the strongest models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
