框定博弈：情境如何塑造大语言模型的决策
Framing the Game: How Context Shapes LLM Decision-Making
AI总结：
本文提出一个程序化生成多样化情境的评估框架，揭示大语言模型在相同博弈结构下的决策受情境框定显著影响，并呼吁采用动态、情境感知的评估方法。
摘要：
大语言模型（LLMs）正越来越多地被部署于多样化的情境中以支持决策。尽管现有评估能够有效探测模型的潜在能力，但它们往往忽视了情境框定对感知理性决策的影响。在本研究中，我们提出了一个新的评估框架，该框架在关键特征上系统地变化评估实例，并通过程序化方式生成情境短文，以创建高度多样化的场景。通过分析在相同底层博弈结构下不同情境中的决策模式，我们揭示了大语言模型响应中显著的情境变异性。我们的研究结果表明，这种变异性在很大程度上是可预测的，但对框定效应高度敏感。我们的结果强调了在真实世界部署中需要动态的、情境感知的评估方法。
英文摘要：
Large Language Models (LLMs) are increasingly deployed across diverse contexts to support decision-making. While existing evaluations effectively probe latent model capabilities, they often overlook the impact of context framing on perceived rational decision-making. In this study, we introduce a novel evaluation framework that systematically varies evaluation instances across key features and procedurally generates vignettes to create highly varied scenarios. By analyzing decision-making patterns across different contexts with the same underlying game structure, we uncover significant contextual variability in LLM responses. Our findings demonstrate that this variability is largely predictable yet highly sensitive to framing effects. Our results underscore the need for dynamic, context-aware evaluation methodologies for real-world deployments.
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。
