发表机构
Harvard University; Kempner Institute, Harvard University; Technion - Israel Institute of Technology（哈佛大学; 哈佛大学肯普纳研究所; 以色列理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过认知经济理论评估LLM决策，发现其上下文敏感性在行为上类人，但注意力分配机制与人类不同，规模与推理未改善。
AI 中文摘要
大型语言模型（LLMs）展现出多种类人决策行为，但这些行为反映的是相似的底层机制还是表面模仿仍不清楚。我们评估LLM的上下文敏感性是否与一种认知经济理论一致，该理论通过问题分类和注意力分配来解释人类行为。在12个开源和商业LLM上，使用一个新颖的140,000次试验产品选择基准，上下文诱导了选择与问题分类的类人变化，但并未可靠地重新加权价格与质量等特征之间的注意力。模型规模或思维链推理均未可靠地减弱上下文敏感性或产生类人行为。这些结果表明，LLM的决策机制与人类不同。
英文摘要
Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface-level mimicry remains unclear. We evaluate whether LLM context sensitivity aligns with a cognitive economic theory that explains human behavior through problem categorization and attention allocation. Across 12 open-source and commercial LLMs on a novel 140,000-trial product choice benchmark, context induces human-like shifts in choice and problem categorization, but does not reliably reweight attention between features like price and quality. Neither scale nor chain-of-thought reasoning reliably attenuates context sensitivity or generates human-like behavior. These results suggest that LLM decision mechanisms are distinct from human ones.
CommentsAccepted to EMNLP Findings 2026
