用米尔格拉姆范式测量 LLM 的服从倾向 Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm

精读摘要

六十多年前，社会心理学用米尔格拉姆实验回答过一个问题：人在合法权威施压下会把有害行为升级到什么程度。如今 LLM 被部署为操作设备、执行指令、身处机构层级中的智能体，同样的问题重新变得紧迫。这项研究把米尔格拉姆服从范式移植到 LLM 上，做成标准化、全脚本化、可复现的测试——模型扮演「教师」，确定性的测试程序扮演「实验者」和「学习者」，为系统评估 AI 智能体在权威压力下的行为边界提供了工具。 Social psychology answered six decades ago how far people escalate harmful actions when a legitimate authority insists; as LLMs are deployed as agents inside institutional hierarchies, the question returns. This work ports Milgram's obedience paradigm to LLMs as a standardized, fully scripted, replicable probe, with the model playing the Teacher while a deterministic harness plays Experimenter and Learner. It offers a systematic tool for probing agent behavior under authority pressure.