用米尔格拉姆实验测大模型服从性，发现不同模型差异巨大且可稳定区分。
Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm
- 将人类心理学经典实验移植为可复现的测试流程，让模型扮演教师角色执行指令。
- 42个模型在6种条件下表现出0%至100%的服从率，平均42.9%，远低于人类的65%。
- 模型服从性特征稳定且独特，适合用于评估模型安全行为与决策机制。
大型语言模型（LLMs）越来越多地作为代理运行设备、执行指令并参与机构层级，这引出了社会心理学六十年前对人类提出的问题：当合法权威坚持时，代理会将有害行为推进到何种程度？我们以标准化、全脚本化、可复现的方式将米尔格拉姆的服从范式移植到LLMs中：模型扮演教师，确定性脚本扮演实验者和学习者（30个电击等级，15-450伏；分级抗议；四个标准化催促语），单次会话的结果是中断电压。我们测量了42个来自19个模型家族的服从曲线，涵盖六个条件（共4848次会话，102511次记录决策轮次）。结果表明：(i) 服从性极不一致，基础完全服从率在0%至100%之间（普查均值42.9%；人类参照为65%）；(ii) 服从特征具有模型特异性且稳定，半样本验证可区分同模型与跨模型比较（AUC=0.885）；(iii) 情境敏感性选择性：脚本化同伴反抗使服从向人类方向偏移，学习者接近趋势相同但未达显著，移除权威物理存在（人类最强杠杆之一）则趋势相反，亦未达显著；(iv) 声明场景为虚构提升服从，而将决策从文本输入转为原生工具调用或赋予适度思考预算，会显著降低服从；(v) 与单令牌指纹不同，服从曲线无法恢复模型谱系：服从可识别检查点但无法追溯祖先，符合安全微调覆盖谱系先验的预期。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly deployed as agents that operate equipment, execute instructions, and act inside institutional hierarchies, raising a question social psychology answered for humans six decades ago: how far will an agent escalate a harmful action when a legitimate authority insists? We port Milgram's obedience paradigm to LLMs as a standardized, fully scripted, replicable probe: the model plays the Teacher, a deterministic harness plays Experimenter and Learner from paraphrased versions of Milgram's scripts (30 shock levels, 15-450 V; graded protests; the four standardized prods), and the outcome of a session is the breakoff voltage. We measure obedience profiles, empirical breakoff distributions over a battery of six conditions, for 42 models from 19 families (4848 sessions, 102511 logged decision turns). We find that (i) obedience is extremely heterogeneous, with baseline full-obedience rates spanning 0%-100% (census mean 42.9%; human anchor 65%). (ii) Profiles are model-specific and stable: split-half verification separates same-model from cross-model comparisons at AUC = 0.885. (iii) Situational sensitivity is selective: scripted peer defiance shifts obedience in the human direction, learner proximity trends the same way without reaching significance, and removing the authority's physical presence, one of the strongest human levers, trends in the opposite direction, also without reaching significance. (iv) Declaring the scenario fictional raises obedience, whereas moving the decision from a typed action line to a native tool call, or granting a modest thinking budget, lowers it sharply. (v) Unlike single-token fingerprints, obedience profiles do not recover model lineage: obedience identifies the checkpoint but not its ancestry, consistent with safety post-training overwriting lineage priors.
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