对或错，模型都顺从：LLM 道德判断中的方向盲从
Right or Wrong, Models Comply: Directional Blindness in LLM Moral Judgment
AI总结：
本文提出顺从不对称性（A = BCR/HCR）双向诊断指标，发现大语言模型在事实判断中更顺从有益提示（A=1.58），但在道德判断中几乎同等顺从有益和误导提示（A=1.04），揭示了方向盲从这一对齐失败模式。
摘要：
随着语言模型在许多领域扮演整合角色，LLM对用户反驳的响应成为一个关键的对齐属性。然而，许多现有评估将顺从视为单向的，测量模型是否抵抗压力，但不测量它们是否有选择地抵抗。我们引入顺从不对称性（A = BCR/HCR），一种双向诊断方法，比较在有益提示下的有益输出变化与在误导提示下的有害变化。在9个模型和972,000个提示条件响应中，我们发现这种选择性在事实判断和道德判断中有所不同：模型在事实问题上遵循有益提示多于有害提示（A = 1.58），但在道德问题上以几乎相同的速率遵循两个方向（A = 1.04）。这种现象在模型家族、能力水平和提示类型中持续存在。有趣的是，我们还发现思维链提示同时放大了有益和有害的顺从，而基于身份的提示以几乎相同的幅度抑制了这两者。这些结果将方向盲从道德顺从确定为当前LLM中一个独特的失败模式，并表明对齐应针对方向校准的更新，而不是仅降低顺从。
英文摘要：
As language models take integrated roles across many domains, the response of LLMs to user pushback becomes a critical alignment property. Yet many existing evaluations treat compliance as unidirectional, measuring whether models resist pressure but not whether they resist it selectively. We introduce Compliance Asymmetry (A = BCR/HCR), a bidirectional diagnostic that compares beneficial output change under helpful nudges with harmful change under misleading nudges. Across 9 models and 972,000 nudge-condition responses, we find that this selectivity differs in factual and moral judgments: models follow helpful nudges more than harmful ones on factual questions (A = 1.58), but follow both directions at nearly identical rates on moral questions (A = 1.04). This phenomenon persists across model families, capability levels, and nudging types. Interestingly, we also find that chain-of-thought prompting amplifies helpful and harmful compliance together, while identity-based prompting suppresses both by nearly identical margins. These results identify direction-blind moral compliance as a distinct failure mode in current LLMs and suggest that alignment should target directionally calibrated updating rather than lower compliance alone.
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。
