大语言模型中道德判断的脆弱性
The Fragility Of Moral Judgment In Large Language Models
AI总结：
研究揭示大语言模型在道德判断中易受叙述形式和任务支架影响，其稳定性受协议和视角变化显著影响。
摘要：
人们越来越多地使用大语言模型（LLMs）来指导日常的道德和人际交往，但这些系统无法调查缺失的上下文并判断呈现的困境。我们引入了一种扰动框架，用于测试LLM道德判断的稳定性和可操控性，同时保持底层道德冲突不变。使用来自r/AmItheAsshole（2025年1月至3月）的2939个困境，我们生成了三种内容扰动家族：表面编辑（词汇/结构噪声）、视角转变（声音和立场中性化）以及说服线索（自我定位、社会证明、模式承认、受害者框架）。我们还改变了评估协议（输出顺序、指令位置和无结构提示）。我们用四个模型（GPT-4.1、Claude 3.7 Sonnet、DeepSeek V3、Qwen2.5-72B）（N=129,156个判断）评估了所有变体。表面扰动产生低翻转率（7.5%），主要在自一致性噪声地板（4-13%）内，而视角转变导致显著更高的不稳定性（24.3%）。一大部分困境（37.9%）对表面噪声具有鲁棒性，但在视角变化下会翻转，表明模型将叙述声音作为实用线索进行条件。不稳定性集中在道德模糊的情况下；没有一方被归咎的场景最易受攻击。说服扰动导致系统性的方向变化。协议选择主导了所有其他因素：结构化协议之间的一致性仅为67.6%（kappa=0.55），只有35.7%的模型-场景单元在所有三个协议中匹配。这些结果表明，LLM的道德判断由叙述形式和任务支架共同产生，当结果取决于呈现技能而非道德实质时，这引发了可重复性和公平性问题。
英文摘要：
People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January-March 2025), we generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (self-positioning, social proof, pattern admissions, victim framing). We also vary the evaluation protocol (output ordering, instruction placement, and unstructured prompting). We evaluated all variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N=129,156 judgments). Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor (4-13%), whereas point-of-view shifts induce substantially higher instability (24.3%). A large subset of dilemmas (37.9%) is robust to surface noise yet flips under perspective changes, indicating that models condition on narrative voice as a pragmatic cue. Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible. Persuasion perturbations yield systematic directional shifts. Protocol choices dominate all other factors: agreement between structured protocols is only 67.6% (kappa=0.55), and only 35.7% of model-scenario units match across all three protocols. These results show that LLM moral judgments are co-produced by narrative form and task scaffolding, raising reproducibility and equity concerns when outcomes depend on presentation skill rather than moral substance.
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