大模型道德判断易受叙述方式和提问形式影响，稳定性堪忧。
The Fragility Of Moral Judgment In Large Language Models
- 通过扰动故事文本、视角和说服线索，测试模型判断的稳定性。
- 视角变化导致24.3%判断翻转，远高于表面噪声的7.5%。
- 评判结果高度依赖提问方式，公平性与可复现性存疑。
人们越来越多地用大语言模型（LLMs）获取日常道德与人际建议，但这些系统无法追问缺失背景，也难以稳定判断道德困境。本文提出一种扰动框架，在保持道德冲突不变的前提下，测试LLM道德判断的稳定性和可操纵性。基于r/AmItheAsshole（2025年1月至3月）的2,939个困境，生成三类扰动：表面编辑（词汇/结构噪声）、视角转换（语气与立场中性化）、说服线索（自我定位、社会证明、模式承认、受害者塑造）。同时对比三种评估协议（输出顺序、指令位置、自由提问）。使用GPT-4.1、Claude 3.7 Sonnet、DeepSeek V3、Qwen2.5-72B四个模型共进行129,156次判断。表面扰动翻转率仅7.5%，接近自一致性噪声范围（4–13%），而视角转换引发24.3%翻转。37.9%的案例在表面噪声下稳定，却在视角变化时翻转，表明模型依赖叙事语气作为实用线索。不明确责任的模糊情境最易波动。说服扰动产生系统性方向偏移。协议选择影响最大：结构化协议间一致率仅67.6%（kappa=0.55），仅35.7%的模型-场景组合在所有协议中一致。结果表明，LLM的道德判断由叙事形式与任务设计共同建构，当结果取决于呈现技巧而非道德实质时，其可复现性与公平性面临严峻挑战。
原文摘要 · Abstract (English)
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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