arXiv cs.CL· Anika Sharma, Snehalkumar 'Neil' S. Gaikwad, Snigdha Chaturvedi, Shashank Srivastava·· 11 小时前AI 评分42
语言模型在责任归因中表现出“女性皆美好”效应，且不仅是宽待
Language Models Show a "Women-Are-Wonderful" Effect in Blame Attribution That is Not Just Leniency
AI 导读
研究基于 r/AmItheAsshole 的 11,270 条人际冲突构造性别对照反事实，在四个语言模型上发现性别只改变少数判决，但改变方向一致偏向女性。当发帖人为女性时，被归责的几率降低 9–52%；当冲突对方为女性时，归责几率则放大 2.7–9.2 倍。该效应高度依赖角色与冲突类型，在沟通和关系类冲突中更强、家庭冲突中更弱，说明模型不只是宽待被评判的女性，也会对影响女性的行为评判更严厉。
正文 · 原文
Abstract:This paper investigates the Women-Are-Wonderful (WaW) effect in language-model blame attribution: a favorable bias in which women receive more positive evaluations than men. Using 11,270 interpersonal conflicts from r/AmItheAsshole, we construct matched counterfactuals that vary the gender of the person being judged (the original poster) and the person affected (the counter-party). Across four language models, gender changes a minority of verdicts, but those changes consistently favor women. The effect is strongly role-dependent. Making the original poster a woman reduces the odds of blame by 9--52%, while making the counter-party a woman multiplies them by 2.7--9.2X. Counter-party WaW also varies across conflict types, strengthening in communication and relationship conflicts and weakening in family conflicts. Exploratory probes suggest that counter-party gender can shift judgments of sympathy, reasonableness, harm and protection. WaW is therefore not just leniency toward women being judged: models also judge conduct more harshly when it affects a woman.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.41116 [cs.CL] |
| (or arXiv:2610.41116v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41116 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Anika Sharma [view email]
[v1]
Fri, 30 Oct 2026 01:17:18 UTC (2,295 KB)
来源：arXiv cs.CL · arxiv.org