用论证理论让大模型揭示隐含性别歧视的逻辑链条
Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts
- 以论证理论设计提示词，挖掘话语中的隐性性别偏见
- 模型多依赖刻板印象而非逻辑推理生成假设
- 适合研究算法偏见与伦理对齐的研究者
我们将性别歧视检测视为一种论证推理任务，探究大语言模型（LLMs）在意大利语和英语中理解隐含性别歧视推理的能力。核心目标是补全话语与隐含性别歧视意义之间的推理断链。研究基于论证理论构建零样本与少样本提示，融合链式思维与增强知识等技术。结果表明，LLMs在性别歧视评论的推理能力上表现不足，主要依赖内部化的女性刻板印象生成隐含假设，而非通过归纳推理得出结论。
原文摘要 · Abstract (English)
We propose misogyny detection as an Argumentative Reasoning task and we investigate the capacity of large language models (LLMs) to understand the implicit reasoning used to convey misogyny in both Italian and English. The central aim is to generate the missing reasoning link between a message and the implied meanings encoding the misogyny. Our study uses argumentation theory as a foundation to form a collection of prompts in both zero-shot and few-shot settings. These prompts integrate different techniques, including chain-of-thought reasoning and augmented knowledge. Our findings show that LLMs fall short on reasoning capabilities about misogynistic comments and that they mostly rely on their implicit knowledge derived from internalized common stereotypes about women to generate implied assumptions, rather than on inductive reasoning.
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