语言在过度分析时令人恐惧：用论证理论驱动的提示拆解隐含的厌女推理
Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts
AI总结：
本文将厌女检测重构为论证推理任务，利用论证理论驱动的提示（含思维链和增强知识）探究大语言模型在英意双语中补全厌女隐含推理的能力，发现模型主要依赖内化刻板印象而非归纳推理。
摘要：
我们提出将厌女检测作为一项论证推理任务，并研究大语言模型（LLMs）理解意大利语和英语中用于传达厌女情绪的隐含推理的能力。核心目标是生成消息与编码厌女情绪的隐含意义之间缺失的推理环节。我们的研究以论证理论为基础，构建了零样本和少样本设置下的提示集合。这些提示整合了不同的技术，包括思维链推理和增强知识。我们的研究结果表明，LLMs 在关于厌女评论的推理能力上存在不足，它们主要依赖从内化的关于女性的常见刻板印象中获得的隐含知识来生成隐含假设，而不是依赖归纳推理。
英文摘要：
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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