Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts

Arianna Muti, Federico Ruggeri, Khalid Al-Khatib, Alberto Barrón-Cedeño, Tommaso Caselli
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Abstract

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.
过度分析的语言是可怕的:用论证理论驱动的提示解读隐含的厌恶女性推理
我们提出将厌女症检测作为一项论证推理任务,并研究了大语言模型(LLM)理解意大利语和英语中用于表达厌女症的隐含推理的能力。研究的核心目的是在信息和编码厌女症的隐含意义之间生成缺失的推理联系。我们的研究以论证理论为基础,形成了一系列零镜头和少镜头的提示语。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,法学硕士对厌恶女性言论的推理能力不足,他们主要依赖于从内化的对女性的共同成见中获得的内隐知识来产生隐含假设,而不是归纳推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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