StereoMap：量化大型语言模型中类人刻板印象的意识
StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models
AI总结：
本文提出基于刻板印象内容模型的StereoMap框架，从温暖度与能力两维度量化LLMs对社会群体的感知，并分析其推理过程，揭示LLMs存在多样化感知、社会差异意识及潜在偏见。
摘要：
大型语言模型（LLMs）已被观察到会编码并延续训练数据中存在的有害关联。我们提出了一个具有理论基础的框架，名为StereoMap，以深入了解它们对人口群体如何被社会看待的感知。该框架以刻板印象内容模型（SCM）为基础；这是一个来自心理学的成熟理论。根据SCM，刻板印象并非完全相同。相反，温暖度和能力这两个维度是界定刻板印象性质的因素。基于SCM理论，StereoMap使用温暖度和能力维度来映射LLMs对社会群体（由社会人口学特征定义）的感知。此外，该框架还能够研究LLMs判断中的关键词和推理表述，以揭示影响其感知的潜在因素。我们的结果表明，LLMs对这些群体表现出多样化的感知，其特征是在温暖度和能力维度上的混合评价。此外，通过分析LLMs的推理，我们的发现表明LLMs表现出对社会差异的意识，经常引用统计数据和研究成果来支持其推理。这项研究有助于理解LLMs如何感知和表征社会群体，揭示其潜在偏见及有害关联的延续。
英文摘要：
Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society. The framework is grounded in the Stereotype Content Model (SCM); a well-established theory from psychology. According to SCM, stereotypes are not all alike. Instead, the dimensions of Warmth and Competence serve as the factors that delineate the nature of stereotypes. Based on the SCM theory, StereoMap maps LLMs' perceptions of social groups (defined by socio-demographic features) using the dimensions of Warmth and Competence. Furthermore, the framework enables the investigation of keywords and verbalizations of reasoning of LLMs' judgments to uncover underlying factors influencing their perceptions. Our results show that LLMs exhibit a diverse range of perceptions towards these groups, characterized by mixed evaluations along the dimensions of Warmth and Competence. Furthermore, analyzing the reasonings of LLMs, our findings indicate that LLMs demonstrate an awareness of social disparities, often stating statistical data and research findings to support their reasoning. This study contributes to the understanding of how LLMs perceive and represent social groups, shedding light on their potential biases and the perpetuation of harmful associations.
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