Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior investigations into bias in LLMs have focused on the association of social groups with stereotypical attributes. However, this is only one form of human bias such systems may reproduce. We investigate a new form of bias in LLMs that resembles a social psychological phenomenon where socially subordinate groups are perceived as more homogeneous than socially dominant groups. We had ChatGPT, a state-of-the-art LLM, generate texts about intersectional group identities and compared those texts on measures of homogeneity. We consistently found that ChatGPT portrayed African, Asian, and Hispanic Americans as more homogeneous than White Americans, indicating that the model described racial minority groups with a narrower range of human experience. ChatGPT also portrayed women as more homogeneous than men, but these differences were small. Finally, we found that the effect of gender differed across racial/ethnic groups such that the effect of gender was consistent within African and Hispanic Americans but not within Asian and White Americans. We argue that the tendency of LLMs to describe groups as less diverse risks perpetuating stereotypes and discriminatory behavior.

翻译：大型语言模型（LLM）在日常生活中的应用日益广泛，但其从训练数据中继承偏见的问题仍令人担忧。此前对LLM偏见的研究主要聚焦于社会群体与刻板印象属性的关联，但这仅是此类系统可能复制的其中一种人类偏见形式。我们探究了LLM中一种新型偏见，该偏见与一种社会心理现象类似：社会弱势群体被感知为比社会优势群体更具同质性。我们使用当前最先进的LLM——ChatGPT——生成关于交叉群体身份的描述文本，并在同质性指标维度上进行比较。结果一致发现：ChatGPT将非裔、亚裔及西班牙裔美国人描绘得比白种美国人更具同质性，表明该模型用更狭隘的人类经验范围描述少数种族群体。ChatGPT还描述女性比男性更具同质性，但差异较小。最后我们发现，性别效应在种族/民族群体间存在差异：性别效应在非裔与西班牙裔美国人内保持稳定，但在亚裔与白种美国人中并不显著。我们认为，LLM倾向将群体描述为缺乏多样性，这可能导致刻板印象与歧视行为的长期存续。