大型语言模型将社会从属群体描绘得更为同质化，这与人类中观察到的一种偏见一致
Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans
AI总结：
本文研究了LLMs中一种类似社会心理学现象的新偏见，发现ChatGPT将非裔、亚裔、西班牙裔及女性等社会从属群体描绘得比主导群体更具同质性，这可能会加剧刻板印象与歧视。
摘要：
大型语言模型（LLMs）在日常生活中的普及度日益增加，然而它们重现训练数据中继承的偏见的倾向仍然是一个紧迫的问题。先前对LLMs中偏见的调查集中在社会群体与刻板属性的联系上。然而，这只是此类系统可能重现的人类偏见的一种形式。我们研究了LLMs中一种新形式的偏见，它类似于一种社会心理学现象，即社会从属群体被认为比社会主导群体更具同质性。我们让最先进的LLM ChatGPT生成了关于交叉群体身份的文本，并在同质性指标上比较了这些文本。我们一致发现，ChatGPT将非裔、亚裔和西班牙裔美国人描绘得比白人美国人更具同质性，这表明该模型用更狭窄的人类经验范围来描述少数族裔群体。ChatGPT还将女性描绘得比男性更具同质性，但这些差异很小。最后，我们发现性别的影响在不同的种族/族裔群体中有所不同，使得性别的影响在非裔和西班牙裔美国人中是一致的，但在亚裔和白人美国人中则不然。我们认为，LLMs将群体描述为缺乏多样性的倾向，有可能使刻板印象和歧视行为长期存在。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
