模拟身份，传播偏见：LLM生成文本中的抽象化与刻板印象
Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text
AI总结：
本文研究人物角色提示是否导致LLM生成文本出现不同语言抽象化水平，引入Self-Stereo数据集，通过具体性、特异性和否定性三个指标分析六种模型，发现该方法在调节抽象化上有限，且可能传播刻板印象。
摘要：
人物角色提示（persona-prompting）是一种日益增长的策略，旨在通过指定身份的视角引导大型语言模型（LLM）模拟特定观点或语言风格。虽然这种方法常用于个性化输出，但它对LLM如何表征社会群体的影响仍未得到充分探索。在本文中，我们研究了人物角色提示是否会导致在生成将社会人口类别与刻板或非刻板属性相关联的短文本时，出现不同程度的语言抽象化——这是刻板印象的一个既定标志。基于语言预期偏差（Linguistic Expectancy Bias）框架，我们分析了六种开放权重LLM在三种提示条件下的输出，将11种人物角色驱动的响应与通用AI助手的响应进行了比较。为支持这一分析，我们引入了Self-Stereo，一个来自Reddit的自我报告刻板印象的新数据集。我们通过三个指标来衡量抽象化：具体性、特异性和否定性。我们的结果凸显了人物角色提示在调节语言抽象化方面的局限性，证实了关于人物角色作为社会人口群体代表生态性的批评，并引发了对即使在看似唤起边缘群体声音时也可能传播刻板印象的担忧。
英文摘要：
Persona-prompting is a growing strategy to steer LLMs toward simulating particular perspectives or linguistic styles through the lens of a specified identity. While this method is often used to personalize outputs, its impact on how LLMs represent social groups remains underexplored. In this paper, we investigate whether persona-prompting leads to different levels of linguistic abstraction - an established marker of stereotyping - when generating short texts linking socio-demographic categories with stereotypical or non-stereotypical attributes. Drawing on the Linguistic Expectancy Bias framework, we analyze outputs from six open-weight LLMs under three prompting conditions, comparing 11 persona-driven responses to those of a generic AI assistant. To support this analysis, we introduce Self-Stereo, a new dataset of self-reported stereotypes from Reddit. We measure abstraction through three metrics: concreteness, specificity, and negation. Our results highlight the limits of persona-prompting in modulating abstraction in language, confirming criticisms about the ecology of personas as representative of socio-demographic groups and raising concerns about the risk of propagating stereotypes even when seemingly evoking the voice of a marginalized group.
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。
