LLMs Reproduce Stereotypes of Sexual and Gender Minorities

• URL: http://arxiv.org/abs/2501.05926v1
• Date: Fri, 10 Jan 2025 12:46:39 GMT
• Title: LLMs Reproduce Stereotypes of Sexual and Gender Minorities
• Authors: Ruby Ostrow, Adam Lopez,
• Abstract summary: We study the biases of large language models towards sexual and gender minorities beyond binary categories. Our analysis shows that LLMs generate stereotyped representations of sexual and gender minorities in creative writing.
• Score: 7.068680287596106
• License:
• Abstract: A large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories _men_ and _women_, conflating gender with sex, and ignoring different sexual identities. But gender and sexuality exist on a spectrum, so in this paper we study the biases of large language models (LLMs) towards sexual and gender minorities beyond binary categories. Grounding our study in a widely used psychological framework -- the Stereotype Content Model -- we demonstrate that English-language survey questions about social perceptions elicit more negative stereotypes of sexual and gender minorities from LLMs, just as they do from humans. We then extend this framework to a more realistic use case: text generation. Our analysis shows that LLMs generate stereotyped representations of sexual and gender minorities in this setting, raising concerns about their capacity to amplify representational harms in creative writing, a widely promoted use case.

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