# LLMs Reproduce Stereotypes of Sexual and Gender Minorities

arXiv: https://arxiv.org/abs/2501.05926 · HTML: https://mlsanity.com/paper/2501.05926/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models](https://mlsanity.com/paper/2206.11684/) (2022)
- [Queer In AI: A Case Study in Community-Led Participatory AI](https://mlsanity.com/paper/2303.16972/) (2023)
- [A Robust Bias Mitigation Procedure Based on the Stereotype Content Model](https://mlsanity.com/paper/2210.14552/) (2022)
- [The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth](https://mlsanity.com/paper/2402.11886/) (2024)

## Built on by (most cited first, 4 of 4)

- [Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge](https://mlsanity.com/paper/2504.07887/) (2025)
- [Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental](https://mlsanity.com/paper/2503.16534/) (2025)
- [StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs](https://mlsanity.com/paper/2606.20527/) (2026)
- [How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network](https://mlsanity.com/paper/2608.22192/) (2026)

## Abstract (arXiv)

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 social psychology model -- the Stereotype Content Model -- we demonstrate that English-language survey questions about social perceptions elicit more negative stereotypes of sexual and gender minorities from both humans and LLMs. 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, showing that they amplify representational harms in creative writing, a widely advertised use for LLMs.