# Sports and Women's Sports: Gender Bias in Text Generation with Olympic Data

arXiv: https://arxiv.org/abs/2502.04218 · HTML: https://mlsanity.com/paper/2502.04218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Evaluating Gender Bias in Machine Translation](https://mlsanity.com/paper/1906.00591/) (2019)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [Challenges in Measuring Bias via Open-Ended Language Generation](https://mlsanity.com/paper/2205.11601/) (2022)
- [Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP](https://mlsanity.com/paper/2405.17159/) (2024)
- [Search Still Matters: Information Retrieval in the Era of Generative AI](https://mlsanity.com/paper/2311.18550/) (2023)
- [Tie-breaker: Using language models to quantify gender bias in sports journalism](https://mlsanity.com/paper/1607.03895/) (2016)
- [Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation](https://mlsanity.com/paper/2401.04972/) (2024)

## Built on by (most cited first, 3 of 3)

- [Identifying Bias in Machine-generated Text Detection](https://mlsanity.com/paper/2512.09292/) (2025)
- [Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs](https://mlsanity.com/paper/2607.26355/) (2026)
- [A Survey of Large Models in Sports](https://mlsanity.com/paper/2608.14377/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have been shown to be biased in prior work, as they generate text that is in line with stereotypical views of the world or that is not representative of the viewpoints and values of historically marginalized demographic groups. In this work, we propose using data from parallel men's and women's events at the Olympic Games to investigate different forms of gender bias in language models. We define three metrics to measure bias, and find that models are consistently biased against women when the gender is ambiguous in the prompt. In this case, the model frequently retrieves only the results of the men's event with or without acknowledging them as such, revealing pervasive gender bias in LLMs in the context of athletics.