Sports and Women’s Sports: Gender Bias in Text Generation with Olympic Data (2025)

Abstract: 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.

## introduction large language models (llms) have quickly become part of the daily lives of many people around the world. while they were initially developed solely for the purpose of generating text, their capabilities have been found to expand to few-shot and zero-shot classification @xcite . the accessibility of models like chatgpt has allowed non-experts to use llms for various tasks that had previously never been imagined, and furthermore, technology giants such as google have begun to experiment with their use in core products including search @xcite . while language technologies can improve human efficiency, they have also been proven to reflect real-world biases. these biases are often surfaced by associating terms representative of demographic groups with professions or activities. in this paper, we seek to quantify gender bias in llm's answers to factual questions. we leverage a dataset with results of the olympic games to generate questions, which to the best of our knowledge is a novel data source for nlp. we take advantage of the fact that parallel events exist for women's and men's teams, and use metadata about those events to construct prompts. we use two types of prompts: one where the gender is stated (specified) and one where the gender is ambiguous (underspecified). we then annotate the generated text to measure various types of bias. this paper makes numerous contributions. first, we introduce a data source and framework for probing gender favoritism of llm's answers to factual questions. next, we compare closed and openweight llms in their overall correctness and gender bias. finally, we define multiple metrics to demonstrate that while models do not exhibit all types of measurable gender bias, they consistently exhibit bias in the face of ambiguity. ## data our data consists of the results from the olympic games from 1988 through 2021, which were obtained through a data request to the olympic studies center. foot_0 this dataset is interesting in the context of studying the reproduction of factual content by llms because each instance is connected to a gender (from the event itself) and a country (the national olympic committee (noc)). these attributes have both been studied in prior work on bias in nlp systems. we focus on team events foot_1 with both a female and male competition in the years 1988 through 2021, leading to a total of 338 events (169 for each gender) in our dataset. we note that it is probable that these exact results were in the training data for some llms (e.g., from wikipedia), but we do not view this as a drawback. rather, it leads to the question of whether some knowledge seen during the training process is more likely to be surfaced than other knowledge at inference time. ## methods in this work, we explore a variety of ways to quantify gender bias in the generation of olympic results across numerous models. we focus on studying bias directly in generated text, rather than metrics like perplexity, as is recommended by @xcite due to the closer connection to real downstream tasks. 3 a shortcoming of this approach is that it is dependent on decoding parameters @xcite . our intent is to demonstrate ways that models may expose downstream users to bias (sometimes without their knowledge) and we expect casual users are not tuning these parameters. therefore, we use the default parameters (from the huggingface generation pipeline foot_3 and the gpt api foot_4 ) in order to most closely mimic a realistic user experience. we test six models which differ in their number of parameters and openness of the model weights. these include gpt-4o-mini/4o, foot_5 llama3.1-8b/70b, foot_6 and mistral-nemo foot_7 /large. 9 we focus on instruction-tuned models, as our prompts fall within the domain of open-domain question answering. appendix a describes computational resources used for text generation. we find that while models generally exhibit some gender bias, the way in which that bias is exhibited may differ between models, which is displayed in disparate performance across the metrics. we use two types of prompts, which we refer to as specified and underspecified, and define three metrics to measure bias: knowledge-based (using the specified prompts), ambiguous-explicit (using the underspecified prompts) and ambiguous-implicit (using the underspecified prompts). the prompt formats and the metrics are detailed below. ## results all results are presented in average f1 the overall f1 scores are fairly high. as expected, models with more parameters have better performance on this task; mistral-large has the best performance. post-hoc analysis while the results in the notable outlier with a score of -.32 is artistic gymnastics; only 18.5% of scores across models and years are positive. this further demonstrates how llms mirror our society, as gymnastics has been classified among a small number of stereotypically feminine sports based on survey responses @xcite and has historically been among the sports with a large percentage of television coverage devoted to women in the united states @xcite . in addition to stereotypical gender associations of individual sports, it is possible that media coverage of individual star athletes such as simone biles (gymnastics) or michael phelps (swimming) may influence the output of llms when using the underspecified prompt. ## conclusions in this paper, we propose a data source and framework for evaluating various types of gender bias in language models. our method is unique in that it does not rely on gendered names or word lists that are indicative of common stereotypes. instead, we rely on the existence of parallel athletic events for men and women, and probe for bias in the models by prompting them to generate the results of those events. to encourage further work in this direction, the prompts and annotations used in this work are discipline mean score artistic gymnastics -0.32 indoor volleyball -0.01 field hockey 0.02 handball 0.03 basketball 0.05 archery 0.07 athletics 0.14 rowing 0.28 swimming 0.36 fencing 0.43 this implicit bias mirrors bias in the language used to describe sporting events as a whole; in the united states, for instance, the men's professional basketball league is the "national basketball association" (nba) while the women's professional league is the "women's national basketball association" (wnba). this language indicates that men are viewed as the default gender in sports, while women are secondary, reflecting the many ways that women are ignored in society at large @xcite . we encourage researchers and engineers to consider this problem of the "default man" when developing future models.