@inproceedings{schaefer-roberts-2025-gender,
    title = "Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in {P}ub{M}ed Abstracts",
    author = "Schaefer, Elizabeth  and
      Roberts, Kirk",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.11/",
    pages = "114--123",
    ISBN = "979-8-89176-275-6",
    abstract = "This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupational pronouns. A set of 379,000 PubMed abstracts from 1965-1980 was processed to identify and modify pronouns tied to professions. We developed a BERT-based model, Modern Occupational Bias Elimination with Refined Training, or MOBERT, trained on these neutralized abstracts, and compared it with 1965BERT, trained on the original dataset. MOBERT achieved a 70{\%} inclusive replacement rate, while 1965BERT reached only 4{\%}. A further analysis of MOBERT revealed that pronoun replacement accuracy correlated with the frequency of occupational terms in the training data. We propose expanding the dataset and refining the pipeline to improve performance and ensure more equitable language modeling in medical applications."
}