@inproceedings{bartl-etal-2025-adapting,
    title = "Adapting Psycholinguistic Research for {LLM}s: Gender-inclusive Language in a Coreference Context",
    author = "Bartl, Marion  and
      Murphy, Thomas Brendan  and
      Leavy, Susan",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.gebnlp-1.38/",
    pages = "451--467",
    ISBN = "979-8-89176-277-0",
    abstract = "Gender-inclusive language is often used with the aim of ensuring that all individuals, regardless of gender, can be associated with certain concepts. While psycholinguistic studies have examined its effects in relation to human cognition, it remains unclear how Large Language Models (LLMs) process gender-inclusive language. Given that commercial LLMs are gaining an increasingly strong foothold in everyday applications, it is crucial to examine whether LLMs in fact interpret gender-inclusive language neutrally, because the language they generate has the potential to influence the language of their users. This study examines whether LLM-generated coreferent terms align with a given gender expression or reflect model biases. Adapting psycholinguistic methods from French to English and German, we find that in English, LLMs generally maintain the antecedent{'}s gender but exhibit underlying masculine bias. In German, this bias is much stronger, overriding all tested gender-neutralization strategies."
}