@inproceedings{doyen-todirascu-2026-gender,
title = "Gender Bias Beyond Stereotypes: Towards Linguistically Gender-Fair Conversational {LLM}s",
author = "Doyen, Enzo and
Todirascu, Amalia",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Sta{\'n}czak, Karolina and
Nozza, Debora and
Attanasio, Giuseppe and
Neplenbroek, Vera and
Hardmeier, Christian",
booktitle = "Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing ({G}e{BNLP})",
month = nov,
year = "2026",
address = "Hengqin, China (remote)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gebnlp-1.6/",
pages = "70--91",
ISBN = "979-8-89176-447-7",
abstract = "Gender bias in NLP has mostly been studied through the lens of stereotypes, i.e. associations between gender and occupations or personality traits. In this position paper, we argue that the rise of conversational large language models (LLMs) and open-ended generation stressed another important problem that remains largely unaddressed: masculine generics bias. In many languages, including German, French or Spanish, masculine forms are used to refer to mixed-gender groups or to people whose gender is unknown or non-binary (masculine generics, or MG for short). Decades of psycholinguistic research show that these forms are not interpreted as truly generic and instead lead people to have more male-centric mental representations. As LLMs are adopted by an ever-growing number of users worldwide for a wide range of tasks, this bias can now be perpetuated in everyday interactions, all the more since preliminary studies already show that LLMs lean heavily on MG and are reluctant to use gender-fair language spontaneously. We review the evidence for why it matters, explain why existing mitigation approaches are not enough for conversational LLMs, show that GFL in German, French and Spanish is lacking in LLM pre-training corpora, and outline 6 research directions towards building multilingual, linguistically gender-fair conversational LLMs."
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<abstract>Gender bias in NLP has mostly been studied through the lens of stereotypes, i.e. associations between gender and occupations or personality traits. In this position paper, we argue that the rise of conversational large language models (LLMs) and open-ended generation stressed another important problem that remains largely unaddressed: masculine generics bias. In many languages, including German, French or Spanish, masculine forms are used to refer to mixed-gender groups or to people whose gender is unknown or non-binary (masculine generics, or MG for short). Decades of psycholinguistic research show that these forms are not interpreted as truly generic and instead lead people to have more male-centric mental representations. As LLMs are adopted by an ever-growing number of users worldwide for a wide range of tasks, this bias can now be perpetuated in everyday interactions, all the more since preliminary studies already show that LLMs lean heavily on MG and are reluctant to use gender-fair language spontaneously. We review the evidence for why it matters, explain why existing mitigation approaches are not enough for conversational LLMs, show that GFL in German, French and Spanish is lacking in LLM pre-training corpora, and outline 6 research directions towards building multilingual, linguistically gender-fair conversational LLMs.</abstract>
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%0 Conference Proceedings
%T Gender Bias Beyond Stereotypes: Towards Linguistically Gender-Fair Conversational LLMs
%A Doyen, Enzo
%A Todirascu, Amalia
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%Y Attanasio, Giuseppe
%Y Neplenbroek, Vera
%Y Hardmeier, Christian
%S Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China (remote)
%@ 979-8-89176-447-7
%F doyen-todirascu-2026-gender
%X Gender bias in NLP has mostly been studied through the lens of stereotypes, i.e. associations between gender and occupations or personality traits. In this position paper, we argue that the rise of conversational large language models (LLMs) and open-ended generation stressed another important problem that remains largely unaddressed: masculine generics bias. In many languages, including German, French or Spanish, masculine forms are used to refer to mixed-gender groups or to people whose gender is unknown or non-binary (masculine generics, or MG for short). Decades of psycholinguistic research show that these forms are not interpreted as truly generic and instead lead people to have more male-centric mental representations. As LLMs are adopted by an ever-growing number of users worldwide for a wide range of tasks, this bias can now be perpetuated in everyday interactions, all the more since preliminary studies already show that LLMs lean heavily on MG and are reluctant to use gender-fair language spontaneously. We review the evidence for why it matters, explain why existing mitigation approaches are not enough for conversational LLMs, show that GFL in German, French and Spanish is lacking in LLM pre-training corpora, and outline 6 research directions towards building multilingual, linguistically gender-fair conversational LLMs.
%U https://aclanthology.org/2026.gebnlp-1.6/
%P 70-91
Markdown (Informal)
[Gender Bias Beyond Stereotypes: Towards Linguistically Gender-Fair Conversational LLMs](https://aclanthology.org/2026.gebnlp-1.6/) (Doyen & Todirascu, GeBNLP 2026)
ACL