@inproceedings{sitaram-etal-2025-multilingual,
title = "A Multilingual, Culture-First Approach to Addressing Misgendering in {LLM} Applications",
author = "Sitaram, Sunayana and
de Wynter, Adrian and
McCrum, Isobel and
Gu, Qilong and
Chen, Si-Qing",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1587/",
doi = "10.18653/v1/2025.emnlp-main.1587",
pages = "31159--31183",
ISBN = "979-8-89176-332-6",
abstract = "Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person{'}s sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun ``they''. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard LLM-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures. We release the guardrails and synthetic dataset encompassing 42 languages, along with human and LLM-judge evaluations, to encourage further research on this subject."
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<abstract>Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person’s sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun “they”. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard LLM-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures. We release the guardrails and synthetic dataset encompassing 42 languages, along with human and LLM-judge evaluations, to encourage further research on this subject.</abstract>
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%0 Conference Proceedings
%T A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
%A Sitaram, Sunayana
%A de Wynter, Adrian
%A McCrum, Isobel
%A Gu, Qilong
%A Chen, Si-Qing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F sitaram-etal-2025-multilingual
%X Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person’s sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun “they”. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard LLM-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures. We release the guardrails and synthetic dataset encompassing 42 languages, along with human and LLM-judge evaluations, to encourage further research on this subject.
%R 10.18653/v1/2025.emnlp-main.1587
%U https://aclanthology.org/2025.emnlp-main.1587/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1587
%P 31159-31183
Markdown (Informal)
[A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications](https://aclanthology.org/2025.emnlp-main.1587/) (Sitaram et al., EMNLP 2025)
ACL