@inproceedings{prama-etal-2026-gender,
title = "Gender Disparities in {LLM}-Based Intimate Partner Violence Detection",
author = "Prama, Tabia Tanzin and
Fudolig, Mikaela Irene and
Crocker, Abigail M. and
Danforth, Christopher M. and
Dodds, Peter",
editor = "Card, Dallas and
Field, Anjalie and
Keith, Katherine and
Mendelsohn, Julia",
booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
month = jul,
year = "2026",
address = "San Diego",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlpcss-1.13/",
pages = "190--197",
ISBN = "979-8-89176-426-2",
abstract = "Intimate Partner Violence (IPV) is a major public health concern, and large language models (LLMs) are increasingly used for support and information-seeking in sensitive domains. We examine whether LLMs perceive relationship abuse differently depending on victim{--}perpetrator gender configuration. Using 475 Reddit posts from r/relationship{\_}advice, we generate counterfactual variants by swapping gendered identifiers to create four dyads: female{--}female (F/F), female{--}male (F/M), male{--}female (M/F), and male{--}male (M/M), where the first position denotes the victim. Four recent LLMs (GPT-5o, Gemini 3, Llama 4, and Grok 3) evaluate each variant using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results show substantial variation across models and dyads. Abuse and intent detection systematically decrease in mixed-gender dyads where the victim is male, with female perpetrator identity emerging as a consistent negative predictor of abuse recognition. Mixed-effects logistic regression confirms that gender roles significantly shape model outputs. Our findings suggest that LLMs reproduce gendered biases from online training data, with implications for support-related deployment. Code and resources are available at GitHub."
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<abstract>Intimate Partner Violence (IPV) is a major public health concern, and large language models (LLMs) are increasingly used for support and information-seeking in sensitive domains. We examine whether LLMs perceive relationship abuse differently depending on victim–perpetrator gender configuration. Using 475 Reddit posts from r/relationship_advice, we generate counterfactual variants by swapping gendered identifiers to create four dyads: female–female (F/F), female–male (F/M), male–female (M/F), and male–male (M/M), where the first position denotes the victim. Four recent LLMs (GPT-5o, Gemini 3, Llama 4, and Grok 3) evaluate each variant using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results show substantial variation across models and dyads. Abuse and intent detection systematically decrease in mixed-gender dyads where the victim is male, with female perpetrator identity emerging as a consistent negative predictor of abuse recognition. Mixed-effects logistic regression confirms that gender roles significantly shape model outputs. Our findings suggest that LLMs reproduce gendered biases from online training data, with implications for support-related deployment. Code and resources are available at GitHub.</abstract>
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%0 Conference Proceedings
%T Gender Disparities in LLM-Based Intimate Partner Violence Detection
%A Prama, Tabia Tanzin
%A Fudolig, Mikaela Irene
%A Crocker, Abigail M.
%A Danforth, Christopher M.
%A Dodds, Peter
%Y Card, Dallas
%Y Field, Anjalie
%Y Keith, Katherine
%Y Mendelsohn, Julia
%S Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego
%@ 979-8-89176-426-2
%F prama-etal-2026-gender
%X Intimate Partner Violence (IPV) is a major public health concern, and large language models (LLMs) are increasingly used for support and information-seeking in sensitive domains. We examine whether LLMs perceive relationship abuse differently depending on victim–perpetrator gender configuration. Using 475 Reddit posts from r/relationship_advice, we generate counterfactual variants by swapping gendered identifiers to create four dyads: female–female (F/F), female–male (F/M), male–female (M/F), and male–male (M/M), where the first position denotes the victim. Four recent LLMs (GPT-5o, Gemini 3, Llama 4, and Grok 3) evaluate each variant using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results show substantial variation across models and dyads. Abuse and intent detection systematically decrease in mixed-gender dyads where the victim is male, with female perpetrator identity emerging as a consistent negative predictor of abuse recognition. Mixed-effects logistic regression confirms that gender roles significantly shape model outputs. Our findings suggest that LLMs reproduce gendered biases from online training data, with implications for support-related deployment. Code and resources are available at GitHub.
%U https://aclanthology.org/2026.nlpcss-1.13/
%P 190-197
Markdown (Informal)
[Gender Disparities in LLM-Based Intimate Partner Violence Detection](https://aclanthology.org/2026.nlpcss-1.13/) (Prama et al., NLP+CSS 2026)
ACL
- Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, Christopher M. Danforth, and Peter Dodds. 2026. Gender Disparities in LLM-Based Intimate Partner Violence Detection. In Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science, pages 190–197, San Diego. Association for Computational Linguistics.