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@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/",
    doi = "10.18653/v1/2026.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 \href{https://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.git}{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 \hrefhttps://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.gitGitHub.</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 \hrefhttps://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.gitGitHub.
%R 10.18653/v1/2026.nlpcss-1.13
%U https://aclanthology.org/2026.nlpcss-1.13/
%U https://doi.org/10.18653/v1/2026.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.