Gender Disparities in LLM-Based Intimate Partner Violence Detection

Researchers found that large language models exhibit gender bias when detecting intimate partner violence, with abuse detection rates dropping significantly when the victim is male and the perpetrator is female. The study, which tested GPT-5o, Gemini 3, Llama 4, and Grok 3 on Reddit posts, suggests these models reproduce gendered stereotypes from training data, raising concerns for their use in sensitive support applications.

Gender Disparities in LLM-Based Intimate Partner Violence Detection https://aclanthology.org/2026.nlpcss-1.13.pdf Tabia Tanzin Prama /people/tabia-tanzin-prama/unverified/ , Mikaela Irene Fudolig /people/mikaela-irene-fudolig/unverified/ , Abigail M. Crocker /people/abigail-m-crocker/unverified/ , Christopher M. Danforth /people/christopher-m-danforth/unverified/ , Peter Dodds /people/peter-dodds/unverified/ 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 https://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.git . - Anthology ID: - 2026.nlpcss-1.13 - Volume: Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science /volumes/2026.nlpcss-1/ - Month: - July - Year: - 2026 - Address: - San Diego - Editors: Dallas Card /people/dallas-card/ , Anjalie Field /people/anjalie-field/ , Katherine Keith /people/katherine-keith/ , Julia Mendelsohn /people/julia-mendelsohn/ - Venues: NLP+CSS /venues/nlpcss/ | WS /venues/ws/ - SIG: - Publisher: - Association for Computational Linguistics - Note: - Pages: - 190–197 - Language: - URL: https://aclanthology.org/2026.nlpcss-1.13/ https://aclanthology.org/2026.nlpcss-1.13/ - DOI: 10.18653/v1/2026.nlpcss-1.13 https://doi.org/10.18653/v1/2026.nlpcss-1.13 - Cite 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 https://aclanthology.org/2026.nlpcss-1.13/ . In Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science , pages 190–197, San Diego. Association for Computational Linguistics. - Cite Informal : Gender Disparities in LLM-Based Intimate Partner Violence Detection https://aclanthology.org/2026.nlpcss-1.13/ Prama et al., NLP+CSS 2026 - PDF: https://aclanthology.org/2026.nlpcss-1.13.pdf https://aclanthology.org/2026.nlpcss-1.13.pdf