[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 atGitHub.
- 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. InProceedings 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)