---
title: "Gender Disparities in LLM-Based Intimate Partner Violence"
description: "The paper asks whether large language models judge the same abuse story differently depending on the genders of victim… 35 references."
authors: ["Tabia Tanzin Prama", "Mikaela Irene Fudolig", "Abigail M. Crocker", "Christopher M. Danforth", "Peter Dodds"]
year: 2026
venue: "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science"
source: "https://aclanthology.org/2026.nlpcss-1.13/"
doi: "10.18653/v1/2026.nlpcss-1.13"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/prama-2026-gender-disparities-llm-based-intimate/"
---
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# Gender Disparities in LLM-Based Intimate Partner Violence Detection

**Authors:** [Tabia Tanzin Prama](https://md.research.elamur.ai/free/en/authors/tabia-tanzin-prama/), Mikaela Irene Fudolig, Abigail M. Crocker, [Christopher M. Danforth](https://md.research.elamur.ai/free/en/authors/christopher-m-danforth/) and [Peter Dodds](https://md.research.elamur.ai/free/en/authors/peter-sheridan-dodds/) · **Year:** 2026 · **Venue:** [Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/) ([Workshop on Natural Language Processing and Computational Social Science 2026](https://md.research.elamur.ai/free/en/venues/nlpcss/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.nlpcss-1.13/) · [PDF](https://aclanthology.org/2026.nlpcss-1.13.pdf) · [DOI 10.18653/v1/2026.nlpcss-1.13](https://doi.org/10.18653/v1/2026.nlpcss-1.13) · [OpenAlex](https://openalex.org/W7166798787)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks whether large language models judge the same abuse story differently depending on the genders of victim and abuser. This matters because people seeking help with intimate partner violence (IPV) increasingly turn to LLM tools. The authors manually selected 475 two-person narratives from r/relationship_advice that had clear victim and perpetrator roles and stated genders. They then programmatically rewrote pronouns, kinship terms, names and gender tags to produce every victim/perpetrator configuration (F/F, F/M, M/F, M/M), giving 1,900 texts.

GPT-5o, Gemini 3, Llama 4 and Grok 3 each completed a structured questionnaire grounded in IPV frameworks. It covered relationship status, IPV presence, controlling intent, infidelity and six abuse types. No gold labels exist, so the authors compare positive-label rates (PLR) and fit mixed-effects logistic regressions with a random intercept per post.

The models differ widely in baseline sensitivity. All of them flag abuse less often when the perpetrator is a woman, so male victims of female partners are recognized least. This gendered double standard argues for bias audits before LLMs are used in survivor support.

## Key points
- **Dataset:** The original posts were skewed toward mixed-gender dyads (29 F/F, 228 F/M, 202 M/F, 16 M/M). Counterfactual swapping placed every post in all four dyads (475 × 4 = 1,900 samples).
- **Outcomes:** The ten labels were IS_REL, IS_IPV, HAS_INTENT, IS_CHEATING, IS_EMOT, IS_PSYC, IS_PHYS, IS_SEXL, IS_FINL and IS_TECH. For the three-way questions, only explicit "yes" answers counted as positive.
- **Llama 4 shows the largest gender sensitivity:**
  - IPV detection falls from 40.08% (F/F) to 30.59% (M/F).
  - Intent attribution falls from 39.87% to 21.73%, a drop of about 18 points.
  - GPT-5o and Grok 3 are much more conservative overall, flagging IPV in roughly 12–20% of cases.
- **Regression results:**
  - A female perpetrator significantly lowers the odds of an IS_IPV "yes" for every model. GPT-5o shows the strongest effect (OR = 0.504).
  - Female–female dyads produce a compensating boost, most strikingly for Llama 4 on IS_TECH (OR = 9.316).
- **Limitations:**
  - The sample is small.
  - Only four proprietary models were tested.
  - All queries used a fixed temperature of 1.0.
  - There is no expert-annotated ground truth.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Vermont](https://md.research.elamur.ai/free/en/universities/university-of-vermont/) — University, Burlington, United States · 6 works · 3,740 citations
- [The University of Adelaide](https://md.research.elamur.ai/free/en/universities/the-university-of-adelaide/) — University, Adelaide, Australia · 6 works · 1,417 citations
- [Adelaide University](https://md.research.elamur.ai/free/en/universities/adelaide-university/) — University, Adelaide, Australia · 5 works · 3 citations

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## Cite

Cite as: 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. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{prama2026gender,
  title = {{Gender Disparities in LLM-Based Intimate Partner Violence Detection}},
  author = {Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, Christopher M. Danforth and Peter Dodds},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.13},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.13}
}
```

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34. Marudan Sivagurunathan, David M. Walton, Tara L. Packham, Richard G. Booth, and Joy Macdermid. 2021b. “punched in the balls”: Male intimate partner violence disclosures and replies on reddit. American Journal of Men’s Health, 15. [find](https://scholar.google.com/scholar?q=Marudan+Sivagurunathan%2C+David+M%2E+Walton%2C+Tara+L%2E+Packham%2C+Richard+G%2E+Booth%2C+and+Joy+Macdermid%2E+2021b%2E+%E2%80%9Cpunched+in+the+balls%E2%80%9D%3A+Male+intimate+partner+violence+disclosures+and+replies+on+reddit%2E+American)
35. H. L. Storer, E. X. Nyerges, and S. Hamby. 2022. Technology “feels less threatening”: The processes by [find](https://scholar.google.com/scholar?q=H%2E+L%2E+Storer%2C+E%2E+X%2E+Nyerges%2C+and+S%2E+Hamby%2E+2022%2E+Technology+%E2%80%9Cfeels+less+threatening%E2%80%9D%3A+The+processes+by)

*Updated 1 November 2026*