# Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts

arXiv: https://arxiv.org/abs/2410.11084 · HTML: https://mlsanity.com/paper/2410.11084/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation](https://mlsanity.com/paper/2101.11718/) (2021)
- [Understanding Dataset Difficulty with V-Usable Information](https://mlsanity.com/paper/2110.08420/) (2021)
- [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020)
- [Gender Bias in Masked Language Models for Multiple Languages](https://mlsanity.com/paper/2205.00551/) (2022)
- [Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models](https://mlsanity.com/paper/2101.09688/) (2021)
- [Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution](https://mlsanity.com/paper/2403.03121/) (2024)
- [Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation](https://mlsanity.com/paper/2311.00306/) (2023)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic](https://mlsanity.com/paper/2204.03558/) (2022)
- [DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias](https://mlsanity.com/paper/2310.14329/) (2023)

## Built on by (most cited first, 15 of 15)

- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)
- [Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/R'esum'e Evaluations](https://mlsanity.com/paper/2505.17049/) (2025)
- [RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models](https://mlsanity.com/paper/2601.03699/) (2026)
- [From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship](https://mlsanity.com/paper/2506.23101/) (2025)
- [Investigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes](https://mlsanity.com/paper/2508.03292/) (2025)
- [ProText: A benchmark dataset for measuring (mis)gendering in long-form texts](https://mlsanity.com/paper/2603.27838/) (2026)
- [Harsher on Male? Evaluating LLMs on Gender-Asymmetric Moral Framing Across Diverse Conflict Scenarios](https://mlsanity.com/paper/2606.14068/) (2026)
- [Gender Encoding Patterns in Pretrained Language Model Representations](https://mlsanity.com/paper/2503.06734/) (2025)
- [GenderBench: Evaluation Suite for Gender Biases in LLMs](https://mlsanity.com/paper/2505.12054/) (2025)
- [Analysing LLM Persona Generation and Fairness Interpretation in Polarised Geopolitical Contexts](https://mlsanity.com/paper/2603.22837/) (2026)
- [Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark](https://mlsanity.com/paper/2606.02214/) (2026)
- [Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing](https://mlsanity.com/paper/2606.15914/) (2026)
- [How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans](https://mlsanity.com/paper/2608.09717/) (2026)
- [Personas Differ from Native-Language Generation: Language Pathways Shape LLM Interpersonal Advice](https://mlsanity.com/paper/2608.30873/) (2026)
- [Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models](https://mlsanity.com/paper/2609.34065/) (2026)

## Abstract (arXiv)

Large language models (LLMs) acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. Prior studies have demonstrated model generations favor one gender or exhibit stereotypes about gender, but have not investigated the complex dynamics that can influence model reasoning and decision-making involving gender. We study gender equity within LLMs through a decision-making lens with a new dataset, DeMET Prompts, containing scenarios related to intimate, romantic relationships. We explore nine relationship configurations through name pairs across three name lists (men, women, neutral). We investigate equity in the context of gender roles through numerous lenses: typical and gender-neutral names, with and without model safety enhancements, same and mixed-gender relationships, and egalitarian versus traditional scenarios across various topics. While all models exhibit the same biases (women favored, then those with gender-neutral names, and lastly men), safety guardrails reduce bias. In addition, models tend to circumvent traditional male dominance stereotypes and side with 'traditionally female' individuals more often, suggesting relationships are viewed as a female domain by the models.