# On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models

arXiv: https://arxiv.org/abs/2410.03996 · HTML: https://mlsanity.com/paper/2410.03996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 5 Oct 2024.

## Builds on (most cited first, 20 of 20)

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## Abstract (arXiv)

We study the presence of heteronormative biases and prejudice against interracial romantic relationships in large language models by performing controlled name-replacement experiments for the task of relationship prediction. We show that models are less likely to predict romantic relationships for (a) same-gender character pairs than different-gender pairs; and (b) intra/inter-racial character pairs involving Asian names as compared to Black, Hispanic, or White names. We examine the contextualized embeddings of first names and find that gender for Asian names is less discernible than non-Asian names. We discuss the social implications of our findings, underlining the need to prioritize the development of inclusive and equitable technology.