Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models

Gustavo Bonil, João Gondim, Marina dos Santos, Simone Hashiguti, Helena Maia +3 more
9/2/2025
cs.CLcs.AI

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Abstract

This study investigates how large language models, in particular LLaMA 3.2-3B, construct narratives about Black and white women in short stories generated in Portuguese. From 2100 texts, we applied computational methods to group semantically similar stories, allowing a selection for qualitative analysis. Three main discursive representations emerge: social overcoming, ancestral mythification and subjective self-realization. The analysis uncovers how grammatically coherent, seemingly neutral texts materialize a crystallized, colonially structured framing of the female body, reinforcing historical inequalities. The study proposes an integrated approach, that combines machine learning techniques with qualitative, manual discourse analysis.

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Cite this paper

@article{bonil2025clustering,
  title  = {Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models},
  author = {Gustavo Bonil and João Gondim and Marina dos Santos and Simone Hashiguti and Helena Maia and Nadia Silva and Helio Pedrini and Sandra Avila},
  year   = {2025},
  eprint = {2509.02834},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.02834v1}
}

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