Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914484127268864 |
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| author | Bonil, Gustavo Gondim, João Santos, Marina dos Hashiguti, Simone Maia, Helena Silva, Nadia Pedrini, Helio Avila, Sandra |
| author_facet | Bonil, Gustavo Gondim, João Santos, Marina dos Hashiguti, Simone Maia, Helena Silva, Nadia Pedrini, Helio Avila, Sandra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models Bonil, Gustavo Gondim, João Santos, Marina dos Hashiguti, Simone Maia, Helena Silva, Nadia Pedrini, Helio Avila, Sandra Computation and Language Artificial Intelligence 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. |
| title | Clustering Discourses: Racial Biases in Short Stories about Women Generated by Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.02834 |