Gender Bias in Portuguese Literary Texts: A Masked Language Model Approach - Silva, Mariana O.; Brandão, Michele A.; Moro, Mirella M. (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2025: Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem HumanaDO - 10.5753/stil.2025.37842, 2025)
In this work, we investigate how a corpus of Portuguese literary texts shapes the gender bias of a language model using a masked language modeling approach. We fine-tune a BERT-based model on a curated corpus of 592 literary works and analyze gender associations in adjective and verb predictions. Our results show that fine-tuning shifts gender associations: female-denoting targets showed a significant reduction in negative associations, while male-denoting targets retained a negative bias. For verbs, gender disparities decrease, though male subjects retain stronger links to intellectual/work-related verbs. These findings highlight how literary texts can shape gender representations in language models, reinforcing or reshaping biases based on training data.