C. Escolano

Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation

Large Language Models (LLMs) achieve strong performance in machine translation (MT) but often encode gender bias, particularly when translating from non-gendered into gendered languages. This paper introduces a fine-tuning strategy to mitigate such bias in English → Spanish and English → Catalan translation. Using para...