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

Abstract

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 parameter-efficient LoRA fine-tuning, we apply linguistic knowledge infusion—a reasoning-based method that trains models to identify gendered referents and syntactic cues before generating translations. Experiments with Mistral–7B and Salamandrata–7B on MT-GenEval show that linguistically infused models improve gender accuracy by 15 percentage points and reduce gender gaps by 27 points in English-Spanish translation, with comparable trends for Catalan. Gains are strongest for Mistral, suggesting that explicit linguistic reasoning particularly benefits general-purpose LLMs. Overall, these results demonstrate that structured linguistic priors can enhance fairness and referential consistency in multilingual machine translation.

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