@inproceedings{garcia-estrada-etal-2026-linguistic,
title = "Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation",
author = "Garcia Estrada, Luis Ernesto and
Mash, Audrey and
Escolano, Carlos and
Melero, Maite and
Basta, Christine",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.685/",
doi = "10.63317/3suzdcws7pba",
pages = "8699--8709",
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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<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.</abstract>
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%0 Conference Proceedings
%T Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation
%A Garcia Estrada, Luis Ernesto
%A Mash, Audrey
%A Escolano, Carlos
%A Melero, Maite
%A Basta, Christine
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F garcia-estrada-etal-2026-linguistic
%X 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.
%R 10.63317/3suzdcws7pba
%U https://aclanthology.org/2026.lrec-1.685/
%U https://doi.org/10.63317/3suzdcws7pba
%P 8699-8709
Markdown (Informal)
[Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation](https://aclanthology.org/2026.lrec-1.685/) (Garcia Estrada et al., LREC 2026)
ACL