@inproceedings{sanchez-etal-2024-gender,
    title = "Gender-specific Machine Translation with Large Language Models",
    author = "S{\'a}nchez, Eduardo  and
      Andrews, Pierre  and
      Stenetorp, Pontus  and
      Artetxe, Mikel  and
      Costa-juss{\`a}, Marta R.",
    editor = {S{\"a}lev{\"a}, Jonne  and
      Owodunni, Abraham},
    booktitle = "Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.mrl-1.10/",
    doi = "10.18653/v1/2024.mrl-1.10",
    pages = "148--158",
    abstract = "`While machine translation (MT) systems have seen significant improvements,it is still common for translations to reflect societal biases, such as genderbias. Decoder-only language models (LLMs) have demonstrated potential in MT, albeitwith performance slightly lagging behind traditional encoder-decoder neural machinetranslation (NMT) systems. However, LLMs offer a unique advantage: the abilityto control the properties of the output through prompting. In this study, we leveragethis flexibility to explore Llama{''}s capability to produce gender-specific translations.Our results indicate that Llama can generate gender-specific translations withtranslation quality and gender bias comparable to NLLB, a state-of-the-art multilingualNMT system.'"
}