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    <titleInfo>
        <title>Gender-specific Machine Translation with Large Language Models</title>
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    <name type="personal">
        <namePart type="given">Eduardo</namePart>
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    <name type="personal">
        <namePart type="given">Mikel</namePart>
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    <name type="personal">
        <namePart type="given">Marta</namePart>
        <namePart type="given">R</namePart>
        <namePart type="family">Costa-jussà</namePart>
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            <title>Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)</title>
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            <namePart type="given">Jonne</namePart>
            <namePart type="family">Sälevä</namePart>
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    <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.’</abstract>
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        <url>https://aclanthology.org/2024.mrl-1.10</url>
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%0 Conference Proceedings
%T Gender-specific Machine Translation with Large Language Models
%A Sánchez, Eduardo
%A Andrews, Pierre
%A Stenetorp, Pontus
%A Artetxe, Mikel
%A Costa-jussà, Marta R.
%Y Sälevä, Jonne
%Y Owodunni, Abraham
%S Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F sanchez-etal-2024-gender
%X ‘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.’
%U https://aclanthology.org/2024.mrl-1.10
%P 148-158

Markdown (Informal)

[Gender-specific Machine Translation with Large Language Models](https://aclanthology.org/2024.mrl-1.10) (Sánchez et al., MRL 2024)

• Gender-specific Machine Translation with Large Language Models (Sánchez et al., MRL 2024)

ACL

• Eduardo Sánchez, Pierre Andrews, Pontus Stenetorp, Mikel Artetxe, and Marta R. Costa-jussà. 2024. Gender-specific Machine Translation with Large Language Models. In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024), pages 148–158, Miami, Florida, USA. Association for Computational Linguistics.