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    <titleInfo>
        <title>On Instruction-Finetuning Neural Machine Translation Models</title>
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    <name type="personal">
        <namePart type="given">Vikas</namePart>
        <namePart type="family">Raunak</namePart>
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        <namePart type="given">Roman</namePart>
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    <name type="personal">
        <namePart type="given">Marcin</namePart>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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            <namePart type="given">Barry</namePart>
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            <namePart type="given">Tom</namePart>
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            <namePart type="given">Philipp</namePart>
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        <name type="personal">
            <namePart type="given">Christof</namePart>
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    <abstract>In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) into orders-of-magnitude smaller NMT models. Our instruction-finetuning recipe for NMT models enables customization of translations for a limited but disparate set of translation-specific tasks.We show that NMT models are capable of following multiple instructions simultaneously and demonstrate capabilities of zero-shot composition of instructions.We also show that through instruction finetuning, traditionally disparate tasks such as formality-controlled machine translation, multi-domain adaptation as well as multi-modal translations can be tackled jointly by a single instruction finetuned NMT model, at a performance level comparable to LLMs such as GPT-3.5-Turbo.To the best of our knowledge, our work is among the first to demonstrate the instruction-following capabilities of traditional NMT models, which allows for faster, cheaper and more efficient serving of customized translations.</abstract>
    <identifier type="citekey">raunak-etal-2024-instruction</identifier>
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        <url>https://aclanthology.org/2024.wmt-1.114</url>
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        <date>2024-11</date>
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            <start>1155</start>
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%0 Conference Proceedings
%T On Instruction-Finetuning Neural Machine Translation Models
%A Raunak, Vikas
%A Grundkiewicz, Roman
%A Junczys-Dowmunt, Marcin
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F raunak-etal-2024-instruction
%X In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) into orders-of-magnitude smaller NMT models. Our instruction-finetuning recipe for NMT models enables customization of translations for a limited but disparate set of translation-specific tasks.We show that NMT models are capable of following multiple instructions simultaneously and demonstrate capabilities of zero-shot composition of instructions.We also show that through instruction finetuning, traditionally disparate tasks such as formality-controlled machine translation, multi-domain adaptation as well as multi-modal translations can be tackled jointly by a single instruction finetuned NMT model, at a performance level comparable to LLMs such as GPT-3.5-Turbo.To the best of our knowledge, our work is among the first to demonstrate the instruction-following capabilities of traditional NMT models, which allows for faster, cheaper and more efficient serving of customized translations.
%U https://aclanthology.org/2024.wmt-1.114
%P 1155-1166

Markdown (Informal)

[On Instruction-Finetuning Neural Machine Translation Models](https://aclanthology.org/2024.wmt-1.114) (Raunak et al., WMT 2024)

• On Instruction-Finetuning Neural Machine Translation Models (Raunak et al., WMT 2024)

ACL

• Vikas Raunak, Roman Grundkiewicz, and Marcin Junczys-Dowmunt. 2024. On Instruction-Finetuning Neural Machine Translation Models. In Proceedings of the Ninth Conference on Machine Translation, pages 1155–1166, Miami, Florida, USA. Association for Computational Linguistics.