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        <title>Zero-shot cross-lingual transfer in instruction tuning of large language models</title>
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            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
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            <namePart type="given">Saad</namePart>
            <namePart type="family">Mahamood</namePart>
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    <abstract>Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only data and then tested on user prompts in other languages. We advocate for the importance of evaluating various aspects of model responses in multilingual instruction following and investigate the influence of different model configuration choices. We find that cross-lingual transfer does happen successfully in IT even if all stages of model training are English-centric, but only if multiliguality is taken into account in hyperparameter tuning and with large enough IT data. English-trained LLMs are capable of generating correct-language, comprehensive and helpful responses in other languages, but suffer from low factuality and may occasionally have fluency errors.</abstract>
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%0 Conference Proceedings
%T Zero-shot cross-lingual transfer in instruction tuning of large language models
%A Chirkova, Nadezhda
%A Nikoulina, Vassilina
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F chirkova-nikoulina-2024-zero-shot
%X Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only data and then tested on user prompts in other languages. We advocate for the importance of evaluating various aspects of model responses in multilingual instruction following and investigate the influence of different model configuration choices. We find that cross-lingual transfer does happen successfully in IT even if all stages of model training are English-centric, but only if multiliguality is taken into account in hyperparameter tuning and with large enough IT data. English-trained LLMs are capable of generating correct-language, comprehensive and helpful responses in other languages, but suffer from low factuality and may occasionally have fluency errors.
%U https://aclanthology.org/2024.inlg-main.53
%P 695-708

Markdown (Informal)

[Zero-shot cross-lingual transfer in instruction tuning of large language models](https://aclanthology.org/2024.inlg-main.53) (Chirkova & Nikoulina, INLG 2024)

• Zero-shot cross-lingual transfer in instruction tuning of large language models (Chirkova & Nikoulina, INLG 2024)

ACL

• Nadezhda Chirkova and Vassilina Nikoulina. 2024. Zero-shot cross-lingual transfer in instruction tuning of large language models. In Proceedings of the 17th International Natural Language Generation Conference, pages 695–708, Tokyo, Japan. Association for Computational Linguistics.