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        <title>Pruning Multilingual Large Language Models for Multilingual Inference</title>
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        <namePart type="given">Hwichan</namePart>
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    <name type="personal">
        <namePart type="given">Mamoru</namePart>
        <namePart type="family">Komachi</namePart>
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            <namePart type="given">Mohit</namePart>
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            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a challenge yet to be fully addressed. This study introduces a promising direction for enhancing non-English performance through a specialized pruning approach. Specifically, we prune MLLMs using bilingual sentence pairs from English and other languages and empirically demonstrate that this pruning strategy can enhance the MLLMs’ performance in non-English language.</abstract>
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%0 Conference Proceedings
%T Pruning Multilingual Large Language Models for Multilingual Inference
%A Kim, Hwichan
%A Suzuki, Jun
%A Hirasawa, Tosho
%A Komachi, Mamoru
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kim-etal-2024-pruning
%X Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a challenge yet to be fully addressed. This study introduces a promising direction for enhancing non-English performance through a specialized pruning approach. Specifically, we prune MLLMs using bilingual sentence pairs from English and other languages and empirically demonstrate that this pruning strategy can enhance the MLLMs’ performance in non-English language.
%U https://aclanthology.org/2024.findings-emnlp.580
%P 9921-9942

Markdown (Informal)

[Pruning Multilingual Large Language Models for Multilingual Inference](https://aclanthology.org/2024.findings-emnlp.580) (Kim et al., Findings 2024)

• Pruning Multilingual Large Language Models for Multilingual Inference (Kim et al., Findings 2024)

ACL

• Hwichan Kim, Jun Suzuki, Tosho Hirasawa, and Mamoru Komachi. 2024. Pruning Multilingual Large Language Models for Multilingual Inference. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9921–9942, Miami, Florida, USA. Association for Computational Linguistics.