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    <abstract>Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages.</abstract>
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%0 Conference Proceedings
%T LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
%A Yamada, Ikuya
%A Ri, Ryokan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yamada-ri-2024-leia
%X Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages.
%U https://aclanthology.org/2024.findings-acl.419
%P 7029-7039

Markdown (Informal)

[LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation](https://aclanthology.org/2024.findings-acl.419) (Yamada & Ri, Findings 2024)

• LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation (Yamada & Ri, Findings 2024)

ACL

• Ikuya Yamada and Ryokan Ri. 2024. LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation. In Findings of the Association for Computational Linguistics ACL 2024, pages 7029–7039, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.