LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911907297886208 |
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| author | Yamada, Ikuya Ri, Ryokan |
| author_facet | Yamada, Ikuya Ri, Ryokan |
| contents | 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. The source code is available at https://github.com/studio-ousia/leia. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11485 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation Yamada, Ikuya Ri, Ryokan Computation and Language Artificial Intelligence Machine Learning 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. The source code is available at https://github.com/studio-ousia/leia. |
| title | LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.11485 |