LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models
  with Entity-based Data Augmentation
Ikuya Yamada Ryokan Ri
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.