Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data
This work addresses the challenge of multilingual adaptation for AI systems, particularly benefiting low-resource language communities, but it is incremental as it builds on existing models and methods.
The paper tackles the problem of adapting large language models to 500 languages by investigating the impact of bilingual translation data in continual pre-training, finding that it enhances language transfer and performance, especially for low-resource languages, as demonstrated across 7 tasks and 12 benchmarks.
This paper investigates a critical design decision in the practice of massively multilingual continual pre-training -- the inclusion of parallel data. Specifically, we study the impact of bilingual translation data for massively multilingual language adaptation of the Llama3 family of models to 500 languages. To this end, we construct the MaLA bilingual translation corpus, containing data from more than 2,500 language pairs. Subsequently, we develop the EMMA-500 Llama 3 suite of four massively multilingual models -- continually pre-trained from the Llama 3 family of base models extensively on diverse data mixes up to 671B tokens -- and explore the effect of continual pre-training with or without bilingual translation data. Comprehensive evaluation across 7 tasks and 12 benchmarks demonstrates that bilingual data tends to enhance language transfer and performance, particularly for low-resource languages. We open-source the MaLA corpus, EMMA-500 Llama 3 suite artefacts, code, and model generations.