Large language models (LLMs) have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates a substantial gap in their effectiveness for low-resource languages. To bridge this gap, we introduce MaLA-500, a novel large language model designed to cover an extensive range of 534 languages. To train MaLA-500, we employ vocabulary extension and continued pretraining on LLaMA 2 with Glot500-c. Our intrinsic evaluation demonstrates that MaLA-500 is better at predicting the given texts of low-resource languages than existing multilingual LLMs. Moreover, the extrinsic evaluation of in-context learning shows that MaLA-500 outperforms previous LLMs on SIB200 and Taxi1500 by a significant margin, i.e., 11.68% and 4.82% marco-average accuracy across languages.
@article{lin2024mala,
title={MaLA-500: Massive Language Adaptation of Large Language Models},
author={Lin, Peiqin and Ji, Shaoxiong and Tiedemann, J{\"o}rg and Martins, Andr{\'e} FT and Sch{\"u}tze, Hinrich},
journal={arXiv preprint arXiv:2401.13303},
year={2024}
}