  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. We release MaLA-500 at https://huggingface.co/MaLA-LM
