# MaLA-500: Massive Language Adaptation of Large Language Models

**Authors:** Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann, André F. T. Martins, Hinrich Schütze  
**arXiv:** [2401.13303](https://arxiv.org/abs/2401.13303)  
**Date:** 2024-01-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2401.13303v1/

## Abstract

Large language models 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 experiments on SIB-200 show that MaLA-500 achieves state-of-the-art in-context learning results. We release MaLA-500 at https://huggingface.co/MaLA-LM