LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation
  Capabilities Beyond 100 Languages
Authors: name: Yinquan Lu, name: Wenhao Zhu, name: Lei Li, name: Yu Qiao, name: Fei Yuan

Large Language Models~(LLMs) demonstrate remarkable translation capabilities
in high-resource language tasks, yet their performance in low-resource
languages is hindered by insufficient multilingual data during pre-training. To
address this, we dedicate 35,000 A100-SXM4-80GB GPU hours in conducting
extensive multilingual continual pre-training on the LLaMA series models,
enabling translation support across more than 100 languages. Through a
comprehensive analysis of training strategies, such as vocabulary expansion and
data augmentation, we develop LLaMAX. Remarkably, without sacrificing its
generalization ability, LLaMAX achieves significantly higher translation
performance compared to existing open-source LLMs~(by more than 10 spBLEU
points) and performs on-par with specialized translation model~(M2M-100-12B) on
the Flores-101 benchmark. Extensive experiments indicate that LLaMAX can serve
as a robust multilingual foundation model. The
code~\url{https://github.com/CONE-MT/LLaMAX/.} and
models~\url{https://huggingface.co/LLaMAX/.} are publicly available.