LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, Fei Yuan
Abstract
Large Language Models (LLMs) demonstrate remarkable translation capabilities in highresource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training.To address this, we conduct 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 opensource 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 1 and the models 2 are publicly available.