FuxiMT: Sparsifying Large Language Models for Chinese-Centric
  Multilingual Machine Translation
Authors: name: Shaolin Zhu, name: Tianyu Dong, name: Bo Li, name: Deyi Xiong

In this paper, we present FuxiMT, a novel Chinese-centric multilingual
machine translation model powered by a sparsified large language model (LLM).
We adopt a two-stage strategy to train FuxiMT. We first pre-train the model on
a massive Chinese corpus and then conduct multilingual fine-tuning on a large
parallel dataset encompassing 65 languages. FuxiMT incorporates
Mixture-of-Experts (MoEs) and employs a curriculum learning strategy for robust
performance across various resource levels. Experimental results demonstrate
that FuxiMT significantly outperforms strong baselines, including
state-of-the-art LLMs and machine translation models, particularly under
low-resource scenarios. Furthermore, FuxiMT exhibits remarkable zero-shot
translation capabilities for unseen language pairs, indicating its potential to
bridge communication gaps where parallel data are scarce or unavailable.