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arXiv · 2505.14256

FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation

Shaolin Zhu·Tianyu Dong·Bo Li·Deyi Xiong
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Abstract

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.

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Shaolin Zhu, Tianyu Dong, Bo Li, Deyi Xiong. 2025-05-20. FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation. https://arxiv.org/abs/2505.14256

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