title: FuxiMT: Sparsifying Large Language Models for Chinese-Centric
    Multilingual Machine Translation
summary: 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.
paper_authors._id: 683330f7e10e89e250807ec2
paper_authors.name: Shaolin Zhu
paper_authors._id: 683330f7e10e89e250807ec3
paper_authors.name: Tianyu Dong
paper_authors._id: 683330f7e10e89e250807ec4
paper_authors.name: Bo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:10:37.787Z
paper_authors._id: 6582c482f3006507ea10302a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6582c482f3006507ea10302a/KbgSsq0FnbMngBcWPhIXi.jpeg
paper_authors.fullname: Bo Li
paper_authors.type: user
paper_authors.user: liboaccn
paper_authors._id: 683330f7e10e89e250807ec5
paper_authors.name: Deyi Xiong
paper_title: FuxiMT: Sparsifying Large Language Models for Chinese-Centric
    Multilingual Machine Translation
paper_summary: 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.
paper_ai_summary: FuxiMT, a Chinese-centric multilingual machine translation model utilizing a sparsified large language model, demonstrates superior performance in low-resource scenarios and strong zero-shot capabilities across 65 languages.
paper_ai_keywords: sparsified large language model Mixture-of-Experts (MoEs) curriculum learning zero-shot translation