FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916746200350720 |
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| author | Zhu, Shaolin Dong, Tianyu Li, Bo Xiong, Deyi |
| author_facet | Zhu, Shaolin Dong, Tianyu Li, Bo Xiong, Deyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation Zhu, Shaolin Dong, Tianyu Li, Bo Xiong, Deyi Computation and Language Artificial Intelligence 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. |
| title | FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14256 |