Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
Large Language Models (LLMs) have shown great promise in multilingual machine translation (MT), even with limited bilingual supervision. However, fine-tuning LLMs with parallel corpora presents major challenges, namely parameter interference. To address these issues, we propose Mix-MoE, a mixed Mixture-of-Experts framework designed to train LLMs for multilingual MT. Our framework operates in two distinct stages: (1) post-pretraining with MoE on monolingual corpora, and (2) post-pretraining with MoE on parallel corpora. Crucially, we divide the MoE layers into two specialized groups: Language Model Experts (LM Experts) and Machine Translation Experts (MT Experts). LM Experts are designed t...
| Authors | Bo Li, Tianyu Dong, Shaolin Zhu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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