Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs

arXiv:2605.24681v1 Announce Type: new Abstract: 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 Mo

Source: arXiv cs.CL — read the full report at the original publisher.

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