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
The continuous development and refinement of AI architectures like LLMs drive the need for more efficient and performant multilingual capabilities, especially as AI adoption globalizes.
Improving multilingual machine translation is crucial for global interoperability of AI systems, reducing language barriers in data and communication, and broadening the reach of AI applications.
This advancement suggests a path toward more accurate and scalable multilingual LLMs, potentially lowering the computational and data overhead for supporting diverse languages.
- · AI developers
- · Multilingual businesses
- · International organizations
- · Translation service providers leveraging AI
- · Traditional translation agencies resistant to AI integration
Increased accuracy and efficiency in multilingual communication facilitated by AI.
Broader global adoption of AI products and services due to enhanced language model accessibility.
Potential for new AI applications that seamlessly operate across multiple languages, fostering cross-cultural innovation.
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Read at arXiv cs.CL