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AI Research
arXiv CS.AI·May 26·5 min read
Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
TL;DR
Multilingual machine translation (MT) using large language models (LLMs) faces challenges like parameter interference during fine-tuning. Mix-MoE, a mixed Mixture-of-Experts framework, was developed to enhance MT by separating experts into Language Model Experts and Machine Translation Experts.
✦ Why It Matters
Engineers can leverage Mix-MoE to improve multilingual translation systems while minimizing interference issues.
Key Takeaways
- Utilize Mix-MoE for improved multilingual translation tasks.
- Implement specialized experts to retain monolingual and bilingual knowledge.
- Leverage Fourier Transform features for better expert interaction.
- Reduce parameter interference in large language models.
How It Works
Mix-MoE operates in two stages: first, it fine-tunes LLMs on monolingual corpora to establish a strong language foundation. Then, it fine-tunes on parallel corpora, using two types of experts—LM Experts for monolingual knowledge and MT Experts for translation tasks.
The routing mechanism, enhanced by Fourier Transform features, allows for dynamic selection of experts based on the input, optimizing the translation process.