Summary of Competesmoe — Effective Training Of Sparse Mixture Of Experts Via Competition, by Quang Pham et al.
CompeteSMoE – Effective Training of Sparse Mixture of Experts via Competition
by Quang Pham, Giang Do, Huy Nguyen, TrungTin Nguyen, Chenghao Liu, Mina Sartipi, Binh T. Nguyen, Savitha Ramasamy, Xiaoli Li, Steven Hoi, Nhat Ho
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to training sparse mixture of experts (SMoE) models, which have been limited by the representation collapse issue. The authors introduce a competition mechanism that routes inputs to only the most responsive experts, achieving optimal convergence rates. They also develop CompeteSMoE, an efficient algorithm for large language model training using this routing policy. Empirical evaluations on transformer architectures and various tasks demonstrate the improved performance, robustness, and scalability of CompeteSMoE compared to state-of-the-art SMoE strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning called representation collapse. It’s like when you try to draw something complex but all you can do is copy what someone else drew before. The authors found a way to make experts (specialized parts of the model) work together by giving them a competition to see who gets the most attention. This makes the model better at doing tasks and using its knowledge. They even made an algorithm called CompeteSMoE that does this efficiently, so it can be used with big models like those for language. |
Keywords
* Artificial intelligence * Attention * Large language model * Machine learning * Mixture of experts * Transformer
