Summary of Multi-head Mixture-of-experts, by Xun Wu et al.
Multi-Head Mixture-of-Experts
by Xun Wu, Shaohan Huang, Wenhui Wang, Furu Wei
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed Multi-Head Mixture-of-Experts (MH-MoE) architecture addresses two limitations of Sparse Mixtures of Experts (SMoE): low expert activation and lacking fine-grained analytical capabilities. By splitting tokens into sub-tokens, assigning them to diverse experts in parallel, and reintegrating the results, MH-MoE enhances expert activation, deepens context understanding, and alleviates overfitting. This architecture is easy to implement, decouples from other SMoE optimization methods, and can be integrated with other SMoE models for enhanced performance. Experimental results across three tasks – English-focused language modeling, Multi-lingual language modeling, and Masked multi-modality modeling tasks – demonstrate the effectiveness of MH-MoE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MH-MoE is a new way to make models better at understanding context. It solves two problems with another model called SMoE: not enough experts are activated, and it’s hard to understand small details within words. To fix this, MH-MoE breaks down words into smaller parts, gives each part to different experts to work on, and then puts the results back together. This makes the model better at understanding context and less likely to overfit. It’s also easy to use with other models. |
Keywords
» Artificial intelligence » Mixture of experts » Optimization » Overfitting
