Summary of A Survey on Mixture Of Experts, by Weilin Cai et al.
A Survey on Mixture of Experts
by Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, Jiayi Huang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Large language models have made significant advancements in various fields, including natural language processing and computer vision. Their success is attributed to large model sizes, diverse datasets, and computational power during training. This paper focuses on mixture-of-experts (MoE), a method for scaling up model capacity with minimal computation overhead. Despite its growing popularity, there lacks a comprehensive review of the literature on MoE. This survey aims to fill this gap by introducing the structure of the MoE layer, proposing a new taxonomy, and reviewing core designs for various MoE models. The paper also highlights open-source implementations, hyperparameter configurations, and empirical evaluations. Additionally, it outlines applications of MoE in practice and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about large language models and how they work well for many tasks. The author looks at a special part of these models called mixture-of-experts (MoE). MoE helps make the models better without using too much computer power. Even though MoE is popular, there isn’t a big review of all the research on it yet. This paper tries to fix that by explaining what MoE does and how different researchers have used it. |
Keywords
* Artificial intelligence * Hyperparameter * Mixture of experts * Natural language processing
