Summary of Xmoe: Sparse Models with Fine-grained and Adaptive Expert Selection, by Yuanhang Yang et al.
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection
by Yuanhang Yang, Shiyi Qi, Wenchao Gu, Chaozheng Wang, Cuiyun Gao, Zenglin Xu
First submitted to arxiv on: 27 Feb 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 The paper proposes a novel Mixture-of-Experts (MoE) model, called , designed to improve the efficiency and effectiveness of sparse MoE models. These models are effective for scaling Transformer models but suffer from computational inefficiency due to unnecessary computations involving zero or low activation values. leverages small experts and a threshold-based router to enable tokens to selectively engage only essential parameters, reducing computation load at MoE layers by over 50% without sacrificing performance. The authors demonstrate the efficacy of on language modeling and machine translation tasks and showcase its versatility by applying it to dense models for sparse computation during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a powerful computer that can help with tasks like language translation or text analysis. But sometimes, this computer uses too much energy and time because it’s doing unnecessary work. The authors of this paper created a new way to make computers work more efficiently without sacrificing their ability to do things well. They call this new way . It helps computers use less energy and time by only using the parts that are really important for each task. This can help with many types of tasks, including language translation and text analysis. |
Keywords
* Artificial intelligence * Inference * Mixture of experts * Transformer * Translation
