Summary of Efficient Expert Pruning For Sparse Mixture-of-experts Language Models: Enhancing Performance and Reducing Inference Costs, by Enshu Liu et al.
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
by Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
First submitted to arxiv on: 1 Jul 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 The paper introduces EEP (Efficient Expert Pruning), a gradient-free evolutionary strategy that enhances the pruning of experts in Sparse Mixture-of-Experts (SMoE) architectures. This approach achieves greater sparsity while maintaining or improving performance on downstream tasks, such as reducing GPU memory requirements and accelerating inference. By pruning up to 75% of experts in Mixtral models, the authors demonstrate a substantial reduction in parameters with minimal performance loss. Furthermore, they observe improved performance on certain tasks, like a significant increase in accuracy on the SQuAD dataset when pruning half of the experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make large language models use less memory and computing power while still working well. To do this, researchers developed a new method called EEP that helps remove some of the unnecessary parts of these models without hurting their performance. This can help make it easier to use these powerful models in everyday applications. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Pruning
