Summary of Not All Experts Are Equal: Efficient Expert Pruning and Skipping For Mixture-of-experts Large Language Models, by Xudong Lu et al.
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li
First submitted to arxiv on: 22 Feb 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 This paper presents a significant breakthrough in the development of large language models (LLMs), specifically Mixture-of-Experts (MoE) LLMs. Unlike traditional LLMs, MoE LLMs can achieve better performance with fewer parameters, but their immense parameter sizes make deployment challenging. To address this issue, the authors introduce novel plug-and-play expert-level sparsification techniques to enhance the deployment efficiency of MoE LLMs. This is achieved through post-training approaches for task-agnostic and task-specific expert pruning and skipping, designed to maintain model performance across a wide range of tasks while reducing model sizes and increasing inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make big language models smaller and faster without losing their power. It’s like finding a way to make a powerful computer run more efficiently. The authors figured out how to do this for special kinds of language models called Mixture-of-Experts (MoE) LLMs. These models are good at doing many tasks, but they use up a lot of energy and take a long time to work. By making them smaller and faster, we can use them more easily on devices like smartphones or laptops. |
Keywords
* Artificial intelligence * Inference * Mixture of experts * Pruning
