Summary of Diversifying the Expert Knowledge For Task-agnostic Pruning in Sparse Mixture-of-experts, by Zeliang Zhang et al.
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
by Zeliang Zhang, Xiaodong Liu, Hao Cheng, Chenliang Xu, Jianfeng Gao
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper introduces the Mixture-of-Experts (MoE) architecture to improve Large Language Models’ (LLMs) performance without increasing inference cost. By sparsely activating model parameters, MoE enhances LLMs’ capabilities while preserving computational efficiency. However, growing memory consumption due to expert proliferation hinders deployment in real-world scenarios. The study identifies redundant knowledge encoded by some experts during pre-training and proposes a grouping and pruning method to improve parameter efficiency. This approach is validated through pruning three state-of-the-art MoE architectures, outperforming other model pruning methods on natural language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make Large Language Models better without making them slower or use more memory. By using something called Mixture-of-Experts (MoE), the models can do a lot more than before without wasting extra computing power. However, this makes them take up more space on computers and devices, which is not great for real-world uses. The researchers found that some parts of the model are storing unnecessary information and suggest a way to fix this by grouping similar parts together and removing the duplicates. This method was tested on three different models and proved to be better than other ways to make models smaller. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Pruning
