Summary of Toward Adaptive Large Language Models Structured Pruning Via Hybrid-grained Weight Importance Assessment, by Jun Liu et al.
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
by Jun Liu, Zhenglun Kong, Pu Zhao, Changdi Yang, Hao Tang, Xuan Shen, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang
First submitted to arxiv on: 16 Mar 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 The paper introduces Hybrid-grained Weight Importance Assessment (HyWIA), a novel method for structured pruning large language models (LLMs) that combines fine-grained and coarse-grained evaluations of weight importance. HyWIA leverages an attention mechanism to adaptively determine the optimal blend of granularity in weight importance assessments during end-to-end pruning. Experimental results demonstrate the effectiveness of HyWIA, surpassing state-of-the-art LLM-Pruner by an average margin of 2.82% in accuracy across seven downstream tasks when pruning LLaMA-7B by 50%. The paper shows that evaluating both holistic and individual assessments for weight importance is essential for LLM pruning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyWIA is a new way to prune large language models, which helps keep them efficient and fast. Right now, there are two main ways to do this: structured pruning and unstructured pruning. Structured pruning looks at the model as a whole to figure out what’s important, while unstructured pruning looks at each individual part (or “weight”) to decide what’s important. The paper shows that both methods have their own strengths and weaknesses, but by combining them in a new way, HyWIA can prune models even better. This is important because it means we can use these powerful language models for more tasks, like generating text or answering questions. |
Keywords
» Artificial intelligence » Attention » Llama » Pruning
