Summary of Dependency-aware Semi-structured Sparsity Of Glu Variants in Large Language Models, by Zhiyu Guo et al.
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models
by Zhiyu Guo, Hidetaka Kamigaito, Taro Wanatnabe
First submitted to arxiv on: 3 May 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 proposes Dependency-aware Semi-structured Sparsity (DaSS), a novel method for pruning Large Language Models (LLMs) that incorporates structural dependency into the weight magnitude-based unstructured pruning. DaSS uses an MLP-specific pruning metric to evaluate the importance of each weight by jointly considering its magnitude and intermediate activation norms. The proposed method aims to balance adaptability and structural consistency, enabling LLMs to achieve hardware-friendly N:M sparsity patterns while maintaining computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make Large Language Models smaller and faster on computers. It’s called Dependency-aware Semi-structured Sparsity (DaSS). The model uses information about how different parts of the language work together to decide which parts to remove. This helps keep the important parts that help the model understand and generate text, but makes it more efficient for computing. The results show that DaSS works better than other methods in making LLMs smaller while still being able to do tasks quickly. |
Keywords
» Artificial intelligence » Pruning
