Summary of Alphapruning: Using Heavy-tailed Self Regularization Theory For Improved Layer-wise Pruning Of Large Language Models, by Haiquan Lu et al.
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
by Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 abstract presents an innovative approach to pruning large language models (LLMs), which aims to reduce their size without compromising performance. Existing pruning strategies often assign uniform ratios across layers, limiting the overall pruning ability. The proposed method, AlphaPruning, leverages Heavy-Tailed Self-Regularization (HT-SR) Theory and empirical spectral densities (ESDs) of weight matrices to design improved layerwise pruning ratios for LLMs. This theoretically principled approach leads to a more effective allocation of sparsity ratios across layers. Empirical results show that AlphaPruning can prune the LLaMA-7B model to 80% sparsity while maintaining reasonable perplexity, achieving a first in the literature on LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting bigger and better at understanding human language. Researchers have been working on ways to make them smaller and faster without losing their ability to understand us. One approach is called pruning, where you remove some of the model’s parameters without affecting its performance. The problem is that most pruning methods treat all parts of the model equally, which isn’t very effective. In this paper, scientists propose a new way to prune language models that takes into account how well each part of the model was trained. They call it AlphaPruning and show that it can make large models like LLaMA-7B much smaller without losing their ability to understand text. |
Keywords
» Artificial intelligence » Llama » Perplexity » Pruning » Regularization
