Summary of Alps: Improved Optimization For Highly Sparse One-shot Pruning For Large Language Models, by Xiang Meng et al.
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
by Xiang Meng, Kayhan Behdin, Haoyue Wang, Rahul Mazumder
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces ALPS, an optimization-based framework for pruning Large Language Models (LLMs) without retraining. The approach uses operator splitting technique and preconditioned conjugate gradient-based post-processing step to efficiently reduce the computational resources and storage requirements of LLMs while preserving their impressive performance. ALPS outperforms state-of-the-art methods in terms of pruning objective and perplexity reduction, particularly for highly sparse models. For example, on the OPT-30B model with 70% sparsity, ALPS achieves a 13% reduction in test perplexity on the WikiText dataset and a 19% improvement in zero-shot benchmark performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models smaller without losing their ability to understand language. Right now, these models are very big and use lots of computer power and storage space. To fix this, scientists have developed ways to remove some parts of the model that aren’t important. But these methods often don’t work well or take a long time. The new approach, called ALPS, is better because it uses math problems to find the right parts to remove and then checks itself to make sure it’s doing a good job. This helps to keep the model small while still keeping its good language understanding skills. |
Keywords
» Artificial intelligence » Language understanding » Optimization » Perplexity » Pruning » Zero shot
