Summary of Beyond Linear Approximations: a Novel Pruning Approach For Attention Matrix, by Yingyu Liang et al.
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
by Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Large Language Models (LLMs) have revolutionized various areas of life, including conversational AI and search assistants. However, their growing capabilities come at a cost: extremely large model sizes that are challenging to deploy on edge devices due to memory and computational constraints. This paper proposes a novel approach to LLM weight pruning that optimizes attention matrix approximations in transformer architectures. Unlike existing methods focusing on linear approximations, this approach accounts for the non-linear Softmax attention mechanism. Theoretical guarantees ensure convergence to an optimal pruning mask solution using Gradient Descent-based optimization. Empirical results demonstrate reduced computational costs while maintaining model performance, surpassing current state-of-the-art methods (SparseGPT and Wanda) by a significant margin. This work establishes a new theoretical foundation for LLM pruning algorithm design, potentially enabling more efficient inference on resource-constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make large language models smaller so they can be used on devices that don’t have as much memory or power. They do this by using a special way to remove some of the model’s “weights” (like a recipe) without changing how well it works. This is important because making these models smaller means we can use them on our phones, computers, and other devices instead of just powerful computers. The paper shows that their new method is better than what others have tried before and could help us make even more progress in using language models to improve our lives. |
Keywords
* Artificial intelligence * Attention * Gradient descent * Inference * Mask * Optimization * Pruning * Softmax * Transformer
