Summary of Large Language Model Pruning, by Hanjuan Huang (1)(2) et al.
Large Language Model Pruning
by Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao
First submitted to arxiv on: 24 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 The paper proposes a model pruning technique specifically designed for Large Language Models (LLMs), which have gained popularity in recent years due to their superior performance in text understanding and generation tasks. The goal is to reduce the number of parameters required, making it possible to train smaller models that are more explainable and trustworthy. The authors use mutual information-based estimation to identify redundant neurons and eliminate them, achieving a more precise pruning procedure. They also explore the difference between pruning large-scale LLMs versus small-scale ones, finding that the choice of pruning criteria is more critical for small models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method for reducing the size of Large Language Models (LLMs) while maintaining their performance. By eliminating unnecessary neurons, the model becomes smaller and easier to understand. This technique helps reduce the need for extremely large models and makes it possible to train more efficient ones that are better suited for certain tasks. |
Keywords
» Artificial intelligence » Pruning
