Summary of Greedy Output Approximation: Towards Efficient Structured Pruning For Llms Without Retraining, by Jianwei Li and Yijun Dong and Qi Lei
Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining
by Jianwei Li, Yijun Dong, Qi Lei
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 research proposes innovative methods for pruning large language models (LLMs) to reduce computational costs and hardware requirements without sacrificing performance. The study simplifies the pruning process for Transformer-based LLMs by identifying a depth-2 structure that functions independently, eliminating the need for retraining phases. Two inference-aware pruning criteria are also introduced, derived from the optimization perspective of output approximation, which outperforms traditional training-aware metrics such as gradient and Hessian. A two-step reconstruction technique is proposed to mitigate pruning errors without model retraining. Experimental results demonstrate significant reductions in computational costs and hardware requirements while maintaining superior performance across various datasets and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make big language models smaller and faster, which can save time and energy on computers. The study finds a way to remove parts of the model that are not important without having to retrain the entire thing. It also comes up with new ways to decide what parts of the model to keep or remove based on how well they work together. By using these methods, we can make language models more efficient and effective, which is important for things like chatbots, translation systems, and virtual assistants. |
Keywords
» Artificial intelligence » Inference » Optimization » Pruning » Transformer » Translation
