Summary of Beware Of Calibration Data For Pruning Large Language Models, by Yixin Ji et al.
Beware of Calibration Data for Pruning Large Language Models
by Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang
First submitted to arxiv on: 23 Oct 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 This paper investigates the crucial role of model compression in large language models (LLMs) for reducing costs and improving inference efficiency. The authors focus on post-training pruning as a promising method that requires only a small amount of calibration data to assess parameter importance. They surprisingly find that the effects of calibration data have a greater impact than designing advanced pruning strategies, particularly at high sparsity levels. To address this gap, the paper proposes a self-generating calibration data synthesis strategy to construct feasible calibration data for pre-training data inaccessible in many cases. Experiments on recent strong open-source LLMs (e.g., DCLM and LLaMA-3) demonstrate that the proposed method outperforms commonly used calibration data and enhances strong pruning methods (e.g., Wanda and OWL). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models smaller and faster, which helps reduce costs and improves how they can be used. The researchers looked at a special way to make these models smaller called post-training pruning. They found that using the right kind of data to decide what parts of the model are most important makes a big difference in how well it works. In fact, it’s more important than finding new ways to do this pruning. To help solve this problem, the researchers came up with a way to make the necessary calibration data without needing access to the original training data. They tested their idea on some very good language models and showed that it works better than usual. |
Keywords
» Artificial intelligence » Inference » Llama » Model compression » Pruning
