Papers by Sungbin Shin
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization (2024.emnlp-main)
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| Challenge: | minimizing reconstruction error is not always ideal and can overfit calibration data. |
| Approach: | They propose a method to prune large language models by divide and conquer . they propose minimizing reconstruction error by more than 90% by using calibration data . |
| Outcome: | The proposed pruning approach generates high reconstruction errors . the proposed technique reduces reconstruction error by more than 90% . |