Large Language Model Pruning

• URL: http://arxiv.org/abs/2406.00030v1
• Date: Fri, 24 May 2024 18:22:15 GMT
• Title: Large Language Model Pruning
• Authors: Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao,
• Abstract summary: We suggest a model pruning technique specifically focused on LLMs. The proposed methodology emphasizes the explainability of deep learning models. We also explore the difference between pruning on large-scale models vs. pruning on small-scale models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We surely enjoy the larger the better models for their superior performance in the last couple of years when both the hardware and software support the birth of such extremely huge models. The applied fields include text mining and others. In particular, the success of LLMs on text understanding and text generation draws attention from researchers who have worked on NLP and related areas for years or even decades. On the side, LLMs may suffer from problems like model overfitting, hallucination, and device limitation to name a few. In this work, we suggest a model pruning technique specifically focused on LLMs. The proposed methodology emphasizes the explainability of deep learning models. By having the theoretical foundation, we obtain a trustworthy deep model so that huge models with a massive number of model parameters become not quite necessary. A mutual information-based estimation is adopted to find neurons with redundancy to eliminate. Moreover, an estimator with well-tuned parameters helps to find precise estimation to guide the pruning procedure. At the same time, we also explore the difference between pruning on large-scale models vs. pruning on small-scale models. The choice of pruning criteria is sensitive in small models but not for large-scale models. It is a novel finding through this work. Overall, we demonstrate the superiority of the proposed model to the state-of-the-art models.

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