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2025 ICML ICML 2025

Let LLM Tell What to Prune and How Much to Prune

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Mingzhe Yang, Sihao Lin, Changlin Li, Xiaojun Chang

Topics

Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Efficient Computing Artificial Intelligence > Core AI > Large Language Models

Keywords

inference cost large language model pruning efficiency dynamic pruning ratios functionality
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