Paper · arXiv 2503.07605
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Xun Liang, Hanyu Wang, Huayi Lai, Simin Niu, Shichao Song, Jiawei Yang, Jihao Zhao, Feiyu Xiong, Bo Tang, Zhiyu Li
Sparse Expert Activation Pruning (SEAP) is a method for pruning large language models that reduces computational overhead while maintaining accuracy by identifying and retaining task-specific expert activations.
Abstract
Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottleneck. This paper introduces Sparse Expert Activation Pruning (SEAP), a training-free pruning method that selectively retains task-relevant parameters to reduce inference overhead. Inspired by the clustering patterns of hidden states and activations in LLMs, SEAP identifies task-specific expert activation patterns and prunes the model while preserving task performance and enhancing computational efficiency. Experimental results demonstrate that SEAP significantly reduces computational overhead while maintaining competitive accuracy. Notably, at 50% pruning, SEAP surpasses both WandA and FLAP by over 20%, and at 20% pruning, it incurs only a 2.2% performance drop compared to the dense model. These findings highlight SEAP's scalability and effectiveness, making it a promising approach for optimizing large-scale LLMs.