Can Pruning Make Large Language Models More Efficient?

Abstract

Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships. However, the vast number of parameters in these models has raised concerns regarding computational efficiency, environmental impact, and deployability on resource-limited platforms. To address these challenges, this chapter investigates the application of weight pruning—a strategic reduction of model parameters based on their significance—as an optimization strategy for transformer architectures. Through extensive experimentation, the authors explore various pruning methodologies, highlighting their impact on model performance, size, and computational demands.

Topics & Concepts