The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques. We propose a novel pruning method utilising centrality measures from graph theory, reducing both the computational requirements and the memory footprint of these models. Specifically, we devise a method for creating a weighted directed acyclical graph representation of multilayer perceptrons to which we apply a modified version of the weighted PageRank centrality measure to compute node importance scores. In combination with uniform pruning this leads to structured sparsity. We call this pruning method MLPRank. Furthermore we introduce an extension to decoder-only transformer models and call it LLMRank. For both variants we demonstrate a strong performance. With MLPRank on average leading to 6.09 % higher accuracy retention than three popular baselines and 13.42 % with LLMRank compared to two popular baselines.

本研究解决了大型语言模型在部署时面临的尺寸和成本问题，提出了一种利用图论中心性度量的创新剪枝方法。该方法通过创建加权有向无环图并应用加权PageRank中心性度量计算节点重要性，从而实现更高的精度保留，MLPRank和LLMRank表现出比传统方法更优的性能。

LLM-Rank：一种图论方法用于剪枝大型语言模型