# Compressing Large Language Models by Streamlining the Unimportant Layer

**Authors:** Xiaodong Chen, Yuxuan Hu, Jing Zhang  
**arXiv:** [2403.19135](https://arxiv.org/abs/2403.19135)  
**Date:** 2024-03-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.19135v1/

## Key points

- Large language models (LLM) are widely used in natural language processing but face challenges due to their large number of parameters.
- Researchers Xiaodong Chen, Yuxuan Hu, and Jing Zhang developed LLM-Streamline to address this challenge.
- LLM-Streamline involves layer pruning and layer replacement to create more compact models with high performance.
- In layer pruning, less important layers are systematically removed based on a target sparsity level to streamline the model.
- Layer replacement involves training lightweight models like multi-layer perceptrons (MLPs) or transformer layers to compensate for the pruned layers' functions.
- Extensive experimentation showed that even a single MLP could effectively fit into pruned layers while maintaining high performance levels.
- LLM-Streamline outperformed existing model pruning methods in terms of efficiency and effectiveness.

## Summary

In the realm of natural language processing, large language models (LLM) have become a cornerstone in various linguistic tasks and domains. However, their widespread application is often hindered by the sheer magnitude of parameters these models possess. As a result, there has been a growing interest in developing more compact models that can deliver high performance without the excessive computational burden. A recent study conducted by Xiaodong Chen, Yuxuan Hu, and Jing Zhang delves into this challenge by exploring the nuanced impact different layers within LLM have on the hidden states of the model. By recognizing that certain layers contribute less significantly to overall performance, the researchers introduce a novel approach known as LLM-Streamline. This method comprises two key components: layer pruning and layer replacement. In the process of layer pruning, consecutive layers with the lowest importance are systematically removed based on a predefined target sparsity level. This strategic elimination helps streamline the model by discarding redundant or less impactful components. To counteract any potential drop in performance resulting from pruning, the researchers implement layer replacement. Here, a lightweight model such as a multi-layer perceptron (MLP) or transformer layer is trained to take over the functions of the pruned layers, effectively compensating for any loss in functionality. Through extensive experimentation and evaluation using various lightweight structures like MLPs and transformer layers, it was demonstrated that even a single MLP could adeptly fit into the pruned layers while maintaining high performance levels. The results showcased that LLM-Streamline surpassed existing state-of-the-art model pruning methods in terms of efficiency and effectiveness. Overall, this research not only sheds light on the importance of identifying and optimizing less critical components within large language models but also presents a practical solution in LLM-Streamline for creating more streamlined and efficient models with superior performance capabilities in natural language processing tasks.