# A Survey on Model Compression for Large Language Models

**Authors:** Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang  
**arXiv:** [2308.07633](https://arxiv.org/abs/2308.07633)  
**Date:** 2023-08-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.07633v2/

## Key points

- Large Language Models (LLMs) have revolutionized natural language processing tasks
- LLMs present challenges for practical deployment in resource-constrained environments
- Model compression has emerged as a pivotal research area to alleviate these limitations
- The paper titled "A Survey on Model Compression for Large Language Models" provides a comprehensive survey of model compression techniques tailored specifically for LLMs
- The authors explore methodologies such as quantization, pruning, and knowledge distillation
- Recent advancements and innovative approaches within each technique are highlighted
- Benchmarking strategies and evaluation metrics are discussed to assess the effectiveness of compressed LLMs
- The survey serves as an invaluable resource for researchers and practitioners in the field of LLMs
- It aims to enhance efficiency and real-world applicability while establishing a foundation for future advancements.

## Summary

Large Language Models (LLMs) have revolutionized natural language processing tasks with remarkable success. However, their formidable size and computational demands present significant challenges for practical deployment, especially in resource-constrained environments. As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations. In this paper titled "A Survey on Model Compression for Large Language Models," authors Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang present a comprehensive survey that navigates the landscape of model compression techniques tailored specifically for LLMs. The authors address the imperative need for efficient deployment by delving into various methodologies encompassing quantization, pruning, knowledge distillation, and more. The survey highlights recent advancements and innovative approaches within each of these techniques that contribute to the evolving landscape of LLM research. By exploring benchmarking strategies and evaluation metrics essential for assessing the effectiveness of compressed LLMs, the authors provide insights into the latest developments and practical implications. This survey serves as an invaluable resource for both researchers and practitioners in the field of LLMs. It aims to facilitate enhanced efficiency and real-world applicability while establishing a foundation for future advancements. As LLMs continue to evolve, this survey provides valuable guidance to overcome challenges related to their size and computational demands.