# 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/2308.07633v1/

## Key points

- Large Language Models (LLMs) have demonstrated remarkable success in various NLP tasks but face challenges due to their large size and computational demands.
- Model compression has emerged as a crucial area of research to address the limitations of LLMs in resource-constrained environments.
- The paper provides a comprehensive survey on model compression techniques tailored for LLMs, including quantization, pruning, knowledge distillation, and more.
- Recent advancements and innovative approaches in model compression contribute to the evolving landscape of LLM research.
- Benchmarking strategies and evaluation metrics are discussed as essential for assessing the effectiveness of compressed LLMs.
- The paper serves as a valuable resource for both researchers and practitioners aiming to enhance efficiency and real-world applicability of LLMs.
- The insights provided by the authors offer a roadmap for navigating complexities in model compression for large language models and pave the way for further innovations in NLP technology.

## Summary

In their paper titled "A Survey on Model Compression for Large Language Models," authors Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang explore the challenges and opportunities surrounding Large Language Models (LLMs) in natural language processing. LLMs have demonstrated remarkable success in various NLP tasks but face obstacles due to their large size and computational demands, particularly in resource-constrained environments. To address these limitations, the field of model compression has emerged as a crucial area of research. The authors provide a comprehensive survey that delves into various model compression techniques tailored specifically for LLMs. They discuss methodologies such as quantization, pruning, knowledge distillation, and more, highlighting recent advancements and innovative approaches 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 paper serves as a valuable resource for both researchers and practitioners. As LLMs continue to evolve, this survey aims to facilitate enhanced efficiency and real-world applicability while establishing a foundation for future advancements in the field. The insights provided by the authors offer a roadmap for navigating the complexities of model compression in large language models and pave the way for further innovations in natural language processing technology.