# A Comprehensive Survey of Compression Algorithms for Language Models

**Authors:** Seungcheol Park, Jaehyeon Choi, Sojin Lee, U Kang  
**arXiv:** [2401.15347](https://arxiv.org/abs/2401.15347)  
**Date:** 2024-01-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.15347v1/

## Key points

- Authors explore the challenge of compressing language models without sacrificing accuracy
- Recent advancements in language models have led to increased size, causing issues such as carbon emissions and expensive maintenance fees
- Numerous compression algorithms have been developed to address this problem
- Excessive number of compression algorithms makes it challenging to capture emerging trends and understand fundamental concepts
- Survey conducted to provide comprehensive summary of diverse compression algorithms
- Techniques covered include pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design
- Representative compression algorithms selected for in-depth analysis
- Value of each category of compression algorithms discussed
- Desired properties of low-cost compression algorithms highlighted
- Promising future research topics introduced based on survey results

## Summary

, , , , 

In their paper titled "A Comprehensive Survey of Compression Algorithms for Language Models," authors Seungcheol Park, Jaehyeon Choi, Sojin Lee, and U Kang explore the challenge of compressing language models without sacrificing accuracy. With recent advancements in language models, their size has become gigantic, leading to issues such as increased carbon emissions and expensive maintenance fees. To address this problem, numerous compression algorithms have been developed. However, the authors note that the excessive number of compression algorithms makes it challenging to capture emerging trends and understand the fundamental concepts underlying them. In response, they conduct a survey and provide a comprehensive summary of diverse compression algorithms. The paper covers various techniques including pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design. The authors not only summarize the overall trend of these compression algorithms but also select representative ones for in-depth analysis. Additionally, they discuss the value of each category of compression algorithms and highlight the desired properties of low-cost compression algorithms that can have a significant impact on large language models. Finally, based on their survey results, they introduce promising future research topics in this field. Overall,<fd> this paper serves as a valuable resource for understanding different compression algorithms for language models </fd>and provides insights into their potential applications and future directions.