# A Survey on LoRA of Large Language Models

**Authors:** Yuren Mao, Yuhang Ge, Yijiang Fan, Wenyi Xu, Yu Mi, Zhonghao Hu, Yunjun Gao  
**arXiv:** [2407.11046](https://arxiv.org/abs/2407.11046)  
**Date:** 2024-07-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2407.11046v1/

## Key points

- Large Language Models (LLMs) have seen a rapid increase in parameter scales, leading to enhanced generalization abilities.
- LLMs still face limitations in certain downstream tasks due to knowledge boundaries.
- Fine-tuning LLMs on specific downstream tasks is essential for overcoming these challenges.
- Traditional full fine-tuning of LLMs involves adjusting all parameters, which can be computationally expensive and memory-intensive.
- LoRA has emerged as a promising approach for parameter-efficient fine-tuning by updating dense neural network layers with pluggable low-rank matrices.
- LoRA offers advantages in cross-task generalization and privacy preservation.
- The growing attention towards LoRA is evident from the exponential increase in related literature.
- Advancements in several key areas include techniques that enhance LoRA's performance on specific tasks, combining multiple LoRA plugins for broader applicability, enhancing computation efficiency, leveraging LoRA in federated learning, and exploring real-world applications.
- The survey discusses future directions for research and development in the field of LoRA for Large Language Models.

## Summary

In recent years, the rapid increase in parameter scales of pre-training language models has led to the emergence of Large Language Models (LLMs) with enhanced generalization abilities. However, despite their large parameter scales, LLMs still face limitations in certain downstream tasks due to knowledge boundaries. To overcome this challenge, fine-tuning LLMs on specific downstream tasks is essential. Traditional full fine-tuning of LLMs involves adjusting all parameters, which is computationally expensive and memory-intensive. For instance, full fine-tuning of a model like LLaMA2-7B requires significant resources. have seen a rapid increase in parameter scales in recent years leading to enhanced generalization abilities. However, they still face limitations in certain downstream tasks due to knowledge boundaries. To overcome this challenge,  on specific downstream tasks is essential. This process typically involves adjusting all parameters through traditional full fine-tuning methods which can be computationally expensive and memory-intensive. For example, of a model like LLaMA2-7B requires significant resources. To address these challenges and improve efficiency, has emerged as a promising approach for parameter-efficient . LoRA updates dense neural network layers with pluggable low-rank matrices, offering advantages in cross-task generalization and privacy preservation. The growing attention towards LoRA is evident from the exponential increase in related literature. To provide a comprehensive overview of the current progress on LoRA, categorizes and reviews advancements in several key areas:  that enhance LoRA's performance on specific tasks;  that combine multiple LoRA plugins for broader applicability;  to enhance computation efficiency;  leveraging LoRA in federated learning; and real-world applications. Moreover, the survey discusses future directions for research and development in the field of LoRA for Large Language Models. By exploring these perspectives and advancements, researchers can gain insights into optimizing fine-tuning processes for LLMs while addressing computational challenges and ensuring privacy protection.