Low-Rank Adaptation for Foundation Models: A Comprehensive Review

TL;DR

This paper provides a comprehensive review of Low-Rank Adaptation (LoRA), a parameter-efficient method for fine-tuning large foundation models across various domains, highlighting recent techniques, applications, challenges, and future directions.

Contribution

It is the first survey to extensively review LoRA techniques beyond large language models, covering recent developments, applications, and future research challenges.

Findings

LoRA significantly reduces computational costs in model fine-tuning.

It enables effective adaptation of foundation models across multiple domains.

The survey identifies key challenges and future research directions in LoRA.

Abstract

The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advancements across domains such as natural language processing, computer vision, and scientific discovery. However, the substantial parameter count of these models, often reaching billions or trillions, poses significant challenges in adapting them to specific downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a highly promising approach for mitigating these challenges, offering a parameter-efficient mechanism to fine-tune foundation models with minimal computational overhead. This survey provides the first comprehensive review of LoRA techniques beyond large Language Models to general foundation models, including recent techniques foundations, emerging frontiers and applications of low-rank adaptation across multiple domains. Finally, this survey discusses key challenges and future research directions in theoretical understanding, scalability, and robustness. This survey serves as a valuable resource for researchers and practitioners working with efficient foundation model adaptation.