Low-Rank Adaptation for Scalable Fine-Tuning of Pre-Trained Language Models

Low-Rank Adaptation (LoRA) is a computationally efficient approach for fine-tuning large pre-trained language models, designed to reduce memory and computational overhead by introducing low-rank matrices into the model’s weight updates. This survey provides a comprehensive overview of LoRA, including its theoretical foundations, applications, and the advantages it offers over traditional fine-tuning methods. We explore how LoRA enables efficient task adaptation in scenarios such as domain adaptation, few-shot learning, transfer learning, and zero-shot learning. Additionally, we examine its challenges, such as rank selection, generalization to complex tasks, and risks of overfitting, while identifying key areas for future research, including adaptive rank selection, integration with other fine-tuning techniques, and multi-modal and cross-domain adaptation. LoRA's potential to make large-scale models more adaptable and efficient is significant for advancing natural language processing (NLP) and machine learning applications, especially when computational resources are limited. This survey aims to highlight the current state of LoRA, its practical applications, and the ongoing research opportunities to further enhance its capabilities.