Efficient Fine-Tuning Strategies for Large Language Models Using Low-Rank Adaptation Techniques

Abstract

The advent of large language models (LLMs) has revolutionized natural language processing by providing pre-trained models capable of a wide range of tasks. However, fine-tuning these models to specific tasks remains computationally expensive and resource-intensive. This paper explores efficient fine-tuning strategies for LLMs by leveraging low-rank adaptation (LoRA) techniques. We investigate how LoRA reduces the computational burden while preserving model performance, offering practical benefits for deploying LLMs in real-world applications. Experimental results demonstrate that LoRA achieves competitive performance with significantly lower computational and memory costs compared to traditional fine-tuning methods.