LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models

TL;DR

LoRA-SP introduces a randomized partial parameter adaptation method that significantly reduces resource usage during fine-tuning of large language models while maintaining competitive performance.

Contribution

It presents a novel randomized parameter freezing technique within the LoRA framework, enhancing resource efficiency in large language model fine-tuning.

Findings

Achieves competitive NLP task performance with lower resource consumption.

Reduces computational and memory requirements compared to full fine-tuning.

Enables deployment of large models in resource-limited environments.

Abstract

In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utilizing randomized half-selective parameter freezing within the Low-Rank Adaptation(LoRA)framework. This method efficiently balances pre-trained knowledge retention and adaptability for task-specific optimizations. Through a randomized mechanism, LoRA-SP determines which parameters to update or freeze, significantly reducing computational and memory requirements without compromising model performance. We evaluated LoRA-SP across several benchmark NLP tasks, demonstrating its ability to achieve competitive performance with substantially lower resource consumption compared to traditional full-parameter fine-tuning and other parameter-efficient techniques. LoRA-SP innovative approach not only facilitates the deployment of advanced NLP models in resource-limited settings but also opens new research avenues into effective and efficient model adaptation strategies.