LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient
  Fine-Tuning of Large Language Models
Yichao Wu Yafei Xiang Shuning Huo Yulu Gong Penghao Liang
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.