LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
<|reference_start|>LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models: 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.<|reference_end|>
@article{wu2024lora-sp:,
    title={LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient
  Fine-Tuning of Large Language Models},
    author={Yichao Wu, Yafei Xiang, Shuning Huo, Yulu Gong, Penghao Liang},
    journal={arXiv preprint arXiv:2403.08822},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08822},
    primaryClass={cs.LG cs.CL}
}