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

• URL: http://arxiv.org/abs/2403.08822v1
• Date: Wed, 28 Feb 2024 06:50:10 GMT
• Title: LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
• Authors: Yichao Wu, Yafei Xiang, Shuning Huo, Yulu Gong, Penghao Liang,
• Abstract summary: LoRA-SP is a novel approach utilizing randomized half-selective parameter freezing. LoRA-SP significantly reduces computational and memory requirements without compromising model performance.
• Score: 7.926974917872204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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