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LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
March 15, 2024, 4:41 a.m. | Yichao Wu, Yafei Xiang, Shuning Huo, Yulu Gong, Penghao Liang
cs.LG updates on arXiv.org arxiv.org
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 …
abstract adaptability arxiv computational cs.cl cs.lg fine-tuning framework knowledge language language models large language large language models llms lora low low-rank adaptation memory novel retention through type
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