LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models

• URL: http://arxiv.org/abs/2408.06854v1
• Date: Tue, 13 Aug 2024 12:31:30 GMT
• Title: LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models
• Authors: Jia-Chen Zhang, Yu-Jie Xiong, He-Xi Qiu, Dong-Hai Zhu, Chun-Ming Xia,
• Abstract summary: Low-Rank Adaptation (LoRA) significantly reduces the number of trainable parameters for fine-tuning. We extend the LoRA to multiple scales, dubbed as LoRA$2$.
• Score: 3.7049613588433497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning large language models (LLMs) with high parameter efficiency for downstream tasks has become a new paradigm. Low-Rank Adaptation (LoRA) significantly reduces the number of trainable parameters for fine-tuning. Although it has demonstrated commendable performance, updating parameters within a single scale may not be the optimal choice for complex downstream tasks.In this paper, we extend the LoRA to multiple scales, dubbed as LoRA$^2$. We first combine orthogonal projection theory to train a set of LoRAs in two mutually orthogonal planes. Then, we improve the importance score algorithm, which reduce parameter sensitivity score calculations by approximately 98.5\%. By pruning singular values with lower importance scores, thereby enhancing adaptability to various downstream tasks. Extensive experiments are conducted on two widely used pre-trained models to validate the effectiveness of LoRA$^2$. Results show that it significantly reduces the number of trainable parameters to just 0.72\% compared to full fine-tuning, while still delivering highly impressive performance. Even when the parameters are further reduced to 0.17M, it still achieves comparable results to the baseline with 8 times more parameters. Our code is available here: https://anonymous.4open.science/r/LoRA-2-5B4C

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