LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models
<|reference_start|>LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models: 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<|reference_end|>
@article{zhang2024lora$^2$,
    title={LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large
  Language Models},
    author={Jia-Chen Zhang, Yu-Jie Xiong, He-Xi Qiu, Dong-Hai Zhu, Chun-Ming Xia},
    journal={arXiv preprint arXiv:2408.06854},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06854},
    primaryClass={cs.CL}
}