Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices

• URL: http://arxiv.org/abs/2309.02411v1
• Date: Tue, 5 Sep 2023 17:40:34 GMT
• Title: Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices
• Authors: Bojia Zi, Xianbiao Qi, Lingzhi Wang, Jianan Wang, Kam-Fai Wong, Lei Zhang
• Abstract summary: Delta-LoRA is a novel parameter-efficient approach to fine-tune large language models (LLMs) In contrast to LoRA and other low-rank adaptation methods such as AdaLoRA, Delta-LoRA not only updates the low-rank matrices $bA$ and $bB$, but also propagate the learning to the pre-trained weights $bW$.
• Score: 27.693028578653394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune large language models (LLMs). In contrast to LoRA and other low-rank adaptation methods such as AdaLoRA, Delta-LoRA not only updates the low-rank matrices $\bA$ and $\bB$, but also propagate the learning to the pre-trained weights $\bW$ via updates utilizing the delta of the product of two low-rank matrices ($\bA^{(t+1)}\bB^{(t+1)} - \bA^{(t)}\bB^{(t)}$). Such a strategy effectively addresses the limitation that the incremental update of low-rank matrices is inadequate for learning representations capable for downstream tasks. Moreover, as the update of $\bW$ does not need to compute the gradients of $\bW$ and store their momentums, Delta-LoRA shares comparable memory requirements and computational costs with LoRA. Extensive experiments show that Delta-LoRA significantly outperforms existing low-rank adaptation methods. We further support these results with comprehensive analyses that underscore the effectiveness of Delta-LoRA.

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