Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular and representative method. However, it is implemented with a fixed intrinsic rank that might not be the ideal setting for the downstream tasks. Recognizing the need for more flexible downstream task adaptation, we extend the methodology of LoRA to an innovative approach we call allocating low-rank adaptation (ALoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. First, we propose a novel method, AB-LoRA, that can effectively estimate the importance score of each LoRA rank. Second, guided by AB-LoRA, we gradually prune abundant and negatively impacting LoRA ranks and allocate the pruned LoRA budgets to important Transformer modules needing higher ranks. We have conducted experiments on various tasks, and the experimental results demonstrate that our ALoRA method can outperform the recent baselines with comparable tunable parameters.

通过 AB-LoRA 方法，逐步修剪过多和负面影响的 LoRA 排名，并将修剪后的 LoRA 预算分配给需要更高排名的重要 Transformer 模块，实现了分配低秩适应 (ALoRA) 的灵活下游任务适应方法。实验结果表明，ALoRA 方法在可调参数相当的情况下优于最近的基准模型。

ALoRA: 为大型语言模型调整低秩适应