ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
This work addresses the need for more flexible parameter-efficient fine-tuning in large language models, offering an incremental improvement over LoRA for downstream tasks.
The authors tackled the problem of fixed intrinsic rank in Low-Rank Adaptation (LoRA) for fine-tuning large language models by proposing ALoRA, which dynamically adjusts rank allocation, and it outperformed recent baselines with comparable tunable parameters.
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.