Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
This work addresses a bottleneck in fine-tuning large pre-trained models for downstream tasks, offering a simple, cost-free enhancement that is incremental but broadly applicable.
The paper tackles the limited capacity of parameter-efficient fine-tuning methods under constrained parameter budgets by proposing CapaBoost, a strategy that enhances model capacity through low-rank updates and static random masks, achieving significant improvements on diverse downstream tasks without extra costs.
Fine-tuning large pre-trained foundation models, such as the 175B GPT-3, has attracted more attention for downstream tasks recently. While parameter-efficient fine-tuning methods have been proposed and proven effective without retraining all model parameters, their performance is limited by the capacity of incremental modules, especially under constrained parameter budgets. \\ To overcome this challenge, we propose CapaBoost, a simple yet effective strategy that enhances model capacity by leveraging low-rank updates through parallel weight modules in target layers. By applying static random masks to the shared weight matrix, CapaBoost constructs a diverse set of weight matrices, effectively increasing the rank of incremental weights without adding parameters. Notably, our approach can be seamlessly integrated into various existing parameter-efficient fine-tuning methods. We extensively validate the efficacy of CapaBoost through experiments on diverse downstream tasks, including natural language understanding, question answering, and image classification. Our results demonstrate significant improvements over baselines, without incurring additional computation or storage costs. Our code is available at \url{https://github.com/LINs-lab/CapaBoost}.