Adapter Tuning

Adapter tuning is a parameter-efficient fine-tuning technique for large pre-trained models, aiming to adapt them to specific downstream tasks while minimizing computational cost and storage requirements. Current research focuses on developing novel adapter architectures, such as those employing low-rank matrices, Hadamard products, Fourier transforms, or sparse structures, often integrated into transformer-based models. These methods demonstrate significant improvements in efficiency, achieving performance comparable to full fine-tuning with a drastically reduced number of trainable parameters, impacting various fields including natural language processing, computer vision, and speech processing. The resulting efficiency gains make deploying and adapting large models more feasible for resource-constrained environments and a wider range of applications.