Parameter Efficient Adapter
Parameter-efficient adapters are lightweight neural network modules designed to adapt large pre-trained models to new tasks with minimal parameter updates, thus reducing computational cost and storage requirements. Research focuses on optimizing adapter architectures, exploring techniques like low-rank matrices, Hadamard products, and hypernetworks to achieve extreme parameter efficiency while maintaining performance comparable to full fine-tuning. These methods are significantly impacting various fields, enabling efficient transfer learning in natural language processing, computer vision, and other domains where adapting large models to specific tasks is crucial. The development of more efficient and adaptable adapters continues to be a key area of investigation.