Attention Based Adapter

Attention-based adapters are lightweight modules added to large pre-trained models to efficiently adapt them to new tasks or datasets, minimizing computational costs and communication overhead. Current research focuses on developing efficient adapter architectures, often employing attention mechanisms, for various applications including federated learning and natural language processing. This approach offers significant advantages in resource-constrained environments and enables personalized model adaptation, impacting fields like computer vision and natural language understanding by improving both efficiency and performance.