Efficient Adapter

Efficient adapters are lightweight neural network modules designed to adapt large pre-trained models to new tasks without retraining the entire model, thus saving computational resources and time. Current research focuses on improving adapter architectures, such as exploring different adapter placements within the model (e.g., parallel adapters) and incorporating mechanisms like attention or graph convolutions to enhance performance, particularly in challenging domains like point cloud processing and speech recognition. While some studies highlight the potential for significant efficiency gains, others emphasize that the benefits of adapters over full fine-tuning are task-dependent and may not always translate to practical advantages in terms of training speed or deployment latency, especially for Natural Language Understanding tasks.