Task Specific Adapter

Task-specific adapters are small, specialized modules added to pre-trained large language models (LLMs) or other deep learning models to efficiently adapt them to new tasks without retraining the entire model. Current research focuses on improving adapter design for various applications, including computer vision (e.g., object detection, segmentation), natural language processing (e.g., question answering, machine translation), and even biomedical applications (e.g., disease diagnosis). This approach offers significant advantages in terms of computational efficiency, memory usage, and the ability to handle continual learning scenarios, making it a valuable tool for researchers and practitioners working with large models across diverse domains.