Domain Specific Adapter

Domain-specific adapters are lightweight modules added to pre-trained models to efficiently adapt them to new domains without retraining the entire model. Current research focuses on developing effective methods for selecting, combining, and training these adapters, including techniques like low-rank adaptation (LoRA), mixture-of-experts models, and weight averaging across multiple adapters. This approach offers significant advantages in terms of computational efficiency and resource management, impacting various fields by enabling the customization of large language models, image generation models, and other deep learning architectures for specialized tasks with limited data.