Adapter Fusion

Adapter fusion is a parameter-efficient fine-tuning technique for large language and vision models, aiming to adapt pre-trained models to specific tasks without retraining the entire network. Current research focuses on improving adapter architectures (e.g., sparse high-rank adapters, dual-path adapters), developing efficient merging strategies for multiple adapters, and applying these methods to diverse applications like image recognition, question answering, and time series analysis. This approach offers significant advantages in terms of computational cost and storage, making it particularly valuable for resource-constrained environments and facilitating the development of more customized and adaptable AI models.