Multi Task Transfer

Multi-task transfer learning aims to improve model performance on a target task by leveraging knowledge gained from training on related source tasks. Current research focuses on mitigating "negative transfer" – where source tasks hinder target task performance – through architectural innovations like modular networks (e.g., mixtures of experts) and task-specific adapters within transformer models. These approaches, often employing parameter-efficient fine-tuning techniques, seek to balance the benefits of shared knowledge with the need for task-specific adaptation, leading to improved efficiency and generalization across diverse tasks and domains.