Multi Task

Multi-task learning (MTL) aims to improve model efficiency and performance by training a single model to handle multiple related tasks simultaneously. Current research focuses on developing effective strategies for sharing information between tasks, including novel architectures like multi-expert systems and the adaptation of large language models (LLMs) for various applications. This approach is particularly valuable in scenarios with limited data or computational resources, finding applications in diverse fields such as medical image analysis, robotics, and online advertising, where improved efficiency and generalization are crucial.