Multi Task Model

Multi-task models aim to train a single model capable of performing multiple tasks simultaneously, improving efficiency and generalization compared to training separate models for each task. Current research focuses on developing effective architectures and algorithms, including transformer-based models, mixture-of-experts, and various model merging techniques like task arithmetic and weight averaging, to address challenges such as catastrophic forgetting and representation bias. This field is significant because it offers improved resource utilization and enhanced performance across diverse applications, ranging from medical image analysis and natural language processing to robotics and recommender systems.