Multi Task Transformer

Multi-task transformers leverage the power of transformer architectures to simultaneously learn multiple related tasks from a shared input, improving efficiency and performance compared to training separate models for each task. Current research focuses on enhancing these models through techniques like parameter-efficient fine-tuning, incorporating auxiliary tasks (e.g., contrastive learning), and designing specialized attention mechanisms to manage task interactions. This approach is proving valuable across diverse fields, including medical image analysis, natural language processing, and robotics, by enabling more efficient training and improved generalization across various sub-tasks within a given domain.