Cross Task Knowledge

Cross-task knowledge transfer aims to improve machine learning model efficiency and robustness by leveraging knowledge learned from one task to enhance performance on others. Current research focuses on developing methods for effective knowledge sharing across tasks, employing techniques like multi-task learning, prompt tuning, and meta-learning, often within architectures such as Transformers and Mixture-of-Experts models. This research is significant because it addresses the limitations of training separate models for each task, leading to more efficient and adaptable AI systems with applications across diverse fields like natural language processing and computer vision. The resulting models demonstrate improved performance and generalization capabilities, particularly in low-data regimes.