Meta Task

Meta-tasks represent a powerful approach to improve machine learning model performance, particularly in few-shot learning scenarios. Research focuses on structuring complex problems into hierarchical sub-tasks (meta-tasks) to enhance generalization and efficiency, often employing techniques like contrastive learning and task-adaptive modules within various model architectures, including graph neural networks and large language models. This approach addresses limitations in existing methods by improving feature extraction, data allocation, and robustness, leading to significant performance gains in diverse applications such as time series prediction, node classification, and intelligent agent planning. The resulting improvements in model generalization and efficiency have broad implications across various machine learning domains.