Generalized Knowledge

Generalized knowledge research aims to develop methods for machines to learn and apply knowledge across diverse and unseen situations, improving the robustness and adaptability of AI systems. Current efforts focus on techniques like multi-view knowledge fusion, prompting to simulate generalized knowledge, and selective cross-task distillation, often employing neural networks and leveraging pre-trained models to enhance knowledge transfer and reduce catastrophic forgetting. This research is crucial for advancing AI capabilities in areas such as federated learning, recommendation systems, and few-shot learning, ultimately leading to more efficient and effective AI applications.