Continual Knowledge Learning

Continual knowledge learning (CKL) addresses the challenge of training machine learning models, particularly large language models, to continuously acquire new knowledge without forgetting previously learned information. Current research focuses on developing methods to mitigate "catastrophic forgetting," often employing techniques like meta-learning to selectively update model parameters, incorporating structural knowledge to guide learning, and leveraging external knowledge sources like the web for dynamic updates. This field is crucial for building adaptable and robust AI systems capable of handling the ever-changing nature of real-world data, with significant implications for applications requiring up-to-date knowledge and ongoing learning.