Prompt Based Continual Learning

Prompt-based continual learning (PCL) aims to enable artificial intelligence models to learn new tasks sequentially without forgetting previously acquired knowledge, leveraging the power of pre-trained models and minimizing the need for data storage. Current research focuses on improving prompt selection and management strategies, often employing techniques like clustering, orthogonal projections, and generative replay to enhance knowledge retention and transfer. This approach offers significant advantages in terms of efficiency and data privacy, with potential applications in areas requiring continuous adaptation to new information streams, such as personalized medicine and robotics.