Incremental Knowledge

Incremental knowledge learning focuses on enabling machine learning models, particularly large language models (LLMs), to continuously acquire and integrate new information without catastrophic forgetting of previously learned knowledge. Current research emphasizes techniques like retrieval-augmented generation (RAG), mixture-of-experts (MoE) adaptors, and methods to mitigate "neighboring perturbations" during knowledge updates, often employing strategies such as prompt engineering and knowledge distillation. This field is crucial for developing more robust and adaptable AI systems capable of handling the ever-growing volume of information and maintaining accuracy in dynamic environments, with applications ranging from cybersecurity to robotics and semi-supervised learning.