Continual Lifelong Learning in Foundation Models: Mitigating Catastrophic Forgetting, Model Editing, and Dynamic Modular Expansion
Abstract—Modern foundation models, particularly autoregressive Large Language Models (LLMs) and Vision-Language Models (VLMs), operate predominantly under a static pre-train-then-freeze deployment paradigm. However, real-world enterprise deployments demand continuous, lifelong adaptation to non-stationary data streams:...
Satyam Kumar, Antu Kumar, Vishal Shrivastava et al.
· Zenodo (CERN European Organi... · 0 citations