Continual Learning in Large Language Models: Moving Beyond In-Context Learning

This article examines why large language models need continual learning capabilities to update their parameters after deployment, rather than relying solely on in-context learning. It compares current LLM limitations to the memory disorder in Christopher Nolan's Memento and argues that parametric learning through continual learning is essential for genuine discovery, adversarial scenarios, and tacit knowledge acquisition.