This research introduces a novel contextual fine-tuning approach that mimics human learning processes to improve how Large Language Models acquire new knowledge.

• Models trained with this method show enhanced ability to learn from context
• Bridges the gap between standard fine-tuning and in-context learning
• Demonstrates improved performance in rapidly evolving domains including medicine
• Enables more efficient knowledge updating without complete retraining

Medical Impact: In the medical domain, where knowledge evolves quickly, this approach allows LLMs to more effectively incorporate new research findings, treatment protocols, and diagnostic information—potentially improving healthcare decision support while reducing the need for constant full-scale model retraining.

Teaching LLMs How to Learn with Contextual Fine-Tuning