This research introduces a novel approach to help large language models better handle specialized knowledge that appears rarely in training data.

Key Findings:

• Introduces Dynamic Uncertainty Ranking to improve retrieval-augmented in-context learning
• Enhances LLM performance on specialized domain questions without extensive retraining
• Reduces reliance on pre-trained data memorization for answering long-tail questions
• Demonstrates practical applications across specialized domains including medical knowledge

Medical Significance: This approach enables more reliable medical information retrieval in LLMs by dynamically identifying and addressing uncertainty in specialized medical knowledge—critical for healthcare applications where accuracy and comprehensive knowledge are paramount.

Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs