RGAR (Recurrence Generation-augmented Retrieval) introduces a novel framework that improves factual accuracy in medical question answering by enhancing how LLMs retrieve specialized knowledge.

• Addresses limitations in existing Retrieval-Augmented Generation (RAG) methods by prioritizing factual knowledge
• Uses a recurrence-based approach to iteratively refine information retrieval for medical queries
• Demonstrated superior performance on medical QA benchmarks by improving relevance of retrieved medical concepts
• Enables more reliable clinical decision support based on Electronic Health Records

This research matters because accurate medical information retrieval is critical for patient care, clinical decision-making, and reducing diagnostic errors in healthcare applications of AI.

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering