C-MIG uses multi-view information gain from retrieved documents and refinements to supervise RAG-RL for clinical diagnosis, claiming top performance on four medical benchmarks.
We uniformly sample 8,998 instances for training and 1,003 instances for validation from the original dataset
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C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
C-MIG uses multi-view information gain from retrieved documents and refinements to supervise RAG-RL for clinical diagnosis, claiming top performance on four medical benchmarks.