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Forget painstakingly annotating sensitive patient data: this work shows you can train models to extract complex clinical information with surprisingly high accuracy using only synthetic data.
Substantially improve the factual accuracy of dialogue LLMs by iteratively refining responses at the atomic unit level, verifying each unit against external knowledge.