Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration

This research introduces CURE, a novel framework designed to improve the factuality of long-form generation by LLMs by teaching them to reason about uncertainty at the claim level. It aims to overcome the limitation of models often stating incorrect claims confidently, focusing instead on granular uncertainty calibration.