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

This research improves factuality in long-form language model generation through reasoning calibration — a method for reducing hallucinations in extended text outputs. The approach calibrates the model's underlying reasoning to maintain accuracy across longer sequences, addressing a key limitation in current language models.