CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.
Goal Produce a confidence-calibrated, internally consistent revision in which: •Confidence justifications throughly analyze the model's certainty for each claim
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration
CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.