Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence

In three linesDoublyCal, a double-calibration framework, improves LLM reliability by quantifying epistemic uncertainty in retrieved evidence and reasoning. A lightweight proxy model generates Knowledge Graph evidence with calibrated confidence, guiding a black-box LLM toward more accurate and well-calibrated predictions.

Summary generated by Claude — human-verified