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

In three linesDoublyCal, a framework to improve LLM reliability by combining Knowledge Graphs and uncertainty calibration. A lightweight proxy model generates KG evidence with calibrated confidence, guiding a black-box LLM toward more accurate and well-calibrated predictions. Tested on knowledge-intensive benchmarks with reduced token costs.

Summary generated by Claude — human-verified