AI RESEARCH
Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
arXiv CS.CL
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ArXi:2601.11956v2 Announce Type: replace Reliable reasoning in Large Language Models (LLMs) is challenged by their propensity for hallucination. While augmenting LLMs with Knowledge Graphs (KGs) improves factual accuracy, existing KG-augmented methods fail to quantify epistemic uncertainty in both the retrieved evidence and LLMs' reasoning. To bridge this gap, we