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UAG is a novel multi-hop knowledge graph reasoning framework that incorporates uncertainty quantification (UQ) into its reasoning process. It combines KG-based candidate retrieval and path formulation with LLM-based candidate evaluation and answer generation, all controlled by a global error rate controller using the Learn-Then-Test framework. This synergizes LLMs and KGs for trustworthy reasoning.
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