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When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation

A framework that calibrates knowledge graph retrieval-augmented generation models.

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When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation

By Jing Ren, Bowen Li, Ziqi Xu, Xikun Zhang, Haytham M. Fayek, Xiaodong Li
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The paper proposes Ca2KG, a causality-aware calibration framework for KG-RAG. It integrates counterfactual prompting and a panel-based re-scoring mechanism to improve calibration while maintaining predictive accuracy. Experiments on two QA datasets demonstrate the effectiveness of Ca2KG.

Abstract

The paper proposes Ca2KG, a causality-aware calibration framework for KG-RAG. It integrates counterfactual prompting and a panel-based re-scoring mechanism to improve calibration while maintaining predictive accuracy. Experiments on two QA datasets demonstrate the effectiveness of Ca2KG.

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causality-aware calibrationkg-ragcounterfactual promptingpanel-based re-scoringpredictive accuracyKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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