Knowledge Graph Retrieval-Augmented Generation (KG-RAG) extends the RAG paradigm by incorporating structured knowledge from knowledge graphs, enabling Large Language Models (LLMs) to perform more precise and explainable reasoning. While KG-RAG improves factual accuracy in complex tasks, existing KG-RAG models are often severely overconfident, producing high-confidence predictions even when retrieved sub-graphs are incomplete or unreliable, which raises concerns for deployment in high-stakes domains. To address this issue, we propose Ca2KG, a Causality-aware Calibration framework for KG-RAG. Ca2KG integrates counterfactual prompting, which exposes retrieval-dependent uncertainties in knowledge quality and reasoning reliability, with a panel-based re-scoring mechanism that stabilises predictions across interventions. Extensive experiments on two complex QA datasets demonstrate that Ca2KG consistently improves calibration while maintaining or even enhancing predictive accuracy.

翻译：知识图谱检索增强生成（KG-RAG）通过整合知识图谱中的结构化知识，扩展了RAG范式，使大型语言模型（LLM）能够进行更精确且可解释的推理。尽管KG-RAG在复杂任务中提升了事实准确性，但现有KG-RAG模型往往存在严重的过度自信问题，即使在检索到的子图不完整或不可靠时，仍会生成高置信度的预测，这为其在高风险领域的部署带来了隐患。为解决这一问题，我们提出Ca2KG，一种面向KG-RAG的因果感知校准框架。Ca2KG融合了反事实提示技术——该技术能揭示知识质量与推理可靠性中依赖于检索的不确定性——以及基于专家组的重评分机制，该机制能在不同干预下稳定预测结果。在两个复杂问答数据集上的大量实验表明，Ca2KG在保持甚至提升预测准确率的同时，持续改善了校准效果。