检索增强生成可以通过将答案基于外部证据, 来提高事实性，但 Agentic GraphRAG 使引文忠实的含义变得复杂。在这些系统,中，代理在生成答案和一小组引文之前会探索知识图。我们将引用忠实度视为轨迹级问题:最终引用不仅应该支持答案,，而且还应考虑图遍历,结构,以及可能影响它的已访问但未引用的实体。通过受控消融实验,，我们比较了隔离,、去除,以及屏蔽引用和未引用的图形实体的效果。我们的结果表明，引用的证据通常是必要的,，因为删除它会大大改变答案并降低准确性。然而，, 引用是不够的,，因为准确的答案还可能取决于未引用的遍历上下文和周围的图结构。这些发现表明，Agentic GraphRAG 中的引文评估应该超越来源支持，转向更广泛的检索轨迹上的出处。
Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for citations to be faithful. In these systems, an agent explores a knowledge graph before producing an answer and a small set of citations. We frame citation faithfulness as a trajectory-level problem: final citations should not only support the answer, but also account for the graph traversal, structure, and visited-but-uncited entities that may influence it. Through controlled ablation experiments, we compare the effects of isolating, removing, and masking cited and uncited graph entities. Our results show that cited evidence is often necessary, as removing it substantially changes answers and reduces accuracy. However, citations are not sufficient, because accurate answers can also depend on uncited traversal context and surrounding graph structure. These findings suggest that citation evaluation in Agentic GraphRAG should move beyond source support toward provenance over the broader retrieval trajectory.
科目: 人工智能 (cs.AI); 信息检索 (cs.IR)
Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)