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XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

A framework that generates causally grounded explanations for GraphRAG systems.

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XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

By Zhuoling Li, Ha Linh Hong Tran Nguyen, Valeria Bladinieres, Maxim RomanovskyarXiv
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The paper introduces XGRAG, a graph-native framework for explaining knowledge graph-based retrieval-augmented generation. It employs graph-based perturbation strategies to quantify the contribution of individual graph components on the model answer.

The authors conduct experiments comparing XGRAG against an existing explainability baseline and evaluate its robustness across various question types and LLMs.

Abstract

The paper introduces XGRAG, a graph-native framework for explaining knowledge graph-based retrieval-augmented generation. It employs graph-based perturbation strategies to quantify the contribution of individual graph components on the model answer. The authors conduct experiments comparing XGRAG against an existing explainability baseline and evaluate its robustness across various question types and LLMs.

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graph-based explanationskg-based retrieval-augmented generationxgrag frameworkexplainability in aiknowledge graph reasoningKnowledge GraphsStructured ContentAI AgentsLarge Language Models
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