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

GraphRAG systems currently operate as "black boxes": While knowledge graph-based retrieval systems provide more structured and grounded answers than traditional RAG, the reasoning process for how specific pieces of knowledge influence the final answer remains opaque, creating transparency challenges for responsible AI deployment.

New explainability framework improves transparency by 14.81%: XGRAG introduces a method to generate causal explanations by testing how removing different graph components affects model outputs, achieving significantly better explanation quality (measured by F1-score) compared to existing RAG explanation methods across multiple datasets.

Explanations can be validated against graph structure: The framework's explanations show strong correlation with graph centrality measures, meaning the system can be audited to verify that it's actually identifying the most important knowledge components rather than producing arbitrary explanations.

Explainability improvements work across different LLMs and question types: XGRAG demonstrates robustness when tested with various large language models and different types of questions and narrative structures, suggesting the approach could be broadly applicable rather than limited to specific use cases.

Addresses a critical gap in AI trustworthiness: By making GraphRAG systems interpretable, this work helps organizations understand and verify that their AI systems are using the right information to reach conclusions, which is essential for deploying these systems responsibly in high-stakes applications.