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XGRAG: A Graph-Native Framework for Explaining KG-b... | AI Research

Key Takeaways

XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation Graph-based Retrieval-Augmented Generation (GraphRAG) improves how lar...
However, GraphRAG reasoning process remains a black-box, limiting our ability to understand how specific pieces of structured knowledge influence the final output.
We conduct extensive experiments comparing XGRAG against RAG-Ex, an XAI baseline for standard RAG, and evaluate its robustness across various question types, narrative structures and LLMs.
Furthermore, XGRAG explanations exhibit a strong correlation with graph centrality measures, validating its ability to capture graph structure.
XGRAG provides a scalable and generalizable approach towards trustworthy AI through transparent, graph-based explanations that enhance the interpretability of RAG systems.

Paper AbstractExpand

Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a structured, semantically coherent context, yielding more grounded answers. However, GraphRAG reasoning process remains a black-box, limiting our ability to understand how specific pieces of structured knowledge influence the final output. Existing explainability (XAI) methods for RAG systems, designed for text-based retrieval, are limited to interpreting an LLM response through the relational structures among knowledge components, creating a critical gap in transparency and trustworthiness. To address this, we introduce XGRAG, a novel framework that generates causally grounded explanations for GraphRAG systems by employing graph-based perturbation strategies, to quantify the contribution of individual graph components on the model answer. We conduct extensive experiments comparing XGRAG against RAG-Ex, an XAI baseline for standard RAG, and evaluate its robustness across various question types, narrative structures and LLMs. Our results demonstrate a 14.81% improvement in explanation quality over the baseline RAG-Ex across NarrativeQA, FairyTaleQA, and TriviaQA, evaluated by F1-score measuring alignment between generated explanations and original answers. Furthermore, XGRAG explanations exhibit a strong correlation with graph centrality measures, validating its ability to capture graph structure. XGRAG provides a scalable and generalizable approach towards trustworthy AI through transparent, graph-based explanations that enhance the interpretability of RAG systems.

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

Graph-based Retrieval-Augmented Generation (GraphRAG) improves how large language models (LLMs) answer questions by providing them with structured, context-rich information from knowledge graphs. While this makes answers more grounded, the reasoning process remains a "black box," making it difficult to determine which specific pieces of information actually influence the final output. This paper introduces XGRAG, a framework designed to bring transparency to these systems by quantifying how individual components of a knowledge graph contribute to an LLM's response.

The Problem with Black-Box Reasoning

Current explainability methods for RAG systems were primarily designed for text-based retrieval. These methods struggle to interpret the complex, relational structures found in knowledge graphs. Because these existing tools cannot effectively map how specific graph connections lead to an LLM's answer, there is a significant gap in the transparency and trustworthiness of GraphRAG systems. Users often cannot see the "why" behind a model's conclusion, which limits the reliability of these systems in practical applications.

How XGRAG Works

XGRAG addresses this transparency gap by using a "graph-native" approach. Instead of treating the knowledge graph as simple text, the framework employs graph-based perturbation strategies. By systematically perturbing—or slightly altering—individual components of the graph, the system can measure how those changes affect the LLM's final answer. This allows XGRAG to calculate the specific contribution of each graph element, effectively mapping the causal link between the structured data and the generated response.

Performance and Validation

To test the effectiveness of the framework, the researchers compared XGRAG against RAG-Ex, an existing explainability baseline for standard RAG systems. The experiments, conducted across datasets like NarrativeQA, FairyTaleQA, and TriviaQA, showed that XGRAG achieved a 14.81% improvement in explanation quality, measured by the F1-score alignment between the generated explanations and the model's original answers. Furthermore, the researchers found that XGRAG’s explanations correlate strongly with graph centrality measures, confirming that the framework successfully captures and interprets the underlying structure of the knowledge graph.

Toward Trustworthy AI

By providing a scalable and generalizable method for interpreting GraphRAG, XGRAG represents a step forward in making AI systems more transparent. The framework’s ability to offer clear, graph-based explanations helps bridge the gap between complex machine reasoning and human understanding, ultimately contributing to the development of more trustworthy and interpretable AI technologies.

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