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) has become a pow...
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) has become a powerful way to provide Large Language Models (LLMs) with structured, accurate context by pulling information from knowledge graphs. However, these systems often function as "black boxes," making it difficult to understand exactly how specific pieces of information from the graph influence the final answer. The paper XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation introduces XGRAG, a new framework designed to solve this transparency gap by providing causally grounded explanations for how GraphRAG systems reach their conclusions.

The Problem with Current Explainability

Existing methods for explaining RAG systems were originally designed for simple text-based retrieval. These methods struggle to interpret the complex, interconnected relationships found in knowledge graphs. Because they fail to account for the specific structure of the graph, they cannot accurately pinpoint which nodes or edges are responsible for a model's output. This creates a significant barrier to building trustworthy AI, as users cannot verify the reasoning behind the information provided by the system.

How XGRAG Works

To address this, the authors developed XGRAG, a framework that treats the knowledge graph as a primary component of the explanation process. Instead of relying on text-based analysis, XGRAG uses "graph-based perturbation strategies." By systematically altering or removing individual components of the graph and observing how the LLM’s answer changes, the framework can mathematically quantify the contribution of each specific piece of structured knowledge. This allows the system to identify which parts of the graph were most influential in generating the final response.

Key Results and Performance

The researchers tested XGRAG against RAG-Ex, an existing baseline for standard RAG systems, using datasets like NarrativeQA, FairyTaleQA, and TriviaQA. The results showed that XGRAG outperformed the baseline by 14.81% in explanation quality, as measured by the F1-score of the alignment between the generated explanations and the actual answers. Additionally, the explanations provided by XGRAG showed a strong correlation with graph centrality measures, confirming that the framework successfully captures and interprets the underlying structure of the knowledge graph.

A Path Toward Trustworthy AI

By providing a scalable and generalizable method for interpreting GraphRAG, the authors aim to make AI systems more transparent and reliable. XGRAG moves beyond simple text interpretation, offering a way to visualize and validate the reasoning process of models that rely on structured data. This approach is a significant step toward ensuring that as we integrate more complex knowledge into AI, we maintain the ability to audit and understand how that knowledge is being used.