SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation

• URL: http://arxiv.org/abs/2608.25123v1
• Date: Tue, 25 Aug 2026 20:18:05 GMT
• Title: SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
• Abstract summary: SelfGraphRAG is a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever.<n>We show that SelfGraphRAG improves retrieval precision and downstream reasoning performance over embedding-based baselines.
• Score: 10.08021023788401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often underuse the relational structure encoded in knowledge graphs. Graph-based RAG can capture entity relationships, yet supervised graph retrieval typically requires labeled question-answer data that may not be available for newly constructed graphs. We address this limitation with SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever. The generated questions capture multi-hop paths and local neighborhoods, providing relational supervision without manual annotation. Experiments on multi-hop question answering and classification benchmarks show that SelfGraphRAG improves retrieval precision and downstream reasoning performance over embedding-based baselines. These results suggest that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.

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