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

2026-08-25

AI Summary

为解决图结构RAG缺乏标注数据的问题，SelfGraphRAG通过从知识图谱生成合成QA对来训练查询条件的图检索器，提高了多跳问答和分类任务性能。

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.

Problem

• Graph-based RAG
• Supervised Graph Retrieval
• Labeled Question-Answer Data
• Knowledge Graph Structure

Innovation

• SelfGraphRAG
• synthetic QA generation
• graph-based RAG
• query-conditioned graph retriever
• multi-hop paths