用知识图谱自动生成问答对，解决无标注数据时图检索模型训练难题。
SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
- 从图结构自动生成带关系的问答对，替代人工标注。
- 在多跳问答任务中，检索准确率显著高于基于嵌入的基线方法。
- 适合新构建知识图谱、缺乏标注数据的场景使用。
检索增强生成（RAG）通过引入外部知识提升大语言模型性能，而无需重新训练。然而，现有方法通常未能充分利用知识图谱中的关系结构。基于图的RAG虽能捕捉实体间关系，但其监督式图检索通常需要标注的问答数据，这在新构建的知识图谱中难以获取。本文提出SelfGraphRAG框架，直接从知识图谱结构生成问答对，并用于训练查询条件化的图检索器。生成的问题可捕捉多跳路径与局部邻域信息，提供无需人工标注的关系性监督。在多跳问答与分类基准上的实验表明，SelfGraphRAG在检索精度和下游推理性能上均优于基于嵌入的基线方法。结果表明，当缺乏标注数据时，知识图谱结构本身可为图检索器训练提供有效监督。
原文摘要 · Abstract (English)
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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