arXiv Computation and Language August 27, 2026 By Ben Lagnese, Manas Gaur

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

Read the original on arXiv Computation and Language →

SelfGraphRAG is a framework that generates synthetic question‑answer pairs directly from the structure of a knowledge graph to train a query‑conditioned graph retriever. By capturing multi‑hop paths and local neighborhoods, the generated questions provide relational supervision without requiring manually labeled data. Experiments on multi‑hop question answering and classification tasks show that SelfGraphRAG improves retrieval precision and downstream reasoning performance compared to embedding‑based baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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