SG-RAG: Structure-Guided Retrieval for Factual Queries
A new research paper introduces Structure Guided Retrieval-Augmented Generation (SG-RAG) to improve factual accuracy in large language models. The authors identify a novel problem, Exact Retrieval Problem (ERP), which explicitly incorporates structural information into RAG to satisfy all query conditions. SG-RAG models retrieval as an embedding-based subgraph matching task, using retrieved topological structures to guide generation. The paper is available on arXiv with ID 2604.22843.
Key facts
- arXiv paper 2604.22843 introduces SG-RAG
- SG-RAG addresses Exact Retrieval Problem (ERP)
- ERP is the first problem formulation incorporating structural information into RAG
- SG-RAG models retrieval as embedding-based subgraph matching
- Existing RAG approaches rely on vector similarity prone to semantic noise
- SG-RAG uses retrieved topological structures to guide generation
- The paper is a cross-type announcement on arXiv
- The research aims to mitigate hallucinations in LLMs
Entities
Institutions
- arXiv