Structure Guided Retrieval-Augmented Generation for Factual Queries
Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be factually incorrect. However, existing RAG approaches predominantly rely on vector similarity for retrieval, which is prone to semantic noise and fails to ensure that generated responses fully satisfy the complex conditions specified by factual queries, often leading to incorrect answers. To address this challenge, we introduce a novel research problem, named Exact Retrieval Problem (ERP). To the best of our knowledge, this is the first problem formulation that explicitly incorporates structural information into RAG for factual questions to sati...
| Authors | Miao Xie, Xiao Zhang, Yi Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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