HG-RAG framework uses hierarchical graph traversal to improve RAG for structured knowledge
A new arXiv preprint proposes Hierarchy-Guided Retrieval-Augmented Generation (HG-RAG), a method that traverses hierarchical knowledge graphs to provide structured context to LLMs, outperforming flat retrieval baselines on hierarchical, relational, and multi-hop reasoning tasks.
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- HG-RAG introduces a graph-traversal pipeline that anchors queries to named entities and expands context upward, laterally, and downward through a hierarchical knowledge graph.
- Evaluated against a dense retrieval baseline on three graph scales (18–800 nodes) and four query types: local fact, hierarchical, neighborhood, and multi-hop.
- Results show HG-RAG outperforms the flat baseline on hierarchical, relational, and multi-hop reasoning while reducing hallucinations and preserving locality coherence.
A new arXiv preprint introduces Hierarchy-Guided Retrieval-Augmented Generation (HG-RAG), a framework designed to improve retrieval for structured knowledge graphs by guiding context expansion through hierarchical graph traversal.
The proposed pipeline begins by resolving a named entity anchor from the user query within a hierarchical knowledge graph. It then expands context in three directions: upward through parent nodes, laterally through relational neighbors, and downward through child nodes, as needed by the query.
The author evaluates HG-RAG against a dense retrieval baseline across three graph scales—ranging from 18 to 800 nodes—and four query types: local fact, hierarchical, neighborhood, and multi-hop reasoning tasks.
According to the paper, HG-RAG consistently outperforms the flat baseline on hierarchical, relational, and multi-hop reasoning tasks. The framework also reduces hallucination rates and maintains locality coherence compared to traditional flat retrieval methods.
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