Iterative RAG outperforms Gold Context RAG by up to 25.6 points on ChemKGMultiHopQA across 11 LLMs, mainly by staging retrieval to avoid context overload and correct hypothesis drift.
Jinyu Wang, Jingjing Fu, Rui Wang, Lei Song, and Jiang Bian
2 Pith papers cite this work. Polarity classification is still indexing.
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ArchRAG proposes attributed-community hierarchical indexing and LLM clustering to improve accuracy and lower token usage in graph-based retrieval-augmented generation.
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When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
Iterative RAG outperforms Gold Context RAG by up to 25.6 points on ChemKGMultiHopQA across 11 LLMs, mainly by staging retrieval to avoid context overload and correct hypothesis drift.
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ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
ArchRAG proposes attributed-community hierarchical indexing and LLM clustering to improve accuracy and lower token usage in graph-based retrieval-augmented generation.