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HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation

A novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration.

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HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation

By Hao Liu, Zhengren Wang, Xijing Chen, Zhiyu Li, Feiyu Xiong, Qinhan Yu, Wentao Zhang
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The paper proposes HopRAG, a framework that constructs a passage graph and employs a retriever-reason-prune mechanism to identify relevant passages based on logical connections. Experiments demonstrate improved final answer quality on multi-hop benchmarks.

The framework expands the retrieval scope by exploring multi-hop neighbors guided by pseudo-queries and LLM reasoning.

Abstract

The paper proposes HopRAG, a framework that constructs a passage graph and employs a retriever-reason-prune mechanism to identify relevant passages based on logical connections. Experiments demonstrate improved final answer quality on multi-hop benchmarks. The framework expands the retrieval scope by exploring multi-hop neighbors guided by pseudo-queries and LLM reasoning.

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multi-hop reasoninglogic-aware retrievalgraph-structured knowledge explorationpseudo-queriesllm reasoningKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation | Aramai