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CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning

A framework that enhances Large Language Models by retrieving relevant knowledge.

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CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning

By Kehan Xu, Kun Zhang, Jingyuan Li, Wei Huang, Yuanzhuo WangElectronics
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The CRP-RAG framework addresses limitations in existing Retrieval-Augmented Generation methods. It employs reasoning graphs to model complex query reasoning processes and guides knowledge retrieval, aggregation, and evaluation through these graphs.

This approach outperforms baseline models in open-domain QA, multi-hop reasoning, and factual verification.

Abstract

The CRP-RAG framework addresses limitations in existing Retrieval-Augmented Generation methods. It employs reasoning graphs to model complex query reasoning processes and guides knowledge retrieval, aggregation, and evaluation through these graphs. This approach outperforms baseline models in open-domain QA, multi-hop reasoning, and factual verification.

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crp-rag frameworkretrieval-augmented generationknowledge retrievalreasoning graphslarge language modelsopen-domain qaLarge Language ModelsRetrieval & RAGSemantic InteroperabilityOntology & Taxonomy
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