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FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

A novel flexible modular framework for retrieval-augmented generation using knowledge graphs.

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FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

By Zengyi Gao, Yukun Cao, Hairu Wang, Ao Ke, Yuan Feng, S. Kevin Zhou, Xike Xie
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The paper proposes a flexible modular framework, FRAG, which improves retrieval quality while maintaining flexibility in knowledge graph-based retrieval-augmented generation.

FRAG estimates the hop range of reasoning paths and applies tailored pipelines to ensure efficient and accurate reasoning path retrieval. The method does not require extra LLM fine-tuning or calls, significantly boosting efficiency and conserving resources.

Abstract

The paper proposes a flexible modular framework, FRAG, which improves retrieval quality while maintaining flexibility in knowledge graph-based retrieval-augmented generation. FRAG estimates the hop range of reasoning paths and applies tailored pipelines to ensure efficient and accurate reasoning path retrieval. The method does not require extra LLM fine-tuning or calls, significantly boosting efficiency and conserving resources.

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fragflexible modular frameworkretrieval-augmented generationknowledge graphsreasoning pathsKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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