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Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing

A study on a hybrid framework for retrieval-augmented generation in smart manufacturing.

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Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing

By Yuwei Wan, Zheyuan Chen, Ying Liu, Chong Chen, Michael PackianatherAdvanced Engineering Informatics
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The paper proposes a hybrid knowledge graph-vector retrieval framework to enhance the performance of large language models in question-answering tasks. The approach combines structured knowledge graph metadata with unstructured vector retrieval and achieves high accuracy and contextual relevance.

Evaluated on design for additive manufacturing tasks, the proposed method demonstrates its effectiveness.

Abstract

The paper proposes a hybrid knowledge graph-vector retrieval framework to enhance the performance of large language models in question-answering tasks. The approach combines structured knowledge graph metadata with unstructured vector retrieval and achieves high accuracy and contextual relevance. Evaluated on design for additive manufacturing tasks, the proposed method demonstrates its effectiveness.

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hybrid frameworkretrieval-augmented generationsmart manufacturingquestion-answeringknowledge graph metadataKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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