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Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

A paper introducing SMART-SLIC, a domain-specific LLM framework integrating RAG with KG and vector store.

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Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

By Ryan Barron, Vesselin Grantcharov, Selma Wanna, Maksim E. Eren, Manish Bhattarai, Nicholas Solovyev, George Tompkins, Charles Nicholas
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The authors present SMART-SLIC, a framework that combines retrieval-augmented generation (RAG) with knowledge graphs (KG) and vector stores to improve question answering accuracy in specific domains. The framework is designed to be generalizable and adaptable to various specialized domains.

It aims to mitigate hallucinations, reduce fine-tuning needs, and attribute information sources.

Abstract

The authors present SMART-SLIC, a framework that combines retrieval-augmented generation (RAG) with knowledge graphs (KG) and vector stores to improve question answering accuracy in specific domains. The framework is designed to be generalizable and adaptable to various specialized domains. It aims to mitigate hallucinations, reduce fine-tuning needs, and attribute information sources.

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domain-specific knowledge graphsvector stores for NLPtensor factorization in AIretrieval-augmented generationlarge language modelsknowledge attributionKnowledge GraphsLarge Language ModelsRetrieval & RAGOntology & Taxonomy
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