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Knowledge graph-extended retrieval augmented generation for question answering

A paper proposing a system that integrates Large Language Models and Knowledge Graphs for robust question answering.

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Knowledge graph-extended retrieval augmented generation for question answering

By Jasper Linders, Jakub M. TomczakApplied Intelligence
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The paper presents a system, called KG-RAG, which combines Large Language Models and Knowledge Graphs to improve question answering. It includes a question decomposition module and uses In-Context Learning and Chain-of-Thought prompting to generate explicit reasoning chains.

Experiments show improved accuracy for multi-hop questions compared to baselines.

Abstract

The paper presents a system, called KG-RAG, which combines Large Language Models and Knowledge Graphs to improve question answering. It includes a question decomposition module and uses In-Context Learning and Chain-of-Thought prompting to generate explicit reasoning chains. Experiments show improved accuracy for multi-hop questions compared to baselines.

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question answeringknowledge graph extended retrieval augmented generationin-context learningchain-of-thought promptingmulti-hop questionsKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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Knowledge graph-extended retrieval augmented generation for question answering | Aramai