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WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs

A paper proposing a new approach to integrating web search and knowledge graphs into retrieval-augmented generation systems.

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WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs

By Weijian Xie, Xuefeng Liang, Yuhui Liu, Kaihua Ni, Hong Cheng, Zetian HuarXiv (Cornell University)
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The authors propose WeKnow-RAG, a system that combines knowledge graphs with dense vector retrieval to improve the accuracy and reliability of large language models.

The approach utilizes domain-specific knowledge graphs and multi-stage web page retrieval techniques to enhance performance on factual information and complex reasoning tasks. A self-assessment mechanism is also integrated to evaluate the trustworthiness of generated answers.

Abstract

The authors propose WeKnow-RAG, a system that combines knowledge graphs with dense vector retrieval to improve the accuracy and reliability of large language models. The approach utilizes domain-specific knowledge graphs and multi-stage web page retrieval techniques to enhance performance on factual information and complex reasoning tasks. A self-assessment mechanism is also integrated to evaluate the trustworthiness of generated answers.

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weknow-ragretrieval-augmented-generationknowledge-graph-integrationlanguage-model-enhancementself-assessment-mechanismKnowledge GraphsLarge Language ModelsRetrieval & RAGAI Agents
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