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Fusion-Based Retrieval-Augmented Generation for Complex Question Answering with LLMs

A paper proposing a Retrieval-Augmented Generation model that integrates structured and unstructured knowledge.

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Fusion-Based Retrieval-Augmented Generation for Complex Question Answering with LLMs

By Yumeng Sun, Renyuan Zhang, Ran Meng, Lian Lian, H. J. Wang, Xinyu Quan
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The paper presents a dual-channel knowledge retrieval mechanism that targets structured and unstructured sources. A unified knowledge fusion network integrates both types of information into a coherent generation context, enhancing the accuracy and linguistic quality of generated outputs.

The method shows strong stability and generalization in cross-domain tasks.

Abstract

The paper presents a dual-channel knowledge retrieval mechanism that targets structured and unstructured sources. A unified knowledge fusion network integrates both types of information into a coherent generation context, enhancing the accuracy and linguistic quality of generated outputs. The method shows strong stability and generalization in cross-domain tasks.

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knowledge fusionretrieval-augmented generationcomplex question answeringlanguage modelsknowledge representationLarge Language ModelsRetrieval & RAGSemantic InteroperabilityStructured Content
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