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Pythia-RAG: Retrieval-augmented generation over a unified multimodal knowledge graph for enhanced QA

A paper proposing Pythia-RAG, a retrieval-augmented generation model for question answering.

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Pythia-RAG: Retrieval-augmented generation over a unified multimodal knowledge graph for enhanced QA

By Zafar Ali, Yi Huang, Asad Khan, Guilin Qi, Yuxin Zhang, Junlan Feng, Chao Deng, Pavlos KefalasKnowledge-Based Systems
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The authors introduce Pythia-RAG, a unified multimodal knowledge graph that combines retrieval and generation capabilities. This approach aims to enhance question-answering performance by leveraging the strengths of both methods.

The paper presents experiments demonstrating the effectiveness of Pythia-RAG in various QA tasks.

Abstract

The authors introduce Pythia-RAG, a unified multimodal knowledge graph that combines retrieval and generation capabilities. This approach aims to enhance question-answering performance by leveraging the strengths of both methods. The paper presents experiments demonstrating the effectiveness of Pythia-RAG in various QA tasks.

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pythia-ragmultimodal knowledge graphquestion answeringretrieval-augmented generationknowledge graph enhancementKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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