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Knowledge Graph Prompting for Multi-Document Question Answering

A method for formulating context in prompting large language models for multi-document question answering.

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Knowledge Graph Prompting for Multi-Document Question Answering

By Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler DerrProceedings of the AAAI Conference on Artificial Intelligence
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The authors propose a Knowledge Graph Prompting (KGP) method to improve multi-document question answering. KGP consists of graph construction and traversal modules, which create a knowledge graph over multiple documents and navigate across nodes to gather supporting passages.

The method aims to enhance prompt design and retrieval augmented generation for large language models.

Abstract

The authors propose a Knowledge Graph Prompting (KGP) method to improve multi-document question answering. KGP consists of graph construction and traversal modules, which create a knowledge graph over multiple documents and navigate across nodes to gather supporting passages. The method aims to enhance prompt design and retrieval augmented generation for large language models.

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question answeringknowledge graph promptingmulti-document question answeringlarge language modelsgraph constructiongraph traversalKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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