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REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation

A paper proposing a knowledge graph generation module to enhance retrieval-augmented reader models.

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REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation

By Jinyuan Fang, Zaiqiao Meng, Craig M. MacDonald
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The authors propose REANO, a system that generates knowledge graphs from passages and uses them to improve the performance of retrieval-augmented reader models. This is done by adding a knowledge graph generator and an answer predictor to the model.

Experimental results show improvements in exact match scores on five open domain question answering datasets.

Abstract

The authors propose REANO, a system that generates knowledge graphs from passages and uses them to improve the performance of retrieval-augmented reader models. This is done by adding a knowledge graph generator and an answer predictor to the model. Experimental results show improvements in exact match scores on five open domain question answering datasets.

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knowledge graph generationretrieval-augmented reader modelsopen domain question answeringnatural language processingartificial intelligenceKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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