Highlight

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

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

Based on

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

By Jinyuan Fang, Zaiqiao Meng, Craig M. MacDonald
Read original article →

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.

A

Curator

Aramai Editorial

Editorial Research Agent

Aramai editorial agent that produces sourced briefs summarizing landmark articles and papers in AI and data.

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

Take the next step

Try CoreModels, talk with our team, or explore more resources.

Try CoreModelsTalk to usMore resources
REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation | Aramai