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Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration

A research paper on improving distractor generation in multiple-choice questions using retrieval augmented pretraining and knowledge graph integration.

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Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration

By Han Cheng Yu, Yu An Shih, Kin Man Law, Kai‐Yu Hsieh, Yu Chen Cheng, Hsin Chih Ho, Zih An Lin, Wen-Chuan Hsu
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This paper proposes a method to enhance distractor generation in multiple-choice questions by combining retrieval augmented pretraining and knowledge graph integration. The approach aims to improve the quality of distractors, which are essential for assessing students' understanding.

Experimental results demonstrate the effectiveness of the proposed method in generating high-quality distractors.

Abstract

This paper proposes a method to enhance distractor generation in multiple-choice questions by combining retrieval augmented pretraining and knowledge graph integration. The approach aims to improve the quality of distractors, which are essential for assessing students' understanding. Experimental results demonstrate the effectiveness of the proposed method in generating high-quality distractors.

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distractor generationmultiple-choice questionsretrieval augmented pretrainingknowledge graph integrationnatural language processingmachine learningKnowledge GraphsContent EngineeringLarge Language ModelsRetrieval & RAG
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