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

TL;DR

This paper improves distractor generation for multiple-choice questions by combining retrieval augmented pretraining and knowledge graph integration, leading to significant performance gains on benchmark datasets.

Contribution

It introduces retrieval augmented pretraining and knowledge graph integration to enhance distractor generation, a novel combination for this task.

Findings

F1@3 score increased from 14.80 to 16.47 on MCQ dataset.

F1@3 score increased from 15.92 to 16.50 on Sciq dataset.

Models outperform state-of-the-art methods significantly.

Abstract

In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose \textit{retrieval augmented pretraining}, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs to enhance the performance of DG. Through experiments with benchmarking datasets, we show that our models significantly outperform the state-of-the-art results. Our best-performing model advances the F1@3 score from 14.80 to 16.47 in MCQ dataset and from 15.92 to 16.50 in Sciq dataset.