Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
This work addresses the problem of generating plausible distractors for educational assessments, representing an incremental improvement over existing methods.
The paper tackled distractor generation for multiple-choice questions by introducing retrieval augmented pretraining and knowledge graph integration, resulting in improved F1@3 scores from 14.80 to 16.47 on MCQ and from 15.92 to 16.50 on Sciq datasets.
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.