Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation

TL;DR

This survey reviews methods, datasets, and evaluation metrics for distractor generation in multiple-choice questions, highlighting the transition from traditional approaches to neural network-based models and discussing future research directions.

Contribution

It provides a comprehensive overview of current AI techniques, datasets, and evaluation methods for distractor generation in objective questions, emphasizing recent neural network advancements.

Findings

Neural networks and pre-trained language models have set new benchmarks.

Various datasets and evaluation metrics are used across domains.

AI models show promising results but face challenges in realism and diversity.

Abstract

The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions.