Distractors are important in learning evaluation. This paper surveys distractor generation tasks using English multiple-choice question datasets for textual and multimodal contexts. In particular, this paper presents a thorough literature review of the recent studies on distractor generation tasks, discusses multiple choice components and their characteristics, analyzes the related datasets, and summarizes the evaluation metrics of distractor generation. Our investigation reveals that more than half of datasets are human-generated from educational sources in specific domains such as Science and English, which are largely text-based, with a lack of open domain and multimodal datasets.

翻译：干扰项在学习评估中具有重要意义。本文针对文本与多模态场景下的英文多选题数据集，对干扰项生成任务进行了系统综述。具体而言，本文全面梳理了近期干扰项生成任务的相关研究，讨论了多选题的构成要素及其特征，分析了相关数据集，并总结了干扰项生成的评估指标。我们的调查表明，超过半数的数据集来源于教育领域特定学科（如科学和英语）的人工生成文本数据，这些数据集主要基于文本形式，缺乏开放域和多模态数据集。