Difficulty-Controllable Cloze Question Distractor Generation

Abstract

Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing methods often lack adaptability and control over difficulty levels, and the absence of difficulty-annotated datasets further hinders progress. To address these issues, we propose a novel framework for generating distractors with controllable difficulty by leveraging both data augmentation and a multitask learning strategy. First, to create a high-quality, difficulty-annotated dataset, we introduce a two-way distractor generation process in order to produce diverse and plausible distractors. These candidates are subsequently refined through filtering and then categorized by difficulty using an ensemble QA system. Second, this newly created dataset is leveraged to train a difficulty-controllable generation model via multitask learning. The framework includes carefully designed auxiliary tasks that enhance the model's semantic understanding of distractors and its ability to estimate their difficulty. Experimental results demonstrate that our method generates high-quality distractors across difficulty levels and substantially outperforms GPT-4o in aligning distractor difficulty with human perception.