让填空题干扰项难度可调，生成更符合人类认知的题目。
Difficulty-Controllable Cloze Question Distractor Generation
- 用双向生成+筛选机制构建带难度标注的数据集
- 多任务学习训练模型，实现不同难度干扰项生成
- 效果优于GPT-4o，更贴合真实考试难度感知
多项选择填空题广泛用于评估语言能力与理解水平。然而，生成高质量干扰项仍具挑战性，因现有方法缺乏难度可控性，且缺少标注难度的数据集。为此，我们提出一种可调节难度的干扰项生成框架，结合数据增强与多任务学习策略。首先，通过双向干扰项生成过程生成多样且合理的候选项，经筛选后利用集成问答系统按难度分类，构建高质量、带难度标注的数据集。其次，基于该数据集，采用多任务学习训练可控制难度的生成模型。实验表明，该方法在各难度层级上均能生成高质量干扰项，且在匹配人类对干扰项难度感知方面显著优于GPT-4o。
原文摘要 · Abstract (English)
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 to produce diverse and plausible distractors. These candidates are filtered and then categorized by difficulty using an ensemble QA system. Second, this newly created dataset is used to train a difficulty-controllable generation model via multitask learning. 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.
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