Difficulty-controllable question generation for reading comprehension has gained significant attention in the field of education as a fundamental tool for adaptive learning support. Although several neural question generation methods have recently succeeded in controlling difficulty, conventional approaches still face two major limitations. First, they cannot directly generate multiple-choice questions, which are the most widely used question type in educational contexts. Second, they are not explicitly trained to optimize the accuracy of difficulty control, leaving room for further improvement in difficulty controllability. To address these limitations, this study proposes a novel difficulty-controllable multiple-choice question generation method for reading comprehension which leverages a large language model trained using a direct preference optimization technique to improve the accuracy of difficulty control.

翻译：阅读理解中的难度可控问题生成作为自适应学习支持的基础工具，在教育领域获得了广泛关注。尽管近期若干神经问题生成方法已在难度控制方面取得进展，但传统方法仍面临两大局限：其一，无法直接生成教育场景中最常用的多项选择题题型；其二，未经过显式训练以优化难度控制的准确性，导致难度可控性存在提升空间。为突破这些局限，本研究提出一种创新的阅读理解难度可控多项选择题生成方法，该方法利用基于直接偏好优化技术训练的大语言模型，以提升难度控制的精确度。