用大模型和偏好优化生成可调节难度的阅读理解选择题
Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimization
- 用直接偏好优化训练大模型，精准控制题目难度
- 首次实现可直接生成教育场景常用的选择题
- 适合需要自适应练习系统的教育科技开发者
阅读理解的难度可控题目生成在教育领域受到广泛关注，是支持自适应学习的基础工具。尽管近期已有若干神经网络方法在难度控制方面取得进展，但传统方法仍存在两大局限：一是无法直接生成选择题——教育中最常用的题型；二是未显式优化难度控制准确性，导致难度可控性仍有提升空间。为此，本文提出一种基于大语言模型与直接偏好优化技术的难度可控阅读理解选择题生成方法，通过优化训练提升难度控制的精确度。
原文摘要 · Abstract (English)
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.
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