When evaluating a learner's knowledge proficiency, the multiple-choice question is an efficient and widely used format in standardized tests. Nevertheless, generating these questions, particularly plausible distractors (incorrect options), poses a considerable challenge. Generally, the distractor generation can be classified into cloze-style distractor generation (CDG) and natural questions distractor generation (NQDG). In contrast to the CDG, utilizing pre-trained language models (PLMs) for NQDG presents three primary challenges: (1) PLMs are typically trained to generate ``correct'' content, like answers, while rarely trained to generate ``plausible" content, like distractors; (2) PLMs often struggle to produce content that aligns well with specific knowledge and the style of exams; (3) NQDG necessitates the model to produce longer, context-sensitive, and question-relevant distractors. In this study, we introduce a fine-tuning framework named DGRC for NQDG in Chinese multi-choice reading comprehension from authentic examinations. DGRC comprises three major components: hard chain-of-thought, multi-task learning, and generation mask patterns. The experiment results demonstrate that DGRC significantly enhances generation performance, achieving a more than 2.5-fold improvement in BLEU scores.

翻译：在评估学习者知识掌握程度时，选择题是标准化测试中一种高效且广泛使用的题型。然而，生成此类题目，尤其是看似合理的干扰项（错误选项），构成了重大挑战。通常，干扰项生成可分为完形填空式干扰项生成（CDG）与自然问题干扰项生成（NQDG）。相较于CDG，利用预训练语言模型（PLMs）进行NQDG面临三个主要挑战：（1）PLMs通常被训练用于生成“正确”内容（如答案），而极少被训练生成“看似合理”的内容（如干扰项）；（2）PLMs往往难以生成与特定知识及考试风格高度契合的内容；（3）NQDG要求模型生成更长、与上下文相关且与问题匹配的干扰项。本研究针对真实考试中的中文阅读理解选择题，提出了一种名为DGRC的NQDG微调框架。DGRC包含三大核心组件：硬性思维链、多任务学习与生成掩码模式。实验结果表明，DGRC显著提升了生成性能，在BLEU分数上实现了超过2.5倍的改进。