DGRC：面向中文多选阅读理解干扰项生成的有效微调框架
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
AI总结：
针对中文多选阅读理解干扰项生成，提出DGRC微调框架，融合硬思维链、多任务学习与生成掩码模式，使BLEU分数提升超2.5倍。
摘要：
在评估学习者的知识掌握程度时，多选题是标准化考试中高效且广泛使用的题型。然而，生成这些题目，尤其是合理的干扰项（错误选项），构成了相当大的挑战。一般而言，干扰项生成可分为完形填空式干扰项生成（CDG）和自然问题干扰项生成（NQDG）。与CDG相比，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”的内容，如答案，而很少被训练用于生成“合理”的内容，如干扰项；（2）PLMs往往难以生成与特定知识和考试风格高度契合的内容；（3）NQDG要求模型生成更长、对上下文敏感且与问题相关的干扰项。在本研究中，我们引入了一个名为DGRC的微调框架，用于从真实考试中生成中文多选阅读理解的NQDG。DGRC包含三个主要组成部分：硬思维链、多任务学习和生成掩码模式。实验结果表明，DGRC显著提升了生成性能，BLEU分数提高了2.5倍以上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
