ISSR：用于词汇测试干扰项生成的自审式迭代选择
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation
AI总结：
针对英语词汇题干扰项易出现多个正确答案的问题，本文分析台湾大学入学考试答题数据，提出基于LLM自审机制的ISSR框架以迭代筛选有效且多样的干扰项。
摘要：
词汇习得对第二语言学习至关重要，因为它支撑着所有核心语言技能。准确的词汇评估在标准化考试中尤为重要，这类考试中的试题用于评价学习者对词汇的理解和语境运用。已有研究探索了生成干扰项的方法，以辅助英语词汇测试设计。然而，当前方法通常依赖词汇数据库或预定义规则，并且经常生成可能因引入多个正确选项而使题目失效的干扰项。本研究聚焦台湾大学入学考试中的英语词汇题。我们分析学生作答分布，以深入理解这些试题的特征，并为未来研究提供参考。此外，我们指出了大型语言模型（LLM）在支持教师为词汇测试设计生成干扰项方面的关键局限。为应对这些挑战，我们提出迭代选择与自审（ISSR）框架，该框架利用一种新颖的基于 LLM 的自审机制，在提供多样化选项的同时确保干扰项保持有效。实验结果表明，ISSR 在生成看似合理的干扰项方面取得了可观表现，且自审机制有效过滤了可能导致题目失效的干扰项。
英文摘要：
Vocabulary acquisition is essential to second language learning, as it underpins all core language skills. Accurate vocabulary assessment is particularly important in standardized exams, where test items evaluate learners' comprehension and contextual use of words. Previous research has explored methods for generating distractors to aid in the design of English vocabulary tests. However, current approaches often rely on lexical databases or predefined rules, and frequently produce distractors that risk invalidating the question by introducing multiple correct options. In this study, we focus on English vocabulary questions from Taiwan's university entrance exams. We analyze student response distributions to gain insights into the characteristics of these test items and provide a reference for future research. Additionally, we identify key limitations in how large language models (LLMs) support teachers in generating distractors for vocabulary test design. To address these challenges, we propose the iterative selection with self-review (ISSR) framework, which makes use of a novel LLM-based self-review mechanism to ensure that the distractors remain valid while offering diverse options. Experimental results show that ISSR achieves promising performance in generating plausible distractors, and the self-review mechanism effectively filters out distractors that could invalidate the question.
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。
