FarsiMCQGen：一个波斯语多项选择题生成框架
FarsiMCQGen: a Persian Multiple-choice Question Generation Framework
AI总结：
针对波斯语低资源环境下多项选择题生成难的问题，提出FarsiMCQGen框架，结合候选生成、过滤、排序及Transformer和知识图谱技术，构建了含10,289个问题的数据集，经LLM评估验证了其有效性。
摘要：
多项选择题（MCQ）常用于教育测试中，因为它们提供了一种评估学习者知识的有效手段。然而，生成高质量的多项选择题，尤其是在波斯语等低资源语言中，仍然是一个重大挑战。本文介绍了FarsiMCQGen，一种用于生成波斯语多项选择题的创新方法。我们的方法结合了候选生成、过滤和排序技术，以构建一个能够生成与真实多项选择题中答案选项相似的模型。我们利用先进的方法，包括Transformer和知识图谱，并结合基于规则的方法来制作具有挑战性的干扰项，以考验应试者。我们的工作基于维基百科的数据，其中包含常识性问题。此外，本研究引入了一个新颖的波斯语多项选择题数据集，包含10,289个问题。该数据集由不同的最先进的大型语言模型（LLM）进行评估。我们的结果证明了我们模型的有效性和生成数据集的质量，这有望激发关于多项选择题的进一步研究。
英文摘要：
Multiple-choice questions (MCQs) are commonly used in educational testing, as they offer an efficient means of evaluating learners' knowledge. However, generating high-quality MCQs, particularly in low-resource languages such as Persian, remains a significant challenge. This paper introduces FarsiMCQGen, an innovative approach for generating Persian-language MCQs. Our methodology combines candidate generation, filtering, and ranking techniques to build a model that generates answer choices resembling those in real MCQs. We leverage advanced methods, including Transformers and knowledge graphs, integrated with rule-based approaches to craft credible distractors that challenge test-takers. Our work is based on data from Wikipedia, which includes general knowledge questions. Furthermore, this study introduces a novel Persian MCQ dataset comprising 10,289 questions. This dataset is evaluated by different state-of-the-art large language models (LLMs). Our results demonstrate the effectiveness of our model and the quality of the generated dataset, which has the potential to inspire further research on MCQs.
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。
