FarsiMCQGen: a Persian Multiple-choice Question Generation Framework

• URL: http://arxiv.org/abs/2510.15134v1
• Date: Thu, 16 Oct 2025 20:52:07 GMT
• Title: FarsiMCQGen: a Persian Multiple-choice Question Generation Framework
• Authors: Mohammad Heydari Rad, Rezvan Afari, Saeedeh Momtazi,
• Abstract summary: This paper introduces FarsiMCQGen, an innovative approach for generating Persian-language multiple-choice questions (MCQs)<n>Our methodology combines candidate generation, filtering, and ranking techniques to build a model that generates answer choices resembling those in real MCQs.<n>We leverage advanced methods, including Transformers and knowledge graphs, integrated with rule-based approaches to craft credible distractors that challenge test-takers.
• Score: 2.026379197206863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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