mlsanityData to 7 Oct 2026

FarsiMCQGen: a Persian Multiple-choice Question Generation Framework

arXiv 2510.15134 ↗First posted 16 Oct 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 59 references that are in the map. First posted on arXiv 16 Oct 2025.

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References · 23

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11 more
  1. 2020A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategiescited by 22
  2. 2024Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?cited by 22
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