PersianMCQ-Instruct: A Comprehensive Resource for Generating Multiple-Choice Questions in Persian
Proceedings of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025), at COLING 2025, Abu Dhabi, UAE
Presented on , 4 citations on Google Scholar
In short
The paper releases PersianMCQ-Instruct, a dataset of over 12,000 multiple-choice questions generated from more than 4,000 Persian Wikipedia pages, together with three fine-tuned models (Gemma2-9B, Llama3.1-8B and Mistral-7B) for Persian question generation. Human evaluation and fine-tuning experiments showed clear improvements in Persian MCQ generation.
Multilingual benchmarks Educational content
Cite
@inproceedings{zeinalipour-etal-2025-persianmcq,
title = "{P}ersian{MCQ}-Instruct: A Comprehensive Resource for Generating Multiple-Choice Questions in {P}ersian",
author = "Zeinalipour, Kamyar and
Jamshidi, Neda and
Akbari, Fahimeh and
Maggini, Marco and
Bianchini, Monica and
Gori, Marco",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
month = jan,
year = "2025",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.loreslm-1.27/",
pages = "344--372"
}