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arXiv · 2502.07459

PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian

Erfan Moosavi Monazzah·Vahid Rahimzadeh·Yadollah Yaghoobzadeh·Azadeh Shakery·Mohammad Taher Pilehvar
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Abstract

Large language models predominantly reflect Western cultures, largely due to the dominance of English-centric training data. This imbalance presents a significant challenge, as LLMs are increasingly used across diverse contexts without adequate evaluation of their cultural competence in non-English languages, including Persian. To address this gap, we introduce PerCul, a carefully constructed dataset designed to assess the sensitivity of LLMs toward Persian culture. PerCul features story-based, multiple-choice questions that capture culturally nuanced scenarios. Unlike existing benchmarks, PerCul is curated with input from native Persian annotators to ensure authenticity and to prevent the use of translation as a shortcut. We evaluate several state-of-the-art multilingual and Persian-specific LLMs, establishing a foundation for future research in cross-cultural NLP evaluation. Our experiments demonstrate a 11.3% gap between best closed source model and layperson baseline while the gap increases to 21.3% by using the best open-weight model. You can access the dataset from here: https://huggingface.co/datasets/teias-ai/percul

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Erfan Moosavi Monazzah, Vahid Rahimzadeh, Yadollah Yaghoobzadeh, Azadeh Shakery, Mohammad Taher Pilehvar. 2025-02-11. PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian. https://doi.org/10.18653/v1%2F2025.naacl-long.631

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