MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for
  N-level Assessment
Authors: name: Omid Ghahroodi, name: Arshia Hemmat, name: Marzia Nouri, name: Seyed Mohammad Hadi Hosseini, name: Doratossadat Dastgheib, name: Mohammad Vali Sanian, name: Alireza Sahebi, name: Reihaneh Zohrabi, name: Mohammad Hossein Rohban, name: Ehsaneddin Asgari, name: Mahdieh Soleymani Baghshah

Recent advancements in large vision-language models (VLMs) have primarily
focused on English, with limited attention given to other languages. To address
this gap, we introduce MEENA (also known as PersianMMMU), the first dataset
designed to evaluate Persian VLMs across scientific, reasoning, and human-level
understanding tasks. Our dataset comprises approximately 7,500 Persian and
3,000 English questions, covering a wide range of topics such as reasoning,
mathematics, physics, diagrams, charts, and Persian art and literature. Key
features of MEENA include: (1) diverse subject coverage spanning various
educational levels, from primary to upper secondary school, (2) rich metadata,
including difficulty levels and descriptive answers, (3) original Persian data
that preserves cultural nuances, (4) a bilingual structure to assess
cross-linguistic performance, and (5) a series of diverse experiments assessing
various capabilities, including overall performance, the model's ability to
attend to images, and its tendency to generate hallucinations. We hope this
benchmark contributes to enhancing VLM capabilities beyond English.