EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models

Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Iliyanov Dimitrov, Ivan Koychev, Preslav Nakov

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi & Sofia University

This is arxiv link for the EXAMS-V paper can be found here.

Introduction

We introduce EXAMS-V, a new challenging multi-discipline multimodal multilingual exam benchmark for evaluating vision language models. It consists of 20,932 multiple-choice questions across 20 school disciplines covering natural science, social science, and other miscellaneous studies, e.g.,~religion, fine arts, business, etc. EXAMS-V includes a variety of multimodal features such as text, images, tables, figures, diagrams, maps, scientific symbols, and equations. The questions come in 11 languages from 7 language families. Unlike existing benchmarks, EXAMS-V is uniquely curated by gathering school exam questions from various countries, with a variety of education systems. This distinctive approach calls for intricate reasoning across diverse languages and relies on region-specific knowledge. Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image. Our evaluation results demonstrate that this is a challenging dataset, which is difficult even for advanced vision--text models such as GPT-4V and Gemini; this underscores the inherent complexity of the dataset and its significance as a future benchmark.

Dataset Statistics

The following table shows the statistics of EXAMS-V dataset. The languages are ordered from high-resource to low-resource languages. Here, # visual Q. refers to questions with multimodal context and # text Q. refers to text only questions.

Language	Family	Grade	# Subjects	# Questions	# visual Q.	# text Q.
English	Germanic	11, 12	4	724	181	543
Chinese	Sino-Tibetan	8-12	6	2,635	1,991	644
French	Romance	12	3	439	50	389
German	Germanic	12	5	819	144	675
Italian	Romance	12	11	1,645	292	1,353
Arabic	Semitic	4-12	6	823	117	706
Polish	Slavic	12	1	2,511	422	2,089
Hungarian	Finno-Ugric	12	6	3,801	495	3,306
Bulgarian	Slavic	4, 12	4	2,132	435	1,697
Croatian	Slavic	12	13	3,969	700	3,269
Serbian	Slavic	12	11	1,434	259	1,175

The following is a pie-diagram showing the distribution of subject and languages in the dataset.

Dataset

The dataset will be released soon.

Citation

If you find this useful in your research, please consider citing it:

@misc{das2024examsv,
      title={EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models}, 
      author={Rocktim Jyoti Das and Simeon Emilov Hristov and Haonan Li and Dimitar Iliyanov Dimitrov and Ivan Koychev and Preslav Nakov},
      year={2024},
      eprint={2403.10378},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}