Kaleidoscope (18 Languages)

Dataset Description

The Kaleidoscope Benchmark is a global collection of multiple-choice questions sourced from real-world exams, with the goal of evaluating multimodal and multilingual understanding in VLMs. The collected exams are in a Multiple-choice question answering (MCQA) format which provides a structured framework for evaluation by prompting models with predefined answer choices, closely mimicking conventional human testing methodologies.

📄 Paper: https://arxiv.org/abs/2504.07072
🌐 Website: http://cohere.com/research/kaleidoscope

Dataset Summary

The Kaleidoscope benchmark contains 20,911 questions across 18 languages belonging to 8 language families. A total of 11,459 questions require an image to be answered (55%), while the remaining 9,452 (45%) are text-only. The dataset covers 14 different subjects, grouped into 6 broad domains.

Languages

Arabic, Bengali, Croatian, Dutch, English, French, German, Hindi, Hungarian, Lithuanian, Nepali, Persian, Portuguese, Russian, Serbian, Spanish, Telugu, Ukrainian

Topics

• Humanities & Social Sciences: Economics, Geography, History, Language, Social Sciences, Sociology
• STEM: Biology, Chemistry, Engineering, Mathematics, Physics
• Reasoning, Health Science, and Practical Skills: Reasoning, Medicine, Driving License

Data schema

An example from a UNICAMP question looks as follows:

{
   "question": "Em uma xícara que já contém certa quantidade de açúcar, despeja-se café. A curva abaixo representa a função exponencial $\\mathrm{M}(\\mathrm{t})$, que fornece a quantidade de açúcar não dissolvido (em gramas), t minutos após o café ser despejado. Pelo gráfico, podemos concluir que",
   "options": [
     "$\\mathrm{m}(\\mathrm{t})=2^{(4-\\mathrm{t} / 75)}$.",
     "$m(t)=2^{(4-t / 50)}$.",
     "$m(t)=2^{(5-t / 50)}$",
     "$m(t)=2^{(5-t / 150)}$"
   ],
   "answer": 0,
   "question_image": "unicamp_2011_30_0.png",
   "image_information": "essential",
   "image_type": "graph",
   "language": "pt",
   "country": "Brazil",
   "contributor_country": "Brazil",
   "file_name": "Unicamp2011_1fase_prova.pdf",
   "source": "https://www.curso-objetivo.br/vestibular/resolucao-comentada/unicamp/2011_1fase/unicamp2011_1fase_prova.pdf",
   "license": "Unknown",
   "level": "University Entrance",
   "category_en": "Mathematics",
   "category_source_lang": "Matemática",
   "original_question_num": 30,
 }

Here 'unicamp_2011_30_0.png' contains:

Model Performance

Models performance on the Kaleidoscope benchmark:

Model	Overall			Multimodal			Text-only
	Total Acc.	Format Err.	Valid Acc.	Total Acc.	Format Err.	Valid Acc.	Total Acc.	Format Err.	Valid Acc.
Claude 3.5 Sonnet	62.91	1.78	63.87	55.63	3.24	57.24	73.54	0.02	73.57
Gemini 1.5 Pro	62.10	1.62	62.95	55.01	1.46	55.71	72.35	1.81	73.45
GPT-4o	58.32	6.52	62.10	49.80	10.50	55.19	71.40	1.71	72.39
Qwen2.5-VL-72B	52.94	0.02	53.00	48.40	0.03	48.41	60.00	0.02	60.01
Aya Vision 32B	39.27	1.05	39.66	35.74	1.49	36.28	44.73	0.51	45.00
Qwen2.5-VL-32B	48.21	0.88	48.64	44.90	0.28	45.05	53.77	1.61	54.60
Aya Vision 8B	35.09	0.07	35.11	32.35	0.05	32.36	39.27	0.10	39.30
Molmo-7B-D	32.87	0.04	32.88	31.43	0.06	31.44	35.12	0.01	35.13
Pangea-7B	31.31	7.42	34.02	27.15	13.52	31.02	37.84	0.03	37.86
Qwen2.5-VL-7B	39.56	0.08	39.60	36.85	0.04	36.88	43.91	0.11	43.96
Qwen2.5-VL-3B	35.56	0.19	35.63	33.67	0.32	33.79	38.51	0.03	38.53

Citation

@misc{salazar2025kaleidoscopeinlanguageexamsmassively,
      title={Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation}, 
      author={Israfel Salazar and Manuel Fernández Burda and Shayekh Bin Islam and Arshia Soltani Moakhar and Shivalika Singh and Fabian Farestam and Angelika Romanou and Danylo Boiko and Dipika Khullar and Mike Zhang and Dominik Krzemiński and Jekaterina Novikova and Luísa Shimabucoro and Joseph Marvin Imperial and Rishabh Maheshwary and Sharad Duwal and Alfonso Amayuelas and Swati Rajwal and Jebish Purbey and Ahmed Ruby and Nicholas Popovič and Marek Suppa and Azmine Toushik Wasi and Ram Mohan Rao Kadiyala and Olga Tsymboi and Maksim Kostritsya and Bardia Soltani Moakhar and Gabriel da Costa Merlin and Otávio Ferracioli Coletti and Maral Jabbari Shiviari and MohammadAmin farahani fard and Silvia Fernandez and María Grandury and Dmitry Abulkhanov and Drishti Sharma and Andre Guarnier De Mitri and Leticia Bossatto Marchezi and Johan Obando-Ceron and Nazar Kohut and Beyza Ermis and Desmond Elliott and Enzo Ferrante and Sara Hooker and Marzieh Fadaee},
      year={2025},
      eprint={2504.07072},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.07072}, 
}