Title: Kaleidoscope: In-language Exams for Massively  Multilingual Vision Evaluation
Primary area: datasets and benchmarks
Keywords: multilingual benchmarks, vision-language models, multimodal evaluation, cultural diversity, low-resource languages, machine learning evaluation

Abstract: The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and language, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam benchmark to date for the multilingual evaluation of vision-language models. Kaleidoscope is a large-scale, in-language multimodal benchmark designed to evaluate VLMs across diverse languages and visual inputs. Kaleidoscope covers 18 languages and 14 different subjects, amounting to a total of 20,911 multiple-choice questions. Built through an open science collaboration with a diverse group of researchers worldwide, Kaleidoscope ensures linguistic and cultural authenticity. We evaluate top-performing multilingual vision-language models and find that they perform poorly on low-resource languages and in complex multimodal scenarios. Our results highlight the need for progress on culturally inclusive multimodal evaluation frameworks.

TL;DR: We introduce a large-scale, culturally grounded multimodal multilingual benchmark to evaluate vision-language models across 18 languages and 14 subjects, revealing performance gaps across modalitities and low-resource settings.