ViExam: Are Vision Language Models Better than Humans on Vietnamese
  Multimodal Exam Questions?
Authors: name: Vy Tuong Dang, name: An Vo, name: Quang Tau, name: Duc Dm, name: Daeyoung Kim

Vision language models (VLMs) demonstrate remarkable capabilities on English
multimodal tasks, but their performance on low-resource languages with
genuinely multimodal educational content remains largely unexplored. In this
work, we test how VLMs perform on Vietnamese educational assessments,
investigating whether VLMs trained predominantly on English data can handle
real-world cross-lingual multimodal reasoning. Our work presents the first
comprehensive evaluation of VLM capabilities on multimodal Vietnamese exams
through proposing ViExam, a benchmark containing 2,548 multimodal questions. We
find that state-of-the-art VLMs achieve only 57.74% while open-source models
achieve 27.70% mean accuracy across 7 academic domains, including Mathematics,
Physics, Chemistry, Biology, Geography, Driving Test, and IQ Test. Most VLMs
underperform average human test-takers (66.54%), with only the thinking VLM o3
(74.07%) exceeding human average performance, yet still falling substantially
short of human best performance (99.60%). Cross-lingual prompting with English
instructions while maintaining Vietnamese content fails to improve performance,
decreasing accuracy by 1 percentage point for SOTA VLMs. Human-in-the-loop
collaboration can partially improve VLM performance by 5 percentage points.
Code and data are available at: https://vi-exam.github.io.