ViExam: Are Vision Language Models Better than Humans on Vietnamese
  Multimodal Exam Questions?
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.
68a5830038160bfd39426a76
Vy Tuong Dang
claimed_verified
2025-08-21T10:30:47.794Z
67a87266e113700e5ee1c3fc
/avatars/ea21f5d239244b810c892333663b5390.svg
Tường Vy
user
tuongvy2603
68a5830038160bfd39426a77
An Vo
claimed_verified
2025-08-21T09:16:18.691Z
6631fd5961a4305e5610d403
https://cdn-avatars.huggingface.co/v1/production/uploads/6631fd5961a4305e5610d403/P1Dtxzn-KIbYDDsiw60nr.jpeg
An Vo
user
anvo25
68a5830038160bfd39426a78
Quang Tau
68a5830038160bfd39426a79
Duc Dm
68a5830038160bfd39426a7a
Daeyoung Kim
ViExam: Are Vision Language Models Better than Humans on Vietnamese
  Multimodal Exam Questions?
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.
VLMs perform poorly on Vietnamese educational assessments, with state-of-the-art models achieving lower accuracy than human test-takers, and cross-lingual prompting does not significantly improve performance.
vision language models VLMs Vietnamese educational assessments cross-lingual multimodal reasoning ViExam multimodal questions academic domains Mathematics Physics Chemistry Biology Geography Driving Test IQ Test human-in-the-loop collaboration