The Large Vision-Language Models (LVLMs) have demonstrated great abilities in
image perception and language understanding. However, existing multimodal
benchmarks focus on primary perception abilities and commonsense knowledge
which are insufficient to reflect the comprehensive capabilities of LVLMs. We
propose GAOKAO-MM, a multimodal benchmark based on the Chinese College Entrance
Examination (GAOKAO), comprising of 8 subjects and 12 types of images, such as
diagrams, function graphs, maps and photos. GAOKAO-MM derives from native
Chinese context and sets human-level requirements for the model's abilities,
including perception, understanding, knowledge and reasoning. We evaluate 10
LVLMs and find that the accuracies of all of them are lower than 50%, with
GPT-4-Vison (48.1%), Qwen-VL-Plus (41.2%) and Gemini-Pro-Vision (35.1%) ranking
in the top three positions. The results of our multi-dimension analysis
indicate that LVLMs have moderate distance towards Artificial General
Intelligence (AGI) and provide insights facilitating the development of
multilingual LVLMs.

提出了 GAOKAO-MM，这是一个基于中国高考的多模态基准，评估了 10 个大型视觉语言模型 (LVLMs)，发现它们的准确率都低于 50％，排名前三的是 GPT-4-Vison（48.1％），Qwen-VL-Plus（41.2％）和 Gemini-Pro-Vision（35.1％）。多维分析结果表明 LVLMs 在人工通用智能 (AGI) 方面有适度的距离，并为多语言 LVLMs 的发展提供了启示。