  A well-executed graphic design typically achieves harmony in two levels, from
the fine-grained design elements (color, font and layout) to the overall
design. This complexity makes the comprehension of graphic design challenging,
for it needs the capability to both recognize the design elements and
understand the design. With the rapid development of Multimodal Large Language
Models (MLLMs), we establish the DesignProbe, a benchmark to investigate the
capability of MLLMs in design. Our benchmark includes eight tasks in total,
across both the fine-grained element level and the overall design level. At
design element level, we consider both the attribute recognition and semantic
understanding tasks. At overall design level, we include style and metaphor. 9
MLLMs are tested and we apply GPT-4 as evaluator. Besides, further experiments
indicates that refining prompts can enhance the performance of MLLMs. We first
rewrite the prompts by different LLMs and found increased performances appear
in those who self-refined by their own LLMs. We then add extra task knowledge
in two different ways (text descriptions and image examples), finding that
adding images boost much more performance over texts.
