Can GPTs Evaluate Graphic Design Based on Design Principles?

TL;DR

This paper investigates whether GPT models can reliably evaluate graphic design quality by comparing their assessments to human judgments and heuristic metrics based on fundamental design principles.

Contribution

It demonstrates that GPTs, despite limitations, show a good correlation with human evaluations and heuristic metrics in assessing graphic design quality.

Findings

GPTs correlate well with human annotations

GPTs exhibit similar tendencies to heuristic metrics

GPTs cannot detect small detail differences

Abstract

Recent advancements in foundation models show promising capability in graphic design generation. Several studies have started employing Large Multimodal Models (LMMs) to evaluate graphic designs, assuming that LMMs can properly assess their quality, but it is unclear if the evaluation is reliable. One way to evaluate the quality of graphic design is to assess whether the design adheres to fundamental graphic design principles, which are the designer's common practice. In this paper, we compare the behavior of GPT-based evaluation and heuristic evaluation based on design principles using human annotations collected from 60 subjects. Our experiments reveal that, while GPTs cannot distinguish small details, they have a reasonably good correlation with human annotation and exhibit a similar tendency to heuristic metrics based on design principles, suggesting that they are indeed capable of assessing the quality of graphic design. Our dataset is available at https://cyberagentailab.github.io/Graphic-design-evaluation .