用设计原则评估图形设计，GPT表现接近人类。
Can GPTs Evaluate Graphic Design Based on Design Principles?
- 用60人标注数据对比GPT与设计原则评估方法
- GPT评分与人类评分相关性高，趋势一致
- 适合对设计质量进行快速自动化评估
近期基础模型在图形设计生成方面展现出良好能力。一些研究开始使用大模型（LMMs）评估设计质量，但其可靠性尚不明确。本研究通过60名受试者的人类标注数据，比较GPT评估与基于设计原则的启发式评估行为。实验表明，尽管GPT难以识别细微差异，但其评分与人类标注具有合理相关性，且趋势与基于设计原则的启发式指标相似，说明其确实具备评估图形设计质量的能力。数据集已公开于https://cyberagentailab.github.io/Graphic-design-evaluation。
原文摘要 · Abstract (English)
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 .
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