# Can GPTs Evaluate Graphic Design Based on Design Principles?

arXiv: https://arxiv.org/abs/2410.08885 · HTML: https://mlsanity.com/paper/2410.08885/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Ferret: Refer and Ground Anything Anywhere at Any Granularity](https://mlsanity.com/paper/2310.07704/) (2023)
- [TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering](https://mlsanity.com/paper/2311.16465/) (2023)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [Graphic Design with Large Multimodal Model](https://mlsanity.com/paper/2404.14368/) (2024)
- [OpenCOLE: Towards Reproducible Automatic Graphic Design Generation](https://mlsanity.com/paper/2406.08232/) (2024)

## Built on by (most cited first, 14 of 14)

- [VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction](https://mlsanity.com/paper/2412.04237/) (2024)
- [BannerAgency: Advertising Banner Design with Multimodal LLM Agents](https://mlsanity.com/paper/2503.11060/) (2025)
- [Can Vision Language Models Assess Graphic Design Aesthetics? A Benchmark, Evaluation, and Dataset Perspective](https://mlsanity.com/paper/2603.01083/) (2026)
- [Design-o-meter: Towards Evaluating and Refining Graphic Designs](https://mlsanity.com/paper/2411.14959/) (2024)
- [Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement](https://mlsanity.com/paper/2603.22187/) (2026)
- [Agentic Design Review System](https://mlsanity.com/paper/2508.10745/) (2025)
- [Visual Interestingness Decoded: How GPT-4o Mirrors Human Interests](https://mlsanity.com/paper/2510.13316/) (2025)
- [Step-by-step Layered Design Generation](https://mlsanity.com/paper/2512.03335/) (2025)
- [Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models](https://mlsanity.com/paper/2512.12596/) (2025)
- [PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement](https://mlsanity.com/paper/2601.11747/) (2026)
- [CPT: Controllable and Editable Design Variations with Language Models](https://mlsanity.com/paper/2604.04380/) (2026)
- [Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs](https://mlsanity.com/paper/2606.00592/) (2026)
- [Editing Everything Everywhere All at Once](https://mlsanity.com/paper/2606.31278/) (2026)
- [AUV-Bench: Aesthetic Understanding and Generation Evaluation for User Interfaces](https://mlsanity.com/paper/2609.34854/) (2026)

## Abstract (arXiv)

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 .