Key Points
1. The paper introduces the Design2Code task, which aims to automate front-end engineering by converting visual designs into code implementations using multimodal language models (LLMs). The paper benchmarks 484 real-world webpages for this task and develops automatic evaluation metrics to assess the capabilities of multimodal LLMs in generating code implementations from screenshots.
2. The paper shows that GPT-4V performs the best on this task compared to other models and that GPT-4V's generated webpages were considered better than the original reference webpages in 64% of cases by human evaluators.
3. The paper also explores multimodal prompting methods, such as text-augmented and self-revision prompting, showing their effectiveness in improving model performance, and introduces an open-source 18B finetuned model, Design2Code-18B, which matches the performance of commercial models on the benchmark.
4. The benchmark dataset covers a wide spectrum of HTML tag uses, domains, and complexity levels, making it a comprehensive and representative resource for evaluating multimodal LLMs for the Design2Code task.
5. The paper highlights the challenges in code generation from user interface (UI) designs, including diversity in visual and text signals and the vast search space in resulting code. It also emphasizes the potential of effective automatic generation of functional code from visual designs in democratizing the development of front-end web applications.
6. The discrepancy between automatic and human evaluations is discussed, with the correlation between automatic metrics and human judgment indicating that humans pay more attention to high-level visual effects and layout when evaluating generated webpages.
7. The paper provides insights into the learning process of different dimensions, analyzing how the performance of automatic evaluation metrics evolves during model training.
8. The work is positioned in the context of related research on code LLMs, programming support tools, and multimodal LLMs, underscoring the potential implications and applications of the Design2Code benchmark in advancing research and development in these areas.
9. The paper also considers ethical implications and dual use concerns, emphasizing the intended use of Design2Code technologies for research purposes and providing ethical use guidelines for data, code, and model releases.
Summary
The paper introduces the Design2Code benchmark to assess the capabilities of multimodal language models in converting visual designs into functional code implementations for front-end development. It meticulously curated a benchmark of 484 real-world webpages and developed automatic evaluation metrics to assess model performance against human evaluations. The study showcased the effectiveness of multimodal prompting methods on models like GPT-4V and Gemini Pro Vision, and the finetuning of an open-source Design2Code-18B model.
The results indicated that GPT-4V performed best among the models, with its webpages being preferred over the original reference webpages in 49% of cases visually and 64% considered better designed. The paper also highlighted areas for future research, including exploring better prompting techniques, training multimodal LLMs with real-world webpages, and extending the benchmark to include dynamic webpages. Additionally, the authors addressed the privacy and ethical considerations associated with the dual use of Design2Code technologies. The research sets a foundation for further exploration of AI-powered code generation and its implications for democratizing webpage building while addressing potential misuse.
Reference: https://arxiv.org/abs/2403.031...