Designing with Language: Wireframing UI Design Intent with Generative Large Language Models

Sidong Feng; Mingyue Yuan; Jieshan Chen; Zhenchang Xing; Chunyang Chen

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arXiv · 2312.07755

Designing with Language: Wireframing UI Design Intent with Generative Large Language Models

Sidong Feng·Mingyue Yuan·Jieshan Chen·Zhenchang Xing·Chunyang Chen

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Abstract

Wireframing is a critical step in the UI design process. Mid-fidelity wireframes offer more impactful and engaging visuals compared to low-fidelity versions. However, their creation can be time-consuming and labor-intensive, requiring the addition of actual content and semantic icons. In this paper, we introduce a novel solution WireGen, to automatically generate mid-fidelity wireframes with just a brief design intent description using the generative Large Language Models (LLMs). Our experiments demonstrate the effectiveness of WireGen in producing 77.5% significantly better wireframes, outperforming two widely-used in-context learning baselines. A user study with 5 designers further validates its real-world usefulness, highlighting its potential value to enhance UI design process.

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Sidong Feng, Mingyue Yuan, Jieshan Chen, Zhenchang Xing, Chunyang Chen. 2023-12-12. Designing with Language: Wireframing UI Design Intent with Generative Large Language Models. https://arxiv.org/abs/2312.07755

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