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

arXiv: https://arxiv.org/abs/2312.07755 · HTML: https://mlsanity.com/paper/2312.07755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [On AI-Inspired UI-Design](https://mlsanity.com/paper/2406.13631/) (2024)
- [DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment](https://mlsanity.com/paper/2511.15331/) (2025)
- [1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World](https://mlsanity.com/paper/2602.18548/) (2026)

## Abstract (arXiv)

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.