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

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arxiv 2312.07755 v1 pith:AC2K4PK4 submitted 2023-12-12 cs.HC

Sidong Feng , Mingyue Yuan , Jieshan Chen , Zhenchang Xing , Chunyang Chen This is my paper · ORCID
classification cs.HC
keywords designlanguagewireframesgenerativeintentlargemid-fidelitymodels
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World

    cs.SE 2026-02 conditional novelty 6.0 of 10

    A new e-commerce-sourced benchmark evaluates design-to-code LLMs by requiring executable React generation and iterative revision from a noisy design IR plus a reference image.

  2. DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models

    cs.SE 2025-06 conditional novelty 6.0 of 10

    A hierarchy-aware, self-correcting LLM pipeline for generating React Native UI code improves visual fidelity and structural similarity over baselines on 300 mockups.

  3. Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation

    cs.SE 2024-12 conditional novelty 6.0 of 10

    A self-critique prompting loop outperformed retrieval-augmented and decomposed prompting for zero-shot generation of high-fidelity GUI prototypes, based on over 3,000 crowdworker ratings.

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