  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.
