Graphical user interface (GUI) prototyping represents an essential activity in the development of interactive systems, which are omnipresent today. GUI prototypes facilitate elicitation of requirements and help to test, evaluate, and validate ideas with users and the development team. However, creating GUI prototypes is a time-consuming process and often requires extensive resources. While existing research for automatic GUI generation focused largely on resource-intensive training and fine-tuning of LLMs, mainly for low-fidelity GUIs, we investigate the potential and effectiveness of Zero-Shot (ZS) prompting for high-fidelity GUI generation. We propose a Retrieval-Augmented GUI Generation (RAGG) approach, integrated with an LLM-based GUI retrieval re-ranking and filtering mechanism based on a large-scale GUI repository. In addition, we adapt Prompt Decomposition (PDGG) and Self-Critique (SCGG) for GUI generation. To evaluate the effectiveness of the proposed ZS prompting approaches for GUI generation, we extensively evaluated the accuracy and subjective satisfaction of the generated GUI prototypes. Our evaluation, which encompasses over 3,000 GUI annotations from over 100 crowd-workers with UI/UX experience, shows that SCGG, in contrast to PDGG and RAGG, can lead to more effective GUI generation, and provides valuable insights into the defects that are produced by the LLMs in the generated GUI prototypes.

翻译：图形用户界面（GUI）原型设计是交互系统开发中的核心环节，而这类系统在当今社会已无处不在。GUI原型有助于需求获取，并能帮助开发团队与用户共同测试、评估和验证设计理念。然而，创建GUI原型通常耗时且需要大量资源。现有关于自动GUI生成的研究主要集中于对大型语言模型进行资源密集型的训练与微调，且多针对低保真度GUI。本研究则探讨了零样本提示在高保真度GUI生成中的潜力与有效性。我们提出了一种检索增强的GUI生成方法，该方法与基于大型语言模型的GUI检索重排序及过滤机制相结合，并依托于大规模GUI资源库。此外，我们还将提示分解与自我批判策略适配于GUI生成任务。为评估所提出的零样本提示方法在GUI生成中的有效性，我们全面评估了生成GUI原型的准确性和主观满意度。我们的评估涵盖了来自100多名具有UI/UX经验的众包工作者提供的超过3000份GUI标注，结果表明：相较于提示分解和检索增强生成方法，自我批判策略能实现更有效的GUI生成，并为大型语言模型在生成GUI原型过程中产生的缺陷提供了有价值的洞见。