Automated UI evaluation can be beneficial for the design process; for example, to compare different UI designs, or conduct automated heuristic evaluation. LLM-based UI evaluation, in particular, holds the promise of generalizability to a wide variety of UI types and evaluation tasks. However, current LLM-based techniques do not yet match the performance of human evaluators. We hypothesize that automatic evaluation can be improved by collecting a targeted UI feedback dataset and then using this dataset to enhance the performance of general-purpose LLMs. We present a targeted dataset of 3,059 design critiques and quality ratings for 983 mobile UIs, collected from seven experienced designers. We carried out an in-depth analysis to characterize the dataset's features. We then applied this dataset to achieve a 55% performance gain in LLM-generated UI feedback via various few-shot and visual prompting techniques. We also discuss future applications of this dataset, including training a reward model for generative UI techniques, and fine-tuning a tool-agnostic multi-modal LLM that automates UI evaluation.

翻译：自动化UI评估对设计过程大有裨益，例如可用于比较不同的UI设计或进行自动化启发式评估。特别是基于大语言模型（LLM）的UI评估，有望广泛适用于多种UI类型和评估任务。然而，当前基于LLM的技术在性能上尚未达到人类评估者的水平。我们假设，通过收集有针对性的UI反馈数据集，并利用该数据集提升通用大语言模型的性能，可以改进自动化评估。我们提出了一个针对性数据集，包含来自七位经验丰富的设计师对983个移动端UI的3,059条设计批评和质量评分。我们进行了深入分析以刻画该数据集的特征。随后，我们应用该数据集，通过多种少样本提示和视觉提示技术，使LLM生成的UI反馈性能提升了55%。我们还讨论了该数据集的未来应用，包括为生成式UI技术训练奖励模型，以及微调一个与工具无关、可自动化UI评估的多模态大语言模型。