UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset
This work addresses the gap in automated UI evaluation for designers by providing a dataset and methods to enhance LLM performance, though it is incremental as it builds on existing LLM-based techniques.
The paper tackled the problem of improving LLM-based automated UI evaluation by collecting a targeted dataset of 3,059 design critiques and quality ratings for 983 mobile UIs from experienced designers, resulting in a 55% performance gain in LLM-generated UI feedback using few-shot and visual prompting techniques.
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.