UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset

TL;DR

This paper introduces UICrit, a dataset of 3,059 UI critiques aimed at improving automated UI evaluation by enhancing LLM performance through targeted feedback, with potential applications in training reward models and fine-tuning multi-modal LLMs.

Contribution

The paper presents a new dataset of UI critiques and demonstrates how it significantly improves LLM-based UI evaluation performance.

Findings

55% performance improvement in LLM-generated UI feedback

Dataset contains 3,059 critiques for 983 mobile UIs

Potential for training reward models and fine-tuning multi-modal LLMs

Abstract

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.