2 citations · 4 across the 5 of their papers we have counts for
5 papers
FlowEval: Reference-based Evaluation of Generated User Interfaces
Jason Wu, Priyan Vaithilingam, Eldon Schoop +2
While large language models (LLMs) and coding agents are often applied to user interface (UI) development, developers find it difficult to reliably assess their proficiency in visu…
Improving User Interface Generation Models from Designer Feedback
Jason Wu, Amanda Swearngin, Arun Krishna Vajjala +3
Despite being trained on vast amounts of data, most LLMs are unable to reliably generate well-designed UIs. Designer feedback is essential to improving performance on UI generation…
UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback
Jason Wu, Eldon Schoop, Alan Leung +3
Large language models (LLMs) struggle to consistently generate UI code that compiles and produces visually relevant designs. Existing approaches to improve generation rely on expen…
UIClip: A Data-driven Model for Assessing User Interface Design
Jason Wu, Yi-Hao Peng, Amanda Li +3
User interface (UI) design is a difficult yet important task for ensuring the usability, accessibility, and aesthetic qualities of applications. In our paper, we develop a machine-…
Towards Automated Accessibility Report Generation for Mobile Apps
Amanda Swearngin, Jason Wu, Xiaoyi Zhang +8
Many apps have basic accessibility issues, like missing labels or low contrast. Automated tools can help app developers catch basic issues, but can be laborious or require writing…