  The importance of computational modeling of mobile user interfaces (UIs) is
undeniable. However, these require a high-quality UI dataset. Existing datasets
are often outdated, collected years ago, and are frequently noisy with
mismatches in their visual representation. This presents challenges in modeling
UI understanding in the wild. This paper introduces a novel approach to
automatically mine UI data from Android apps, leveraging Large Language Models
(LLMs) to mimic human-like exploration. To ensure dataset quality, we employ
the best practices in UI noise filtering and incorporate human annotation as a
final validation step. Our results demonstrate the effectiveness of
LLMs-enhanced app exploration in mining more meaningful UIs, resulting in a
large dataset MUD of 18k human-annotated UIs from 3.3k apps. We highlight the
usefulness of MUD in two common UI modeling tasks: element detection and UI
retrieval, showcasing its potential to establish a foundation for future
research into high-quality, modern UIs.
