MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human
  Perception of User Interfaces
Authors: name: Reuben A. Luera, name: Ryan Rossi, name: Franck Dernoncourt, name: Samyadeep Basu, name: Sungchul Kim, name: Subhojyoti Mukherjee, name: Puneet Mathur, name: Ruiyi Zhang, name: Jihyung Kil, name: Nedim Lipka, name: Seunghyun Yoon, name: Jiuxiang Gu, name: Zichao Wang, name: Cindy Xiong Bearfield, name: Branislav Kveton

In an ideal design pipeline, user interface (UI) design is intertwined with
user research to validate decisions, yet studies are often resource-constrained
during early exploration. Recent advances in multimodal large language models
(MLLMs) offer a promising opportunity to act as early evaluators, helping
designers narrow options before formal testing. Unlike prior work that
emphasizes user behavior in narrow domains such as e-commerce with metrics like
clicks or conversions, we focus on subjective user evaluations across varied
interfaces. We investigate whether MLLMs can mimic human preferences when
evaluating individual UIs and comparing them. Using data from a crowdsourcing
platform, we benchmark GPT-4o, Claude, and Llama across 30 interfaces and
examine alignment with human judgments on multiple UI factors. Our results show
that MLLMs approximate human preferences on some dimensions but diverge on
others, underscoring both their potential and limitations in supplementing
early UX research.