# MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces

arXiv: https://arxiv.org/abs/2510.08783 · HTML: https://mlsanity.com/paper/2510.08783/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models](https://mlsanity.com/paper/2405.01535/) (2024)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://mlsanity.com/paper/2210.01241/) (2022)
- [Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges](https://mlsanity.com/paper/2406.12624/) (2024)
- [Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators](https://mlsanity.com/paper/2403.16950/) (2024)
- [Wider and Deeper LLM Networks are Fairer LLM Evaluators](https://mlsanity.com/paper/2308.01862/) (2023)
- [UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset](https://mlsanity.com/paper/2407.08850/) (2024)
- [AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents](https://mlsanity.com/paper/2504.09723/) (2025)
- [How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts](https://mlsanity.com/paper/2408.06837/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts](https://mlsanity.com/paper/2505.17374/) (2025)
- [Write, Rank, or Rate: Comparing Methods for Studying Visualization Affordances](https://mlsanity.com/paper/2507.17024/) (2025)

## Built on by (most cited first, 11 of 11)

- [Human-Aligned MLLM Judges for Fine-Grained Image Editing Evaluation: A Benchmark, Framework, and Analysis](https://mlsanity.com/paper/2602.13028/) (2026)
- [WebVR: Benchmarking Multimodal LLMs for WebPage Recreation from Videos via Human-Aligned Visual Rubrics](https://mlsanity.com/paper/2603.13391/) (2026)
- [DesignPref: Capturing Personal Preferences in Visual Design Generation](https://mlsanity.com/paper/2511.20513/) (2025)
- [Avenir-UX: Automated UX Evaluation via Simulated Human Web Interaction with GUI Grounding](https://mlsanity.com/paper/2604.09581/) (2026)
- [Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study](https://mlsanity.com/paper/2607.02436/) (2026)
- [Paper2Web: Let's Make Your Paper Alive!](https://mlsanity.com/paper/2510.15842/) (2025)
- [PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://mlsanity.com/paper/2606.05697/) (2026)
- [Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge](https://mlsanity.com/paper/2603.11665/) (2026)
- [Efficient Personalization of Generative User Interfaces](https://mlsanity.com/paper/2604.09876/) (2026)
- [From Subjective Judgments to Auditable Standards:Protocol-Guided AI Auditing of Website Redundancy](https://mlsanity.com/paper/2608.21476/) (2026)
- [Language Models for Page-Level Layout Decisions in E-commerce Search](https://mlsanity.com/paper/2610.10920/) (2026)

## Abstract (arXiv)

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.