# WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code

> Diagnoses webpage-to-code capability through visual perception, HTML programming, image-to-HTML understanding, and screenshot reconstruction tasks. It separates underlying skills using question-answer pairs derived from real websites.

- Authors: Zhiyu Lin; Zhengda Zhou; Zhiyuan Zhao; Tianrui Wan; Yilun Ma; Junyu Gao; Xuelong Li
- Venue: Findings of ACL 2025
- Status: Conference paper
- Publication / posting date: July 2025
- Date source: ACL proceedings publication month.
- First arXiv submission: June 9, 2025
- arXiv: 2506.07818
- Reviewed arXiv version: v1
- DOI: 10.18653/v1/2025.findings-acl.815
- Sources reviewed: 2026-10-16
- Added to the catalog: 2026-10-16
- Original paper: <https://aclanthology.org/2025.findings-acl.815/>
- PDF: <https://aclanthology.org/2025.findings-acl.815.pdf>
- Project: <https://github.com/MAIL-Tele-AI/WebUIBench>
- BibTeX: <https://awesomerea.org/papers/citations/paper-webuibench.bib>
- Catalog page: <https://awesomerea.org/papers/paper-webuibench/>
- Record JSON: <https://awesomerea.org/papers/paper-webuibench/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-webuibench/index.md)

## Scope and limitations

Primarily evaluates visual and HTML reconstruction skills. The paper includes a preliminary interaction study, not a full workflow or backend-equivalence benchmark. The official repository currently exposes README and images but not the evaluation scripts described in its instructions.

## Why it fits this collection

Adds diagnostic evidence for screenshot-based website reconstruction, complementary to end-to-end Design2Code evaluation.

## Publication and review notes

The published ACL record supplies the bibliography; repository citation fields remain placeholders and the dataset card displays a different arXiv identifier. The author-linked repository is retained as a project link, not advertised as a runnable code release.

## Primary sources

1. <https://aclanthology.org/2025.findings-acl.815/>
2. <https://aclanthology.org/2025.findings-acl.815.pdf>
3. <https://arxiv.org/abs/2506.07818>
4. <https://github.com/MAIL-Tele-AI/WebUIBench>
5. <https://huggingface.co/datasets/Tele-AI-MAIL/WebUIBench>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.