WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
Rabiul Awal, Mahsa Massoud, Aarash Feizi, Zichao Li, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez
Abstract
We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models’ abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.
BibTeX
@inproceedings{emnlp2025_webmmuabenchmark,
title = {WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation},
author = {Rabiul Awal and Mahsa Massoud and Aarash Feizi and Zichao Li and Suyuchen Wang and Christopher Pal and Aishwarya Agrawal and David Vazquez and Siva Reddy and Juan A. Rodriguez and Perouz Taslakian and Spandana Gella and Sai Rajeswar},
booktitle = {EMNLP 2025},
year = {2025}
}