osunlp/Online-Mind2Web

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Online-Mind2Web

Online-Mind2Web is the online version of Mind2Web, a more diverse and user-centric dataset includes 300 high-quality tasks from 136 popular websites across various domains. The dataset covers a diverse set of user tasks, such as clothing, food, housing, and transportation, to evaluate web agents' performance in a real-world online environment.

Data Fields

• "task_id" (str): Unique id for each task.
• "website" (str): Website url.
• "task_description" (str): Task description.
• "reference_length" (int): Number of steps required for a human annotator to complete the task.

Disclaimer

This dataset was collected and released solely for research purposes, with the goal of making the web more accessible via language technologies. The authors are strongly against any potential harmful use of the data or technology to any party.

Citation Information

Note: Online-Mind2Web is derived from the original Mind2Web dataset. We kindly ask that you cite both the original and this work when using or referencing the data.

@article{xue2025webagents,
  title   = "An Illusion of Progress? Assessing the Current State of Web Agents",
  author  = "Xue, Tianci and Qi, Weijian and Shi, Tianneng and Song, Chan Hee and Gou, Boyu and Song, Dawn and Sun, Huan and Su, Yu",
  journal = "OSU NLP Blog",
  year    = "2025",
  month   = "Mar",
  url     = "https://tiancixue.notion.site/An-Illusion-of-Progress-Assessing-the-Current-State-of-Web-Agents-1ac6cd2b9aac80719cd6f68374aaf4b4"
}

@inproceedings{deng2023mind2web,
 author = {Deng, Xiang and Gu, Yu and Zheng, Boyuan and Chen, Shijie and Stevens, Sam and Wang, Boshi and Sun, Huan and Su, Yu},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
 pages = {28091--28114},
 publisher = {Curran Associates, Inc.},
 title = {Mind2Web: Towards a Generalist Agent for the Web},
 url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf},
 volume = {36},
 year = {2023}
}