AutoGUI/AutoGUI-v1-702k

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Citation text from the card

@inproceedings{li-etal-2025-autogui,
    title = "{A}uto{GUI}: Scaling {GUI} Grounding with Automatic Functionality Annotations from {LLM}s",
    author = "Li, Hongxin  and
      Chen, Jingfan  and
      Su, Jingran  and
      Chen, Yuntao  and
      Qing, Li  and
      Zhang, Zhaoxiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.510/",
    doi = "10.18653/v1/2025.acl-long.510",
    pages = "10323--10358",
    ISBN = "979-8-89176-251-0",
    abstract = "User interface understanding with vision-language models (VLMs) has received much attention due to its potential for enhancing software automation.However, existing datasets used to build UI-VLMs either only contain large-scale context-free element annotations or contextualized functional descriptions for elements at a small scale.In this work, we propose the \textbf{AutoGUI} pipeline for automatically annotating UI elements with detailed functionality descriptions at scale.Specifically, we leverage large language models (LLMs) to infer element functionality by comparing UI state changes before and after simulated interactions. To improve anno

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dataset_licence({ "id": "AutoGUI/AutoGUI-v1-702k" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0