{
 "id": "AutoGUI/AutoGUI-v1-702k",
 "owner": "AutoGUI",
 "name": "AutoGUI-v1-702k",
 "downloads": 923,
 "likes": 8,
 "last_modified": "2025-12-20T06:13:53.000Z",
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 "citation": "@inproceedings{li-etal-2025-autogui,\n    title = \"{A}uto{GUI}: Scaling {GUI} Grounding with Automatic Functionality Annotations from {LLM}s\",\n    author = \"Li, Hongxin  and\n      Chen, Jingfan  and\n      Su, Jingran  and\n      Chen, Yuntao  and\n      Qing, Li  and\n      Zhang, Zhaoxiang\",\n    editor = \"Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher\",\n    booktitle = \"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.acl-long.510/\",\n    doi = \"10.18653/v1/2025.acl-long.510\",\n    pages = \"10323--10358\",\n    ISBN = \"979-8-89176-251-0\",\n    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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 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "937c77379af0500ea36419b95473615eb0b83489",
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