{
 "id": "koakuma/RealDet",
 "owner": "koakuma",
 "name": "RealDet",
 "downloads": 105,
 "likes": 0,
 "last_modified": "2025-09-18T09:04:26.000Z",
 "stated_licence": "cc-by-nc-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification",
  "text-detection"
 ],
 "citation": "@inproceedings{zhu-etal-2025-reliably,\n    title = \"Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction\",\n    author = \"Zhu, Xiaowei  and\n      Ren, Yubing  and\n      Cao, Yanan  and\n      Lin, Xixun  and\n      Fang, Fang  and\n      Li, Yangxi\",\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.601/\",\n    doi = \"10.18653/v1/2025.acl-long.601\",\n    pages = \"12298--12319\",\n    ISBN = \"979-8-89176-251-0\",\n    abstract = \"The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to mitigate these risks has become a critical priority. However, most existing detection methods focus excessively on detection accuracy, often neglecting the societal risks posed by high false positive rates (FPRs). This paper addresses this issue by leveraging Conformal Prediction (CP), which effectively constrains the upper bound of FPRs. While directly applying CP constrains FPRs, it also leads to a significant reduction in detection p",
 "signal": "restrictive",
 "reason": "The declared licence (cc-by-nc-4.0) is classified as having use restrictions.",
 "revision": "0cd71b115b247d40fe66f13ce3ff8cc91832d2b3",
 "stated_evidence": "https://huggingface.co/datasets/koakuma/RealDet/blob/0cd71b115b247d40fe66f13ce3ff8cc91832d2b3/README.md",
 "file_evidence": "https://huggingface.co/datasets/koakuma/RealDet/tree/0cd71b115b247d40fe66f13ce3ff8cc91832d2b3",
 "card_nc": false
}