koakuma/RealDet

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The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-4.0, from the tagEvidence: README.md at 0cd71b1
Licence files: No licence file at the top level of the repositoryEvidence: file list at 0cd71b1
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@inproceedings{zhu-etal-2025-reliably,
    title = "Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction",
    author = "Zhu, Xiaowei  and
      Ren, Yubing  and
      Cao, Yanan  and
      Lin, Xixun  and
      Fang, Fang  and
      Li, Yangxi",
    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.601/",
    doi = "10.18653/v1/2025.acl-long.601",
    pages = "12298--12319",
    ISBN = "979-8-89176-251-0",
    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

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dataset_licence({ "id": "koakuma/RealDet" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0