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arXiv Machine Learning By Hanshuai Cui, Fanshuai Meng, Zhiqing Tang

NormDetect: Anchored Calibration for Low-False-Positive Detection of Machine-Generated Web Text

Read the original on arXiv Machine Learning →

NormDetect is a new detection method that anchors a learned per-section head to a normalized zero-shot score to improve low‑false‑positive detection of machine‑generated web text. By blending the two scores with a weight chosen on training folds under a human false‑positive rate cap, it raises recall by 5.0 points over normalized Binoculars at a calibrated 0.1% FPR on the RAID benchmark. The method maintains a human FPR bound of at most 1% in every section, outperforming other detectors within this bound.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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