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arXiv Machine Learning By Lukas Gehring, Benjamin Paa{\ss}en

Limits of LLM Text Detectors in Education

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The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.

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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