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arXiv Computation and Language By Hasan Amin, Zhuoran Lu, Ming Yin

Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?

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The paper argues that current AI‑text detection methods, which estimate human contribution by measuring how predictive a user’s prompt is of the final text, can be easily manipulated. By introducing SPECIFICITY PADDING—adding random IDs to the prompt—the authors show that a title‑only request can appear far more human than one containing the article’s key ideas, and that scores can swing dramatically when the evaluator’s view of the prompt is altered. Across a range of large language models, the same documents can be scored as 85 % human or 15 % human depending solely on the prompt used, revealing a fundamental limitation of likelihood‑based metrics for reliably measuring human authorship. whyItMatters":"The study demonstrates that likelihood‑based scores cannot reliably establish human contribution without additional evidence of what the person added and how the text was produced."

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 Computation and Language.

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