A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining. Our key observation is that rewrite-based detection implicitly constructs knockoff samples, enabling LLM-generated text detection to be formulated as a multiple hypothesis testing problem with knockoff structure. This perspective separates the design of detection statistics from the control of false discoveries, allowing existing rewrite detectors to inherit finite-sample false discovery rate (FDR) guarantees through a simple calibration procedure. We demonstrate reliable FDR control with meaningful detection power across...
| Authors | Yi Liu |
|---|---|
| Primary category | stat.ME |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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