A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916068864294912 |
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| author | Liu, Yi |
| author_facet | Liu, Yi |
| contents | 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 three detection models, 19 domains, and four LLMs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_00402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering Liu, Yi Methodology Artificial Intelligence Applications 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 three detection models, 19 domains, and four LLMs. |
| title | A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering |
| topic | Methodology Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2606.00402 |