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Raidar: geneRative AI Detection viA Rewriting

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arxiv 2401.12970 v2 pith:3EHS2EQZ submitted 2024-01-23 cs.CL

Chengzhi Mao , Carl Vondrick , Hao Wang , Junfeng Yang This is my paper · ORCID
classification cs.CL
keywords textdetectionllmsai-generatedcontentmethodraidarrewriting
verification ladder T0 review T1 audit T2 compute T3 formal
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We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ARB: A Matched Authorship-Rewriting Benchmark Dataset for AI-Text Detector Evaluation

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A matched four-regime benchmark shows AI-text detectors catch direct LLM output but lose most of their recall on human text rewritten by an LLM.

  2. Who Gets Seen in the Age of AI? Adoption Patterns of Large Language Models in Scholarly Writing and Citation Outcomes

    cs.CY 2025-09 reject novelty 5.0 of 10

    Analyzing 98,000 Scopus computer science papers, the paper finds a global rise in AI-like writing after ChatGPT and reports regional differences in citation returns, but the key differential-gain result is statistical...

  3. A Comprehensive Dataset for Human vs. AI Generated Text Detection

    cs.CL 2025-10 reject novelty 4.0 of 10

    A dataset of ~58k NYT articles plus AI rewrites from six LLMs, evaluated with a rewrite-distance baseline reaching 58.35% detection and 8.92% attribution accuracy.

  4. AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Fine-tuned GPT-4o-mini detects AI versus human text at 95.5% F1 on the DeFactify test set, but identifying the specific generator LLM reaches only 47% F1 with BERT.

  5. Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes

    stat.ML 2026-07 conditional novelty 3.0 of 10

    A one-pass e-process detector for the Gumbel-max LLM watermark preserves anytime-valid Type I error control while accumulating evidence token by token.

  6. Survey on AI-Generated Media Detection: From Non-MLLM to MLLM

    cs.CV 2025-02 unverdicted novelty 3.0 of 10

    A survey organizing AI-generated media detection into Non-MLLM and MLLM based methods, with task and benchmark taxonomies.

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