mlsanityData to 14 Oct 2026

Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature

arXiv 2310.05130 ↗First posted 8 Oct 2023cs.CLcited by 287

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Abstract (arXiv)

Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI systems, it is imperative to distinguish between machine-generated and human-authored content. The leading zero-shot detector, DetectGPT, showcases commendable performance but is marred by its intensive computational costs. In this paper, we introduce the concept of conditional probability curvature to elucidate discrepancies in word choices between LLMs and humans within a given context. Utilizing this curvature as a foundational metric, we present **Fast-DetectGPT**, an optimized zero-shot detector, which substitutes DetectGPT's perturbation step with a more efficient sampling step. Our evaluations on various datasets, source models, and test conditions indicate that Fast-DetectGPT not only surpasses DetectGPT by a relative around 75% in both the white-box and black-box settings but also accelerates the detection process by a factor of 340, as detailed in Table 1. See urlhttps://github.com/baoguangsheng/fast-detect-gpt for code, data, and results.

Key facts. Cited by 287 papers in the map (data through 14 Oct 2026). Builds on 38 of its 60 references that are in the map. First posted on arXiv 8 Oct 2023.

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38 of 60 references.

References · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
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26 more
  1. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  2. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  3. 2018Hierarchical Neural Story Generationcited by 1,193
  4. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  5. 2023A Watermark for Large Language Modelscited by 725
  6. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  7. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  8. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  9. 2023Can AI-Generated Text be Reliably Detected?cited by 386
  10. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 261
  11. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 210
  12. 2023GPT detectors are biased against non-native English writerscited by 178
  13. 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 166
  14. 2023The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Datasetcited by 148
  15. 2022mGPT: Few-Shot Learners Go Multilingualcited by 142
  16. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 130
  17. 2023DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Textcited by 129
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  19. 2020TweepFake: about Detecting Deepfake Tweetscited by 117
  20. 2019Real or Fake? Learning to Discriminate Machine from Human Generated Textcited by 99
  21. 2023ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Textcited by 85
  22. 2022Do Language Models Plagiarize?cited by 73
  23. 2019Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detectioncited by 52
  24. 2022Watermarking Pre-trained Language Models with Backdooringcited by 50
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 70; 2025: 94; 2026: 120.

287 papers.

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  1. 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 263
  2. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 164
  3. 2024RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectorscited by 163
  4. 2024CycleResearcher: Improving Automated Research via Automated Reviewcited by 148
  5. 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 118
  6. 2024LLM Dataset Inference: Did you train on my dataset?cited by 110
  7. 2024Why Tabular Foundation Models Should Be a Research Prioritycited by 106
  8. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 105
  9. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 76
  10. 2023A Survey on Detection of LLMs-Generated Contentcited by 71
  11. 2025DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressivelycited by 63
  12. 2024SoK: Watermarking for AI-Generated Contentcited by 51
38 more
  1. 2024M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detectioncited by 43
  2. 2024AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Textcited by 42
  3. 2025People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated textcited by 40
  4. 2024DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarioscited by 38
  5. 2025Are You Getting What You Pay For? Auditing Model Substitution in LLM APIscited by 38
  6. 2024GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickcited by 37
  7. 2024Training-free LLM-generated Text Detection by Mining Token Probability Sequencescited by 35
  8. 2024Your Large Language Models Are Leaving Fingerprintscited by 33
  9. 2025Idiosyncrasies in Large Language Modelscited by 33
  10. 2024Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmerscited by 27
  11. 2024Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScorecited by 27
  12. 2024Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attackscited by 26
  13. 2024RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Contentcited by 26
  14. 2025EditLens: Quantifying the Extent of AI Editing in Textcited by 26
  15. 2024Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihoodcited by 24
  16. 2025RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patternscited by 24
  17. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  18. 2024Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detectioncited by 23
  19. 2024Permute-and-Flip: An optimally stable and watermarkable decoder for LLMscited by 22
  20. 2024The Rise of AI-Generated Content in Wikipediacited by 22
  21. 2024GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levelscited by 21
  22. 2025Is Your Paper Being Reviewed by an LLM? Benchmarking AI Text Detection in Peer Reviewcited by 21
  23. 2025AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guaranteescited by 21
  24. 2025DAMAGE: Detecting Adversarially Modified AI Generated Textcited by 20
  25. 2024Authorship Obfuscation in Multilingual Machine-Generated Text Detectioncited by 19
  26. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 19
  27. 2025Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writingcited by 19
  28. 2024The Impact of Large Language Models in Academia: from Writing to Speakingcited by 18
  29. 2025Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Textcited by 17
  30. 2024Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Textcited by 16
  31. 2024Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicatedcited by 15
  32. 2025HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoringcited by 15
  33. 2024MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Textscited by 14
  34. 2024Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detectioncited by 14
  35. 2024EAGLE: A Domain Generalization Framework for AI-generated Text Detectioncited by 13
  36. 2024DALD: Improving Logits-based Detector without Logits from Black-box LLMscited by 13
  37. 2024SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphscited by 13
  38. 2024Learning to Rewrite: Generalized LLM-Generated Text Detectioncited by 13

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