mlsanityData to 7 Oct 2026

Automatic Detection of Machine Generated Text: A Critical Survey

arXiv 2011.01314 ↗First posted 2 Nov 2020cs.CL cs.AIcited by 160

Not on the map. Why?

Abstract (arXiv)

Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

Key facts. Cited by 160 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 17; 2023: 53; 2024: 49; 2025: 24; 2026: 9.

160 papers.

Sort:

By area:

  1. 2021Ethical and social risks of harm from Language Modelscited by 957
  2. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
  3. 2023A Watermark for Large Language Modelscited by 667
  4. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
  5. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
  6. 2023Can AI-Generated Text be Reliably Detected?cited by 372
  7. 2023Robust Distortion-free Watermarks for Language Modelscited by 279
  8. 2023Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvaturecited by 278
  9. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  10. 2023Large Language Model Alignment: A Surveycited by 241
  11. 2023Undetectable Watermarks for Language Modelscited by 208
  12. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 207
38 more
  1. 2023GPT detectors are biased against non-native English writerscited by 175
  2. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  3. 2023On the Reliability of Watermarks for Large Language Modelscited by 170
  4. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
  5. 2022mGPT: Few-Shot Learners Go Multilingualcited by 141
  6. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  7. 2021TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generationcited by 118
  8. 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 112
  9. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 101
  10. 2023Language Model Behavior: A Comprehensive Surveycited by 92
  11. 2023Ghostbuster: Detecting Text Ghostwritten by Large Language Modelscited by 90
  12. 2023Identifying and Mitigating the Security Risks of Generative AIcited by 90
  13. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  14. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 75
  15. 2023On the Learnability of Watermarks for Language Modelscited by 72
  16. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  17. 2023Publicly-Detectable Watermarking for Language Modelscited by 69
  18. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  19. 2023Stylometric Detection of AI-Generated Text in Twitter Timelinescited by 57
  20. 2024Raidar: geneRative AI Detection viA Rewritingcited by 51
  21. 2024Community Forensics: Using Thousands of Generators to Train Fake Image Detectorscited by 50
  22. 2021Artificial Text Detection via Examining the Topology of Attention Mapscited by 49
  23. 2023Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedycited by 45
  24. 2023Towards Codable Watermarking for Injecting Multi-bits Information to LLMscited by 43
  25. 2022Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspectivecited by 40
  26. 2023GPT-Sentinel: Distinguishing Human and ChatGPT Generated Contentcited by 40
  27. 2024Few-Shot Detection of Machine-Generated Text using Style Representationscited by 40
  28. 2024AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Textcited by 40
  29. 2023Origin Tracing and Detecting of LLMscited by 39
  30. 2023GPT-who: An Information Density-based Machine-Generated Text Detectorcited by 38
  31. 2023ConDA: Contrastive Domain Adaptation for AI-generated Text Detectioncited by 37
  32. 2023Smaller Language Models are Better Black-box Machine-Generated Text Detectorscited by 35
  33. 2024Training-free LLM-generated Text Detection by Mining Token Probability Sequencescited by 33
  34. 2025Idiosyncrasies in Large Language Modelscited by 32
  35. 2024SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detectioncited by 30
  36. 2022Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russiancited by 29
  37. 2023Towards Possibilities & Impossibilities of AI-generated Text Detection: A Surveycited by 28
  38. 2024Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScorecited by 27

50 most cited of 160 All 160 (JSON)