mlsanityData to 7 Oct 2026

GLTR: Statistical Detection and Visualization of Generated Text

arXiv 1906.04043 ↗First posted 10 Jun 2019cs.CL cs.AI cs.HCcited by 490

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Cited by 34 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs

Key facts. Cited by 490 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2019.

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Citing papers per year: 2021: 8; 2022: 25; 2023: 108; 2024: 114; 2025: 114; 2026: 121.

490 papers.

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  1. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  2. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
  3. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
  4. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 401
  5. 2022Self-critiquing models for assisting human evaluatorscited by 372
  6. 2023Can AI-Generated Text be Reliably Detected?cited by 372
  7. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  8. 2023Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvaturecited by 278
  9. 2023Provable Robust Watermarking for AI-Generated Textcited by 259
  10. 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 255
  11. 2023Challenges and Applications of Large Language Modelscited by 251
  12. 2023Undetectable Watermarks for Language Modelscited by 208
38 more
  1. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 207
  2. 2023RADAR: Robust AI-Text Detection via Adversarial Learningcited by 187
  3. 2023GPT detectors are biased against non-native English writerscited by 175
  4. 2023On the Reliability of Watermarks for Large Language Modelscited by 170
  5. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
  6. 2024RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectorscited by 157
  7. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  8. 2023MAGE: Machine-generated Text Detection in the Wildcited by 130
  9. 2023DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Textcited by 127
  10. 2023The Science of Detecting LLM-Generated Textscited by 126
  11. 2021TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generationcited by 118
  12. 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 118
  13. 2023Who Wrote this Code? Watermarking for Code Generationcited by 116
  14. 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 112
  15. 2023Intrinsic Dimension Estimation for Robust Detection of AI-Generated Textscited by 111
  16. 2022Human heuristics for AI-generated language are flawedcited by 104
  17. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 101
  18. 2023MGTBench: Benchmarking Machine-Generated Text Detectioncited by 90
  19. 2023Ghostbuster: Detecting Text Ghostwritten by Large Language Modelscited by 90
  20. 2023Identifying and Mitigating the Security Risks of Generative AIcited by 90
  21. 2023On the Possibilities of AI-Generated Text Detectioncited by 88
  22. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  23. 2023Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Modelscited by 77
  24. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 76
  25. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 75
  26. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  27. 2022RankGen: Improving Text Generation with Large Ranking Modelscited by 72
  28. 2023On the Learnability of Watermarks for Language Modelscited by 72
  29. 2024Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluationcited by 72
  30. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  31. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  32. 2023Publicly-Detectable Watermarking for Language Modelscited by 69
  33. 2022Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Textcited by 63
  34. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  35. 2023Mark My Words: Analyzing and Evaluating Language Model Watermarkscited by 62
  36. 2023Multiscale Positive-Unlabeled Detection of AI-Generated Textscited by 59
  37. 2023Stylometric Detection of AI-Generated Text in Twitter Timelinescited by 57
  38. 2023SeqXGPT: Sentence-Level AI-Generated Text Detectioncited by 55

50 most cited of 490 All 490 (JSON)