mlsanityData to 7 Oct 2026

The Science of Detecting LLM-Generated Texts

arXiv 2303.07205 ↗First posted 4 Feb 2023cs.CL cs.AIcited by 126

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Cited by 21 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. Although many detection approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models. Furthermore, we emphasize crucial considerations for future research, including the development of comprehensive evaluation metrics and the threat posed by open-source LLMs, to drive progress in the area of LLM-generated text detection.

Key facts. Cited by 126 papers in the map (data through 7 Oct 2026). Builds on 39 of its 87 references that are in the map. First posted on arXiv 4 Feb 2023.

Builds on

39 of 87 references.

References · 39

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27 more
  1. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
  2. 2023A Watermark for Large Language Modelscited by 667
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  4. 2019Defending Against Neural Fake Newscited by 553
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  6. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
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  8. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
  9. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
  10. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  11. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
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  13. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 121
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  20. 2020Neural Deepfake Detection with Factual Structure of Textcited by 40
  21. 2020RoFT: A Tool for Evaluating Human Detection of Machine-Generated Textcited by 36
  22. 2022CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learningcited by 32
  23. 2021Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Modelcited by 28
  24. 2019How Decoding Strategies Affect the Verifiability of Generated Textcited by 27
  25. 2022Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformerscited by 25
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 41; 2024: 46; 2025: 18; 2026: 21.

126 papers.

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  1. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  2. 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 255
  3. 2023Challenges and Applications of Large Language Modelscited by 251
  4. 2023Self-Consuming Generative Models Go MADcited by 239
  5. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  6. 2023On the Reliability of Watermarks for Large Language Modelscited by 170
  7. 2023Personality Traits in Large Language Modelscited by 166
  8. 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 118
  9. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 101
  10. 2023A Semantic Invariant Robust Watermark for Large Language Modelscited by 97
  11. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 88
  12. 2023OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examplescited by 80
38 more
  1. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 76
  2. 2024Delving into LLM-assisted writing in biomedical publications through excess vocabularycited by 75
  3. 2023REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Modelscited by 64
  4. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  5. 2023Contrasting Linguistic Patterns in Human and LLM-Generated News Textcited by 62
  6. 2023Mark My Words: Analyzing and Evaluating Language Model Watermarkscited by 62
  7. 2024Do LLMs write like humans? Variation in grammatical and rhetorical stylescited by 62
  8. 2023Large Language Models in Education: Vision and Opportunitiescited by 54
  9. 2024Raidar: geneRative AI Detection viA Rewritingcited by 51
  10. 2023LLMDet: A Third Party Large Language Models Generated Text Detection Toolcited by 44
  11. 2024Pseudorandom Error-Correcting Codescited by 40
  12. 2023Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarkingcited by 37
  13. 2023Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websitescited by 37
  14. 2024Securing Large Language Models: Threats, Vulnerabilities and Responsible Practicescited by 36
  15. 2023Anatomy of an AI-powered malicious social botnetcited by 35
  16. 2023Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Casescited by 32
  17. 2023HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysiscited by 30
  18. 2023On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writingcited by 26
  19. 2024Beemo: Benchmark of Expert-edited Machine-generated Outputscited by 26
  20. 2023Towards Optimal Statistical Watermarkingcited by 25
  21. 2024LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detectioncited by 25
  22. 2024Embedding-based classifiers can detect prompt injection attackscited by 24
  23. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  24. 2023Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflowcited by 22
  25. 2024The Rise of AI-Generated Content in Wikipediacited by 22
  26. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  27. 2023To ChatGPT, or not to ChatGPT: That is the question!cited by 20
  28. 2024Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancycited by 20
  29. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  30. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 18
  31. 2025BiMark: Unbiased Multilayer Watermarking for Large Language Modelscited by 18
  32. 2023ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLPcited by 16
  33. 2024Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurementcited by 16
  34. 2023Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Textcited by 15
  35. 2025Human-LLM Coevolution: Evidence from Academic Writingcited by 15
  36. 2024Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detectioncited by 14
  37. 2024Zero-Shot Detection of LLM-Generated Text using Token Cohesivenesscited by 13
  38. 2024Lateral Phishing With Large Language Models: A Large Organization Comparative Studycited by 12

50 most cited of 126 All 126 (JSON)