mlsanityData to 14 Oct 2026

Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text

arXiv 2309.07689 ↗First posted 14 Sep 2023cs.CL cs.AIcited by 10

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Abstract (arXiv)

While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem. These models can potentially deceive by generating artificial text that appears to be human-generated. This issue is particularly significant in domains such as law, education, and science, where ensuring the integrity of text is of the utmost importance. This survey provides an overview of the current approaches employed to differentiate between texts generated by humans and ChatGPT. We present an account of the different datasets constructed for detecting ChatGPT-generated text, the various methods utilized, what qualitative analyses into the characteristics of human versus ChatGPT-generated text have been performed, and finally, summarize our findings into general insights

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 43 of its 58 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

43 of 58 references.

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Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 3.

10 papers.

  1. 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 263
  2. 2023A Survey on Detection of LLMs-Generated Contentcited by 71
  3. 2024TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identificationcited by 36
  4. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  5. 2024AI "News" Content Farms Are Easy to Make and Hard to Detect: A Case Study in Italiancited by 12
  6. 2025Linguistic Characteristics of AI-Generated Text: A Surveycited by 7
  7. 2023Co-audit: tools to help humans double-check AI-generated contentcited by 5
  8. 2024Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweetscited by 1
  9. 2025Detecting Sockpuppetry on Wikipedia Using Meta-Learningcited by 1
  10. 2025Stylometry recognizes human and LLM-generated texts in short samplescited by 0