Automatic Detection of Machine Generated Text: A Critical Survey
Open accessAbstract
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.
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LLMs frequently commit factual and common-sense reasoning errors, which are often overlooked by neural network-based detectors but easily noticed by humans.
“In addition, LLMs frequently commit factual and common-sense reasoning errors, which, while often overlooked by neural network-based detectors, are easily noticed by humans (Jawahar, Abdul-Mageed, and Lakshmanan 2020).”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Jawahar, Abdul-Mageed, and Lakshmanan 2020
Section 5 (page not provided) identifies factual errors in 10 machine-generated reviews that the RoBERTa detector incorrectly classified as human-written, and a common-sense violation in one. Section 4.4 (page not provided) reports that human raters are better than automatic detectors at noticing contradictions and semantic errors. These passages support the comparison, but not the broader claim that LLMs frequently make both kinds of error or that humans easily notice them in general. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe majority of previous research on automatic detection of machine-generated text has focused on binary classification.
“The majority of previous research on automatic detection of machine-generated text has focused on binary classification [Jawahar et al., 2020b;Mitchell et al., 2023].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Jawahar et al., 2020b
The task of detecting LLM-generated text is typically approached as a binary classification problem.
“Typically, this task is approached as a binary classification problem [Zellers et al., 2019;Solaiman et al., 2019;Jawahar et al., 2020b;Fagni et al., 2021;Mitchell et al., 2023].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Jawahar et al., 2020b
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- Authors
- Jawahar, G., Abdul-Mageed, M., & Lakshmanan, V. S.
- Affiliations
- University of British Columbia (CA)
- Venue
- Proceedings of the 28th International Conference on Computational Linguistics
- Published
- 2020
- Pages
- 2296-2309
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W3096831026
- Cited by
- 156
- References
- 12
- Citations by year
- 2026: 24 · 2025: 33 · 2024: 33 · 2023: 43 · 2022: 13 · 2021: 9 · 2020: 1
- FWCI
- 6.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Conference on Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Compute Canada
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- AI-generated text detection, fake news detection
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