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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Automatic Detection of Generated Text is Easiest when Humans are Fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.
(2020)
Open Access | Times Cited: 173

Showing 1-25 of 173 citing articles:

On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1521
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
Elizabeth Clark, Tal August, Sofia Serrano, et al.
(2021)
Open Access | Times Cited: 190
Human heuristics for AI-generated language are flawed
Maurice Jakesch, Jeffrey T. Hancock, Mor Naaman
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 11
Open Access | Times Cited: 122
TweepFake: About detecting deepfake tweets
Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, et al.
PLoS ONE (2021) Vol. 16, Iss. 5, pp. e0251415-e0251415
Open Access | Times Cited: 115
A survey of GPT-3 family large language models including ChatGPT and GPT-4
Katikapalli Subramanyam Kalyan
Natural Language Processing Journal (2023) Vol. 6, pp. 100048-100048
Open Access | Times Cited: 106
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Evan Crothers, Nathalie Japkowicz, Herna L. Viktor
IEEE Access (2023) Vol. 11, pp. 70977-71002
Open Access | Times Cited: 75
Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text
Sebastian Gehrmann, Elizabeth A. Clark, Thibault Sellam
Journal of Artificial Intelligence Research (2023) Vol. 77, pp. 103-166
Open Access | Times Cited: 69
Chatgpt for cybersecurity: practical applications, challenges, and future directions
Muna Al-Hawawreh, Ahamed Aljuhani, Yaser Jararweh
Cluster Computing (2023) Vol. 26, Iss. 6, pp. 3421-3436
Closed Access | Times Cited: 67
Can Generative AI improve social science?
Christopher A. Bail
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 21
Open Access | Times Cited: 43
Automatic Detection of Machine Generated Text: A Critical Survey
Ganesh Jawahar, Muhammad Abdul-Mageed, V.S. Laks Lakshmanan
Proceedings of the 17th international conference on Computational linguistics - (2020)
Open Access | Times Cited: 99
Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover
Leon Fröhling, Arkaitz Zubiaga
PeerJ Computer Science (2021) Vol. 7, pp. e443-e443
Open Access | Times Cited: 67
Rethinking search
Donald Metzler, Yi Tay, Dara Bahri, et al.
ACM SIGIR Forum (2021) Vol. 55, Iss. 1, pp. 1-27
Open Access | Times Cited: 61
Embracing ChatGPT: Implications of Emergent Language Models for Academia and Libraries
Aileen B. Houston, Edward M. Corrado
Technical Services Quarterly (2023) Vol. 40, Iss. 2, pp. 76-91
Closed Access | Times Cited: 37
Can Generative AI Improve Social Science?
Christopher A. Bail
(2023)
Open Access | Times Cited: 37
Language Model Behavior: A Comprehensive Survey
Tyler A. Chang, Benjamin Bergen
Computational Linguistics (2023) Vol. 50, Iss. 1, pp. 293-350
Open Access | Times Cited: 37
Deepfake Text Detection: Limitations and Opportunities
Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah, et al.
2022 IEEE Symposium on Security and Privacy (SP) (2023), pp. 1613-1630
Open Access | Times Cited: 27
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.
(2023)
Open Access | Times Cited: 23
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Rami Mubarak, Tariq Alsboui, Omar Alshaikh, et al.
IEEE Access (2023) Vol. 11, pp. 144497-144529
Open Access | Times Cited: 23
The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews?
Balázs Kovács
Marketing Letters (2024) Vol. 35, Iss. 4, pp. 651-666
Closed Access | Times Cited: 12
Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites
Hans W. A. Hanley, Zakir Durumeric
Proceedings of the International AAAI Conference on Web and Social Media (2024) Vol. 18, pp. 542-556
Open Access | Times Cited: 10
Detecting computer-generated disinformation
Harald Stiff, Fredrik Johansson
International Journal of Data Science and Analytics (2021) Vol. 13, Iss. 4, pp. 363-383
Open Access | Times Cited: 49
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
Sahar Abdelnabi, Mario Fritz
2022 IEEE Symposium on Security and Privacy (SP) (2021), pp. 121-140
Open Access | Times Cited: 47
The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation
Marzena Karpinska, Nader Akoury, Mohit Iyyer
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Open Access | Times Cited: 43
Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 11, pp. 12763-12771
Open Access | Times Cited: 18
Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment
Mike Richards, Kevin Waugh, Mark Slaymaker, et al.
ACM Transactions on Computing Education (2023) Vol. 24, Iss. 1, pp. 1-32
Open Access | Times Cited: 17
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