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Recent advances in the capabilities of large language models such as GPT-4 have spurred increasing concern about our ability to detect AI-generated text.
How to construct random functions
Oded Goldreich, Shafi Goldwasser, and Silvio Micali · 1986
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A pseudorandom generator from any one-way function
Johan Håstad, Russell Impagliazzo, Leonid A Levin, and Michael Luby · 1999
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Natural language watermarking: Design, analysis, and a proof-of-concept implementation
Mikhail J Atallah, Victor Raskin, Michael Crogan, Christian Hempelmann, Florian Kerschbaum, Dina Mohamed, and Sanket Naik · 2001
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Natural language watermarking and tamperproofing
Mikhail J Atallah, Victor Raskin, Christian F Hempelmann, Mercan Karahan, Radu Sion, Umut Topkara, and Katrina E Triezenberg · 2003
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From weak to strong watermarking
Nicholas Hopper, David Molnar, and David Wagner · 2007
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Detecting fake content with relative entropy scoring
Thomas Lavergne, Tanguy Urvoy, and François Yvon · 2008
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Upper and lower bounds on black-box steganography
Nenad Dedić, Gene Itkis, Leonid Reyzin, and Scott Russell · 2009
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Provably secure steganography
Nicholas J. Hopper, Luis von Ahn, and John Langford · 2009
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Computer-generated text detection using machine learning: A systematic review
Daria Beresneva · 2016
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Tail bounds for sums of geometric and exponential variables
Svante Janson · 2018
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Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush · 2019
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi · 2019
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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan · 2020
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Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Sahar Abdelnabi and Mario Fritz · 2021
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Meteor: Cryptographically secure steganography for realistic distributions
Gabriel Kaptchuk, Tushar M. Jois, Matthew Green, and Aviel D. Rubin · 2021
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Planting undetectable backdoors in machine learning models
Shafi Goldwasser, Michael P Kim, Vinod Vaikuntanathan, and Or Zamir · 2022
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A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein · 2023
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer · 2023
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Gpt detectors are biased against non-native english writers
Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn · 2023
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Scott Aaronson · 2023
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On the possibilities of ai-generated text detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang · 2023
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We tested a new chatgpt-detector for teachers. it flagged an innocent student
Geoffrey A. Fowler · 2023
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Professors are using chatgpt detector tools to accuse students of cheating. but what if the software is wrong?
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Can ai-generated text be reliably detected?
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