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We propose Easymark, a family of embarrassingly simple yet effective watermarks.
Watermarking the outputs of structured prediction with an application in statistical machine translation
Ashish Venugopal, Jakob Uszkoreit, David Talbot, Franz Josef Och, and Juri Ganitkevitch · 2011
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Unispach: A text-based data hiding method using unicode space characters
Lip Yee Por, KokSheik Wong, and Kok Onn Chee · 2012
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Findings of the 2014 workshop on statistical machine translation
Ondrej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, and Ales Tamchyna · 2014
Earlier work this paper cites.
Findings of the 2016 conference on machine translation
Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno-Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana L. Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri · 2016
Earlier work this paper cites.
Content-preserving text watermarking through unicode homoglyph substitution
Stefano Giovanni Rizzo, Flavio Bertini, and Danilo Montesi · 2016
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Generating steganographic text with LSTMs
Tina Fang, Martin Jaggi, and Katerina J. Argyraki · 2017
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Text watermarking in social media
Stefano Giovanni Rizzo, Flavio Bertini, Danilo Montesi, and Carlo Stomeo · 2017
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Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson · 2018
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A call for clarity in reporting BLEU scores
Matt Post · 2018
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Towards near-imperceptible steganographic text
Falcon Z. Dai and Zheng Cai · 2019
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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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Neural linguistic steganography
Zachary M. Ziegler, Yuntian Deng, and Alexander M. Rush · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
The limitations of stylometry for detecting machine-generated fake news
Tal Schuster, Roei Schuster, Darsh J. Shah, and Regina Barzilay · 2020
Cited alongside, same era.
Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee · 2020
Cited alongside, same era.
Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Sahar Abdelnabi and Mario Fritz · 2021
Undetectable watermarks for language models
Miranda Christ, Sam Gunn, and Or Zamir · 2023
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How close is ChatGPT to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu · 2023
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Regulating ChatGPT and other large generative AI models
Philipp Hacker, Andreas Engel, and Marco Mauer · 2023
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Human heuristics for AI-generated language are flawed
Maurice Jakesch, Jeffrey T. Hancock, and Mor Naaman · 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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Cited alongside, same era.
All that’s ’human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith · 2021
Cited alongside, same era.
Frustratingly easy edit-based linguistic steganography with a masked language model
Honai Ueoka, Yugo Murawaki, and Sadao Kurohashi · 2021
Cited alongside, same era.
No language left behind: Scaling human-centered machine translation
Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loïc Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang · 2022
Cited alongside, same era.
On pushing deepfake tweet detection capabilities to the limits
Margherita Gambini, Tiziano Fagni, Fabrizio Falchi, and Maurizio Tesconi · 2022
Cited alongside, same era.
The ethical need for watermarks in machine-generated language
Alexei Grinbaum and Laurynas Adomaitis · 2022
Cited alongside, same era.
Watermarking pre-trained language models with backdooring
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Private recommender systems: How can users build their own fair recommender systems without log data?
Ryoma Sato · 2022
Cited alongside, same era.
DetectGPT: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Are you copying my model? protecting the copyright of large language models for eaas via backdoor watermark
Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, and Xing Xie · 2023
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Can AI-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
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Necessary and sufficient watermark for large language models
Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, and Makoto Yamada · 2023
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LLaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.