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Watermarking involves implanting an imperceptible signal into generated text that can later be detected via statistical tests.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019a · 1905
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Information Hiding Techniques for Steganography and Digital Watermaking , volume 28
Stephan Katzenbeisser and Fabien Petitcolas. 1999 · 1999
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Information hiding-a survey
Fabien A. P. Petitcolas, Ross J. Anderson, and Markus G. Kuhn. 1999 · 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 · 2001
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Natural language watermarking using semantic substitution for chinese text
Yuei-Lin Chiang, Lu-Ping Chang, Wen-Tai Hsieh, and Wen-Chih Chen. 2003 · 2003
Earlier work this paper cites.
Natural language watermarking using semantic substitution for chinese text
Yuei-Lin Chiang, Lu-Ping Chang, Wen-Tai Hsieh, and Wen-Chih Chen. 2004 · 2003
Earlier work this paper cites.
Natural language watermarking: Challenges in building a practical system
Mercan Topkara, Giuseppe Riccardi, Dilek Hakkani-Tür, and Mikhail J Atallah. 2006 · 2006
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A review of digital watermarking techniques for text documents
Zunera Jalil and Anwar M Mirza. 2009 · 2009
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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 · 2011
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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 Ale s Tamchyna. 2014 · 2014
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Generating steganographic text with LSTMs
Tina Fang, Martin Jaggi, and Katerina Argyraki. 2017 · 2017
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Findings of the 2020 conference on machine translation (WMT20)
Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Matthias Huck, Eric Joanis, Tom Kocmi, Philipp Koehn, Chi-kiu Lo, Nikola Ljubešić, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Santanu Pal, Matt Post, and Marcos Zampieri. 2020 · 2020
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020 · 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 · 2020
Cited alongside, same era.
Frustratingly easy edit-based linguistic steganography with a masked language model
Honai Ueoka, Yugo Murawaki, and Sadao Kurohashi. 2021 · 2021
Cited alongside, same era.
New evaluation metrics capture quality degradation due to llm watermarking
Karanpartap Singh and James Zou. 2023 · 2023
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WatME: Towards lossless watermarking through lexical redundancy
Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, and Kam-Fai Wong. 2024 · 2024
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On the learnability of watermarks for language models
Chenchen Gu, Xiang Lisa Li, Percy Liang, and Tatsunori Hashimoto. 2024 · 2024
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SemStamp: A semantic watermark with paraphrastic robustness for text generation
Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, and Yulia Tsvetkov. 2024 · 2024
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Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang. 2024 · 2024
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Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Watermarking GPT Outputs
Scott Aaronson and Hendrik Kirchner. 2023 · 2023
Cited alongside, same era.
Undetectable watermarks for language models
Miranda Christ, Sam Gunn, and Or Zamir. 2023 · 2023
Cited alongside, same era.
Three bricks to consolidate watermarks for large language models
Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon. 2023 · 2023
Cited alongside, same era.
Social engineering with chatgpt
Dijana Vukovic Grbic and Igor Dujlovic. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
New ai classifier for indicating ai-written text
OpenAI. 2023 · 2023
Cited alongside, same era.
A semantic invariant robust watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng, and Lijie Wen. 2024 · 2024
Closest in time.
An entropy-based text watermarking detection method
Yijian Lu, Aiwei Liu, Dianzhi Yu, Jingjing Li, and Irwin King. 2024 · 2024
Closest in time.
Provably robust multi-bit watermarking for ai-generated text
Wenjie Qu, Wengrui Zheng, Tianyang Tao, Dong Yin, Yanze Jiang, Zhihua Tian, Wei Zou, Jinyuan Jia, and Jiaheng Zhang. 2024 · 2024
Closest in time.
A robust semantics-based watermark for large language model against paraphrasing
Jie Ren, Han Xu, Yiding Liu, Yingqian Cui, Shuaiqiang Wang, Dawei Yin, and Jiliang Tang. 2024 · 2024
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Necessary and sufficient watermark for large language models
Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, and Makoto Yamada. 2024 · 2024
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WaterBench: Towards holistic evaluation of watermarks for large language models
Shangqing Tu, Yuliang Sun, Yushi Bai, Jifan Yu, Lei Hou, and Juanzi Li. 2024 · 2024
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Towards codable watermarking for injecting multi-bits information to LLMs
Lean Wang, Wenkai Yang, Deli Chen, Hao Zhou, Yankai Lin, Fandong Meng, Jie Zhou, and Xu Sun. 2024 · 2024
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Dipmark: A stealthy, efficient and resilient watermark for large language models
Yihan Wu, Zhengmian Hu, Hongyang Zhang, and Heng Huang. 2024 · 2024
Closest in time.
Advancing beyond identification: Multi-bit watermark for large language models
KiYoon Yoo, Wonhyuk Ahn, and Nojun Kwak. 2024 · 2024
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Provable robust watermarking for AI-generated text
Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, and Yu-Xiang Wang. 2024 · 2024
Closest in time.