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Motivated by the problem of detecting AI-generated text, we consider the problem of watermarking the output of language models with provable guarantees.
Weakly learning dnf and characterizing statistical query learning using fourier analysis
Avrim Blum, Merrick Furst, Jeffrey Jackson, Michael Kearns, Yishay Mansour, and Steven Rudich · 1994
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Cryptographic primitives based on hard learning problems
Avrim Blum, Merrick Furst, Michael Kearns, and Richard J. Lipton · 1994
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Yoav Freund and Robert E. Schapire · 1995
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A personal view of average-case complexity
R. Impagliazzo · 1995
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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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Gonzalo Navarro · 2001
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Natural language watermarking and tamperproofing
Mikhail J. Atallah, Victor Raskin, Christian F. Hempelmann, Mercan Topkara, Radu Sion, Umut Topkara, and Katrina E. Triezenberg · 2002
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Learning a function of r relevant variables
Avrim Blum · 2003
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Natural language watermarking
Mercan Topkara, Cuneyt M. Taskiran, and Edward J. Delp III · 2005
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Low distortion embeddings for edit distance
Rafail Ostrovsky and Yuval Rabani · 2007
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159 Corpus-Based and Corpus-driven Analyses of Language Variation and Use
Douglas Biber · 2009
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Vitaly Feldman, Parikshit Gopalan, Subhash Khot, and Ashok Kumar Ponnuswami · 2009
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Polylogarithmic approximation for edit distance and the asymmetric query complexity, 2010
Alexandr Andoni, Robert Krauthgamer, and Krzysztof Onak · 2010
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Approximating edit distance in near-linear time, 2011
Alexandr Andoni and Krzysztof Onak · 2011
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On noise-tolerant learning of sparse parities and related problems
Elena Grigorescu, Lev Reyzin, and Santosh Vempala · 2011
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Analysis of Boolean Functions
Ryan ODonnell · 2014
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Finding correlations in subquadratic time, with applications to learning parities and the closest pair problem
Gregory Valiant · 2015
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Concentration inequalities with exchangeable pairs (ph.d. thesis), 2016
Sourav Chatterjee · 2016
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Efficiently decodable insertion/deletion codes for high-noise and high-rate regimes, 2016
Venkatesan Guruswami and Ray Li · 2016
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Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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An improved bound on the fraction of correctable deletions
Boris Bukh, Venkatesan Guruswami, and Johan Håstad · 2017
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Pseudorandom functions: Three decades later
Andrej Bogdanov and Alon Rosen · 2017
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Deletion codes in the high-noise and high-rate regimes
Venkatesan Guruswami and Carol Wang · 2017
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Essential Coding Theory
Venkatesan Guruswami, Atri Rudra, and Madhu Sudan · 2019
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On the convergence of hypergeometric to binomial distributions
Upul Rupassara and Bishnu Sedai · 2023
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Dipmark: A stealthy, efficient and resilient watermark for large language models, 2023
Yihan Wu, Zhengmian Hu, Hongyang Zhang, and Heng Huang · 2023
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Watermarks in the sand: Impossibility of strong watermarking for generative models, 2023
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak · 2023
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Watme: Towards lossless watermarking through lexical redundancy, 2024
Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, and Kam fai Wong · 2024
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Pseudorandom error-correcting codes
Miranda Christ and Sam Gunn · 2024
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Undetectable watermarks for language models
Miranda Christ, Sam Gunn, and Or Zamir · 2024
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Edit distance in near-linear time: it’s a constant factor
Alexandr Andoni and Negev Shekel Nosatzki · 2020
Cited alongside, same era.
Synchronization strings and codes for insertions and deletions – a survey, 2021
Bernhard Haeupler and Amirbehshad Shahrasbi · 2021
Cited alongside, same era.
Synchronization strings: Codes for insertions and deletions approaching the singleton bound
Bernhard Haeupler and Amirbehshad Shahrasbi · 2021
Cited alongside, same era.
Watermarking gpt outputs, 2022
Scott Aaronson and Hendrik Kirchner · 2022
Cited alongside, same era.
Vector-quantized image modeling with improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu · 2022
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On the learnability of watermarks for language models
Chenchen Gu, Xiang Lisa Li, Percy Liang, and Tatsunori Hashimoto · 2024
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Unbiased watermark for large language models
Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, and Heng Huang · 2024
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Towards optimal statistical watermarking, 2024
Baihe Huang, Hanlin Zhu, Banghua Zhu, Kannan Ramchandran, Michael I. Jordan, Jason D. Lee, and Jiantao Jiao · 2024
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On the reliability of watermarks for large language models, 2024
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein · 2024
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Adaptive text watermark for large language models, 2024
Yepeng Liu and Yuheng Bu · 2024
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An unforgeable publicly verifiable watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shuang Li, Lijie Wen, Irwin King, and Philip S. Yu · 2024
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A semantic invariant robust watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng, and Lijie Wen · 2024
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Attacking llm watermarks by exploiting their strengths, 2024
Qi Pang, Shengyuan Hu, Wenting Zheng, and Virginia Smith · 2024
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Visual autoregressive modeling: Scalable image generation via next-scale prediction, 2024
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang · 2024
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Provable robust watermarking for AI-generated text
Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, and Yu-Xiang Wang · 2024
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Excuse me, sir? your language model is leaking (information), 2024
Or Zamir · 2024
Closest in time.