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Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communication.
Statistical theory of extreme values and some practical applications: a series of lectures , volume 33
Emil Julius Gumbel · 1948
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Coupling, stationarity, and regeneration
Hermann Thorisson · 2000
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Higher criticism for detecting sparse heterogeneous mixtures1
David Donoho and Jiashun Jin · 2004
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Optimal detection of heterogeneous and heteroscedastic mixtures
T Tony Cai, X Jessie Jeng, and Jiashun Jin · 2011
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Pseudorandomness
Salil P Vadhan et al · 2012
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Optimal detection of sparse mixtures against a given null distribution
Tony T Cai and Yihong Wu · 2014
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Higher criticism for large-scale inference, especially for rare and weak effects
David Donoho and Jiashun Jin · 2015
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The intermediates take it all: Asymptotics of higher criticism statistics and a powerful alternative based on equal local levels
Veronika Gontscharuk, Sandra Landwehr, and Helmut Finner · 2015
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Higher criticism: p-values and criticism
Jian Li and David Siegmund · 2015
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Distribution-free tests for sparse heterogeneous mixtures
Ery Arias-Castro and Meng Wang · 2017
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Www’18 open challenge: financial opinion mining and question answering
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Eli5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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Tracing text provenance via context-aware lexical substitution
Xi Yang, Jie Zhang, Kejiang Chen, Weiming Zhang, Zehua Ma, Feng Wang, and Nenghai Yu · 2022
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Watermarking gpt outputs, 2023
Scott Aaronson and Hendrik Kirchner · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Accelerating large language model decoding with speculative sampling
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper · 2023
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Watermarks in the sand: Impossibility of strong watermarking for generative models
Hanlin Zhang, Benjamin L Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak · 2023
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Provable robust watermarking for ai-generated text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, and Yu-Xiang Wang · 2023
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Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
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Fact sheet: President biden issues executive order on safe, secure, and trustworthy artificial intelligence, Oct 2023
Joe Biden · 2024
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Towards better statistical understanding of watermarking llms
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Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon · 2023
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Unbiased watermark for large language models
Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, and Heng Huang · 2023
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Towards optimal statistical watermarking
Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D Lee, Jiantao Jiao, and Michael I Jordan · 2023
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Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang · 2023
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Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
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Mark my words: Analyzing and evaluating language model watermarks
Julien Piet, Chawin Sitawarin, Vivian Fang, Norman Mu, and David Wagner · 2023
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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 · 2023
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Zhongze Cai, Shang Liu, Hanzhao Wang, Huaiyang Zhong, and Xiaocheng Li · 2024
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Undetectable watermarks for language models
Miranda Christ, Sam Gunn, and Or Zamir · 2024
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Scalable watermarking for identifying large language model outputs
Sumanth Dathathri, Abigail See, Sumedh Ghaisas, Po-Sen Huang, Rob McAdam, Johannes Welbl, Vandana Bachani, Alex Kaskasoli, Robert Stanforth, Tatiana Matejovicova, et al · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Chatgpt - the impact of large language models on law enforcement
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Generation with llms
HuggingFace · 2024
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A statistical framework of watermarks for large language models: Pivot, detection efficiency and optimal rules
Xiang Li, Feng Ruan, Huiyuan Wang, Qi Long, and Weijie J Su · 2024
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The i/o complexity of attention, or how optimal is flash attention?
Barna Saha and Christopher Ye · 2024
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Permute-and-flip: An optimally robust and watermarkable decoder for llms
Xuandong Zhao, Lei Li, and Yu-Xiang Wang · 2024
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