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Natural Language Watermarking: Design, Analysis, and a Proof-of-Concept Implementation
Mikhail J. Atallah, Victor Raskin, Michael Crogan, Christian F. Hempelmann, Florian Kerschbaum, Dina Mohamed, and Sanket Naik · 2001
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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
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Identifying Real or Fake Articles: Towards better Language Modeling
Sameer Badaskar, Sachin Agarwal, and Shilpa Arora · 2008
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Detecting Fake Content with Relative Entropy Scoring
Thomas Lavergne, Tanguy Urvoy, and François Yvon · 2008
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Squibs: What Is a Paraphrase?
Rahul Bhagat and Eduard H. Hovy · 2013
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Tradition and innovation in scientists’ research strategies
Jacob G Foster, Andrey Rzhetsky, and James A Evans · 2015
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Computer-Generated Text Detection Using Machine Learning: A Systematic Review
Daria Beresneva · 2016
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Real or Fake? Learning to Discriminate Machine from Human Generated Text
Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, and Arthur Szlam · 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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Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck · 2019
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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GPT-2: 1.5B release
OpenAI · 2019
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Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al · 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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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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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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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
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Authorship Attribution for Neural Text Generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee · 2020
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Attacking Neural Text Detectors
Max Wolff · 2020
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TweepFake: About detecting deepfake tweets
Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, Antonio Martella, and Maurizio Tesconi · 2021
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Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Evan Crothers, Nathalie Japkowicz, and Herna Viktor · 2022
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Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers
Catherine A Gao, Frederick M Howard, Nikolay S Markov, Emma C Dyer, Siddhi Ramesh, Yuan Luo, and Alexander T Pearson · 2022
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How to spot AI-generated text
Melissa Heikkilä · 2022
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Recalibrating the scope of scholarly publishing: A modest step in a vast decolonization process
Saurabh Khanna, Jon Ball, Juan Pablo Alperin, and John Willinsky · 2022
Cited alongside, same era.
Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Yu Lan, and Chao Shen · 2022
Cited alongside, same era.
Simons Institute Talk on Watermarking of Large Language Models, 2023
Scott Aaronson · 2023
Cited alongside, same era.
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang · 2023
Cited alongside, same era.
Google search exposes academics using ChatGPT in research papers
Paulina Okunytė · 2023
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Can AI-Generated Text be Reliably Detected?
Vinu Sankar Sadasivan, Aounon Kumar, S. Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
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Red Teaming Language Model Detectors with Language Models
Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh · 2023
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The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2023
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Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah, and Huan Liu · 2023
Cited alongside, same era.
On the possibilities of ai-generated text detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang · 2023
Cited alongside, same era.
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
Yutian Chen, Hao Kang, Vivian Zhai, Liang Li, Rita Singh, and Bhiksha Ramakrishnan · 2023
Cited alongside, same era.
Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality, 2023
Fabrizio Dell’Acqua, Edward McFowland, Ethan R Mollick, Hila Lifshitz-Assaf, Katherine Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R Lakhani · 2023
Cited alongside, same era.
What’s In My Big Data?
Yanai Elazar, Akshita Bhagia, Ian Helgi Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Evan Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, et al · 2023
Cited alongside, same era.
Abstracts written by ChatGPT fool scientists
Holly Else · 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
Cited alongside, same era.
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Chatgpt is fun, but not an author
H. Holden Thorp · 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, et al · 2023
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Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, S. Barannikov, Irina Piontkovskaya, Sergey I. Nikolenko, and Evgeny Burnaev · 2023
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Veniamin Veselovsky, Manoel Horta Ribeiro, and Robert West · 2023
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‘As an AI language model’: the phrase that shows how AI is pollulating the web
James Vincent · 2023
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Testing of detection tools for AI-generated text
Debora Weber-Wulff, Alla Anohina-Naumeca, Sonja Bjelobaba, Tomáš Foltýnek, Jean Guerrero-Dib, Olumide Popoola, Petr Šigut, and Lorna Waddington · 2023
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DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models
Yihan Wu, Zhengmian Hu, Hongyang Zhang, and Heng Huang · 2023
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Robust Multi-bit Natural Language Watermarking through Invariant Features
Kiyoon Yoo, Wonhyuk Ahn, Jiho Jang, and No Jun Kwak · 2023
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GPT Paternity Test: GPT Generated Text Detection with GPT Genetic Inheritance
Xiao Yu, Yuang Qi, Kejiang Chen, Guoqiang Chen, Xi Yang, Pengyuan Zhu, Weiming Zhang, and Neng H. Yu · 2023
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Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors
Yi-Fan Zhang, Zhang Zhang, Liang Wang, Tien-Ping Tan, and Rong Jin · 2023
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Protecting Language Generation Models via Invisible Watermarking
Xuandong Zhao, Yu-Xiang Wang, and Lei Li · 2023
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AI-generated nonsense is leaking into scientific journals
Mack Deguerin · 2024
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Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, and James Y. Zou · 2024
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The Global AI Talent Tracker, 2024
MacroPolo · 2024
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Papers and peer reviews with evidence of ChatGPT writing
Ivan Oransky and Adam Marcus · 2024
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Generative AI Top 150: The World’s Most Used AI Tools
Dann. Van Rossum · 2024
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