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Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses.
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
Earlier work this paper cites.
Natural language watermarking and tamperproofing
Mikhail J Atallah, Victor Raskin, Christian F Hempelmann, Mercan Karahan, Radu Sion, Umut Topkara, and Katrina E Triezenberg · 2002
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Captcha: Using hard ai problems for security
Luis Von Ahn, Manuel Blum, Nicholas J Hopper, and John Langford · 2003
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The hiding virtues of ambiguity: quantifiably resilient watermarking of natural language text through synonym substitutions
Umut Topkara, Mercan Topkara, and Mikhail J Atallah · 2006
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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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Decoding with large-scale neural language models improves translation
Ashish Vaswani, Yinggong Zhao, Victoria Fossum, and David Chiang · 2013
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Computer-generated text detection using machine learning: A systematic review
Daria Beresneva · 2016
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The enemy in your own camp: How well can we detect statistically-generated fake reviews–an adversarial study
Dirk Hovy · 2016
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Information hiding
Stefan Katzenbeisser and Fabien Petitcolas · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Beam search strategies for neural machine translation
Markus Freitag and Yaser Al-Onaizan · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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Lmu munich’s neural machine translation systems at wmt 2018
Matthias Huck, Dario Stojanovski, Viktor Hangya, and Alexander Fraser · 2018
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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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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Towards near-imperceptible steganographic text
Falcon 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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Openwebtext corpus, 2019
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
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Characterizing and detecting livestreaming chatbots
Shreya Jain, Dipankar Niranjan, Hemank Lamba, Neil Shah, and Ponnurangam Kumaraguru · 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
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi · 2019
Cited alongside, same era.
Neural linguistic steganography
Zachary Ziegler, Yuntian Deng, and Alexander M Rush · 2019
Cited alongside, same era.
Deepfakes and synthetic media in the financial system: Assessing threat scenarios
Jon Bateman · 2020
Cited alongside, same era.
Language models are few-shot learners, 2020
Chatting and cheating: Ensuring academic integrity in the era of chatgpt
Debby RE Cotton, Peter A Cotton, and J Reuben Shipway · 2023
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Academic integrity and artificial intelligence: is chatgpt hype, hero or heresy?
Geoffrey M Currie · 2023
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A preliminary investigation of fake peer-reviewed citations and references generated by chatgpt
Terence Day · 2023
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Chatgpt and the rise of large language models: the new ai-driven infodemic threat in public health
Luigi De Angelis, Francesco Baglivo, Guglielmo Arzilli, Gaetano Pierpaolo Privitera, Paolo Ferragina, Alberto Eugenio Tozzi, and Caterina Rizzo · 2023
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Three bricks to consolidate watermarks for large language models
Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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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.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Cited alongside, same era.
Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan · 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.
Attacking neural text detectors
Max Wolff and Stuart Wolff · 2020
Cited alongside, same era.
Answering complex open-domain questions with multi-hop dense retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, et al · 2020
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization, 2020
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu · 2020
Cited alongside, same era.
There are adversarial attacks for that proposal as well — in particular, generating with emojis after words and then removing them before submitting defeats it, 2023
Riley Goodside · 2023
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Learning to fake it: limited responses and fabricated references provided by chatgpt for medical questions
Jocelyn Gravel, Madeleine D’Amours-Gravel, and Esli Osmanlliu · 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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Radar: Robust ai-text detection via adversarial learning
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho · 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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GPT detectors are biased against non-native english writers
Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou · 2023
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A private watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shu’ang Li, Lijie Wen, Irwin King, and Philip S Yu · 2023
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Smaller language models are better black-box machine-generated text detectors
Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, and Taylor Berg-Kirkpatrick · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn · 2023
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New ai classifier for indicating ai-written text
OpenAI · 2023
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To chatgpt, or not to chatgpt: That is the question!
Alessandro Pegoraro, Kavita Kumari, Hossein Fereidooni, and Ahmad-Reza Sadeghi · 2023
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Academic integrity considerations of ai large language models in the post-pandemic era: Chatgpt and beyond
Mike Perkins · 2023
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Natural language watermarking via paraphraser-based lexical substitution
Jipeng Qiang, Shiyu Zhu, Yun Li, Yi Zhu, Yunhao Yuan, and Xindong Wu · 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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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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Chatgpt in higher education: Considerations for academic integrity and student learning
Miriam Sullivan, Andrew Kelly, and Paul McLaughlan · 2023
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Ghostbuster: Detecting text ghostwritten by large language models
Vivek Verma, Eve Fleisig, Nicholas Tomlin, and Dan Klein · 2023
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Bot or human? detecting chatgpt imposters with a single question
Hong Wang, Xuan Luo, Weizhi Wang, and Xifeng Yan · 2023
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Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text
Xianjun Yang, Wei Cheng, Linda Petzold, William Yang Wang, and Haifeng Chen · 2023
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Copyright protection and accountability of generative ai: Attack, watermarking and attribution
Haonan Zhong, Jiamin Chang, Ziyue Yang, Tingmin Wu, Pathum Chamikara Mahawaga Arachchige, Chehara Pathmabandu, and Minhui Xue · 2023
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