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Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs.
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 · 1906
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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 · 1907
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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 · 1908
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The probable error of a mean
Student. 1908 · 1908
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1911
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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 · 1911
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A new readability yardstick
Rudolph Flesch. 1948 · 1948
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Reality monitoring
Marcia K Johnson and Carol L Raye. 1981 · 1981
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Random decision forests
Tin Kam Ho. 1995 · 1995
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Naive (bayes) at forty: The independence assumption in information retrieval
David D. Lewis. 1998 · 1998
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Using tf-idf to determine word relevance in document queries
Juan Ramos et al. 2003 · 2003
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Linguistic inquiry and word count (LIWC2007)
James W. Pennebaker, Roger John Booth, and Martha E. Francis. 2007 · 2007
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Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language
Cindy K Chung and James W Pennebaker. 2008 · 2008
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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan. 2020 · 2011
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Finding deceptive opinion spam by any stretch of the imagination
Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T Hancock. 2011 · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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When small words foretell academic success: The case of college admissions essays
James W Pennebaker, Cindy K Chung, Joey Frazee, Gary M Lavergne, and David I Beaver. 2014 · 2014
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The development and psychometric properties of LIWC2015
James W. Pennebaker, Ryan L. Boyd, Kayla Jordan, and Kate G. Blackburn. 2015 · 2015
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Linguistic obfuscation in fraudulent science
David M Markowitz and Jeffrey T Hancock. 2016 · 2016
Cited alongside, same era.
Scattertext: a browser-based tool for visualizing how corpora differ
Jason Kessler. 2017 · 2017
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Examining long-term trends in politics and culture through language of political leaders and cultural institutions
Kayla N Jordan, Joanna Sterling, James W Pennebaker, and Ryan L Boyd. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Wiki-40b: Multilingual language model dataset
Mandy Guo, Zihang Dai, Denny Vrandecic, and Rami Al-Rfou. 2020 · 2020
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Putting your best pet forward: Language patterns of persuasion in online pet advertisements
Programming is hard-or at least it used to be: Educational opportunities and challenges of ai code generation
Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. 2023 · 2023
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”i slept like a baby”: Using human traits to characterize deceptive chatgpt and human text
Salvatore Giorgi, David M. Markowitz, Nikita Soni, Vasudha Varadarajan, Siddharth Mangalik, and H. A. Schwartz. 2023 · 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 · 2023
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Textdescriptives: A python package for calculating a large variety of metrics from text
Lasse Hansen, Ludvig Renbo Olsen, and Kenneth Enevoldsen. 2023 · 2023
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Radar: Robust ai-text detection via adversarial learning
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David M Markowitz. 2020 · 2020
Cited alongside, same era.
Stereotyping in the digital age: Male language is “ingenious”, female language is “beautiful”–and popular
Tabea Meier, Ryan L Boyd, Matthias R Mehl, Anne Milek, James W Pennebaker, Mike Martin, Markus Wolf, and Andrea B Horn. 2020 · 2020
Cited alongside, same era.
CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020 · 2020
Cited alongside, same era.
Is gpt-3 text indistinguishable from human text? scarecrow: A framework for scrutinizing machine text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, and Yejin Choi. 2021 · 2021
Cited alongside, same era.
Tweepfake: About detecting deepfake tweets
Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, Antonio Martella, and Maurizio Tesconi. 2021 · 2021
Cited alongside, same era.
I am a scientist… ask me anything: Examining differences between male and female scientists participating in a reddit ama session
Austin Y Hubner and Robert Bond. 2022 · 2022
Cited alongside, same era.
Threat scenarios and best practices to detect neural fake news
Artidoro Pagnoni, Martin Graciarena, and Yulia Tsvetkov. 2022 · 2022
Cited alongside, same era.
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho. 2023 · 2023
Later among the works it cites.
Human heuristics for ai-generated language are flawed
Maurice Jakesch, Jeffrey T Hancock, and Mor Naaman. 2023 · 2023
Later among the works it cites.
Instrumental goal activation increases online petition support across languages
David M Markowitz. 2023 · 2023
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The threat of offensive ai to organizations
Yisroel Mirsky, Ambra Demontis, Jaidip Kotak, Ram Shankar, Deng Gelei, Liu Yang, Xiangyu Zhang, Maura Pintor, Wenke Lee, Yuval Elovici, et al. 2023 · 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 · 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 · 2023
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The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023 · 2023
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M4: Multi-generator, multi-domain, and multi-lingual black-box machine-generated text detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Alham Fikri Aji, et al. 2023 · 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 Nenghai Yu. 2023 · 2023
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Fighting fire with fire: can chatgpt detect ai-generated text?
Amrita Bhattacharjee and Huan Liu. 2024 · 2024
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Outfox: Llm-generated essay detection through in-context learning with adversarially generated examples
Ryuto Koike, Masahiro Kaneko, and Naoaki Okazaki. 2024 · 2024
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Generative ai are more truth-biased than humans: A replication and extension of core truth-default theory principles
David M Markowitz and Jeffrey T Hancock. 2024 · 2024
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Linguistic markers of inherently false ai communication and intentionally false human communication: Evidence from hotel reviews
David M Markowitz, Jeffrey T Hancock, and Jeremy N Bailenson. 2024 · 2024
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Intrinsic dimension estimation for robust detection of ai-generated texts
Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Sergey Nikolenko, Evgeny Burnaev, Serguei Barannikov, and Irina Piontkovskaya. 2024 · 2024
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