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While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem.
Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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 · 1901
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Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2020 · 1905
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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, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, and Jasmine Wang. 2019 · 1908
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DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2003
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A New Approach to the Study of Translationese: Machine-learning the Difference between Original and Translated Text
Marco Baroni and Silvia Bernardini. 2006 · 2006
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A Review of Spoken and Written English Corpus of Chinese Learners
Xu Zhi-jia. 2008 · 2008
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TOEFL11: A corpus of non-native english
Daniel Blanchard, Joel Tetreault, Derrick Higgins, Aoife Cahill, and Martin Chodorow. 2013 · 2013
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XGBoost: A Scalable Tree Boosting System
Tianqi Chen and Carlos Guestrin. 2016 · 2016
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MIMIC-III, a freely accessible critical care database
Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2018 · 2018
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WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 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 · 2019
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ELI5: Long Form Question Answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019 · 2019
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A Call for More Rigor in Unsupervised Cross-lingual Learning
Mikel Artetxe, Sebastian Ruder, Dani Yogatama, Gorka Labaka, and Eneko Agirre. 2020 · 2020
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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. 2020 · 2020
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RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, and Chris Callison-Burch. 2020 · 2020
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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. 2020 · 2020
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Automatic Detection of Machine Generated Text: A Critical Survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks Lakshmanan, V.S. 2020 · 2020
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AI did not write this manuscript, or did it? Can we trick the AI text detector into generated texts? The potential future of ChatGPT and AI in Sports & Exercise Medicine manuscript generation
Nash Anderson, Daniel L Belavy, Stephen M Perle, Sharief Hendricks, Luiz Hespanhol, Evert Verhagen, and Aamir R Memon. 2023 · 2023
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Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Debby R. E. Cotton, Peter A. Cotton, and J. Reuben Shipway. 2023 · 2023
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Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Evan Crothers, Nathalie Japkowicz, and Herna Viktor. 2023 · 2023
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Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Sherry Shi, and Chris Callison-Burch. 2023 · 2023
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How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
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Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, and Benoît Sagot. 2020 · 2020
Cited alongside, same era.
Reverse Engineering Configurations of Neural Text Generation Models
Yi Tay, Dara Bahri, Che Zheng, Clifford Brunk, Donald Metzler, and Andrew Tomkins. 2020 · 2020
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Authorship Attribution for Neural Text Generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020 · 2020
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MFAQ: a multilingual FAQ dataset
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, and Walter Daelemans. 2021 · 2021
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Societal Biases in Language Generation: Progress and Challenges
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2021 · 2021
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 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 · 2022
Cited alongside, same era.
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
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MGTBench: Benchmarking Machine-Generated Text Detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 2023 · 2023
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Hazem Ibrahim, Fengyuan Liu, Rohail Asim, Balaraju Battu, Sidahmed Benabderrahmane, Bashar Alhafni, Wifag Adnan, Tuka Alhanai, Bedoor AlShebli, Riyadh Baghdadi, Jocelyn J. Bélanger, Elena Beretta, Kemal Celik, Moumena Chaqfeh, Mohammed F. Daqaq, Zaynab El Bernoussi, Daryl Fougnie, Borja Garcia de Soto, Alberto Gandolfi, Andras Gyorgy, Nizar Habash, J. Andrew Harris, Aaron Kaufman, Lefteris Kirousis, Korhan Kocak, Kangsan Lee, Seungah S. Lee, Samreen Malik, Michail Maniatakos, David Melcher, Azzam Mourad, Minsu Park, Mahmoud Rasras, Alicja Reuben, Dania Zantout, Nancy W. Gleason, Kinga Makovi, Talal Rahwan, and Yasir Zaki. 2023 · 2023
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Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov. 2023 · 2023
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Differentiate ChatGPT-generated and Human-written Medical Texts
Wenxiong Liao, Zhengliang Liu, Haixing Dai, Shaochen Xu, Zihao Wu, Yiyang Zhang, Xiaoke Huang, Dajiang Zhu, Hongmin Cai, Tianming Liu, and Xiang Li. 2023 · 2023
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ArguGPT: Evaluating, understanding and identifying argumentative essays generated by GPT models
Yikang Liu, Ziyin Zhang, Wanyang Zhang, Shisen Yue, Xiaojing Zhao, Xinyuan Cheng, Yiwen Zhang, and Hai Hu. 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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Sandra Mitrović, Davide Andreoletti, and Omran Ayoub. 2023 · 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 · 2023
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Christoforos Vasilatos, Manaar Alam, Talal Rahwan, Yasir Zaki, and Michail Maniatakos. 2023 · 2023
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Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detection
Luoxuan Weng, Minfeng Zhu, Kam Kwai Wong, Shi Liu, Jiashun Sun, Hang Zhu, Dongming Han, and Wei Chen. 2023 · 2023
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CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts
Peipeng Yu, Jiahan Chen, Xuan Feng, and Zhihua Xia. 2023 · 2023
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Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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