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With the rapid progress of large language models (LLMs) and the huge amount of text they generated, it becomes more and more impractical to manually distinguish whether a text is machine-generated.
Language models are few-shot learners
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Real or fake? learning to discriminate machine from human generated text
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Roberta: A robustly optimized bert pretraining approach
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Release strategies and the social impacts of language models
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Automatic detection of generated text is easiest when humans are fooled
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Identifying real or fake articles: Towards better language modeling
Sameer Badaskar, Sachin Agarwal, and Shilpa Arora. 2008 · 2008
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Detecting fake content with relative entropy scoring
Thomas Lavergne, Tanguy Urvoy, and François Yvon. 2008 · 2008
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Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan. 2020 · 2011
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Computer-generated text detection using machine learning: A systematic review
Daria Beresneva. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
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Snorkel: Rapid training data creation with weak supervision
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
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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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Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
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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 · 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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Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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Ai bot chatgpt writes smart essays-should academics worry?
Chris Stokel-Walker. 2022 · 2022
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Wordcraft: story writing with large language models
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
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How close is chatgpt to human experts? comparison corpus, evaluation, and detection
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Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Sahar Abdelnabi and Mario Fritz. 2021 · 2021
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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Tweepfake: About detecting deepfake tweets
Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, Antonio Martella, and Maurizio Tesconi. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
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The ethical need for watermarks in machine-generated language
Alexei Grinbaum and Laurynas Adomaitis. 2022 · 2022
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gpt: Optimizing language models for dialogue. http://web.archive.org/web/ 20230109000707/https://openai.com/ blog/chatgpt/
OpenAI. 2022 · 2022
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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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A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. 2023 · 2023
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
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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?
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Llama: Open and efficient foundation language models
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Protecting language generation models via invisible watermarking
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