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In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1).
Language Models are Few-Shot Learners
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 · 2005
Earlier work this paper cites.
The SAGE encyclopedia of communication research methods
Mike Allen. 2017 · 2017
Earlier work this paper cites.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C. Lipton. 2018 · 2018
Earlier work this paper cites.
GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019 · 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, and Jasmine Wang. 2019 · 2019
Earlier work this paper cites.
RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, and Chris Callison-Burch. 2020 · 2020
Earlier work this paper cites.
Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
Earlier work this paper cites.
Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
Earlier work this paper cites.
BLiMP: The benchmark of linguistic minimal pairs for English
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. 2020 · 2020
Earlier work this paper cites.
All that‘s ‘human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. 2021 · 2021
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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
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The perils of using Mechanical Turk to evaluate open-ended text generation
Marzena Karpinska, Nader Akoury, and Mohit Iyyer. 2021 · 2021
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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. 2022 · 2022
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DEMETR: Diagnosing evaluation metrics for translation
Marzena Karpinska, Nishant Raj, Katherine Thai, Yixiao Song, Ankita Gupta, and Mohit Iyyer. 2022 · 2022
Earlier work this paper cites.
Fast-detectgpt: Efficient zero-shot detection of machine-generated text via conditional probability curvature
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang. 2023 · 2023
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RADAR: robust ai-text detection via adversarial learning
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho. 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
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Ai vs. human – differentiation analysis of scientific content generation
Yongqiang Ma, Jiawei Liu, Fan Yi, Qikai Cheng, Yong Huang, Wei Lu, and Xiaozhong Liu. 2023 · 2023
Cited alongside, same era.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
Red teaming language model detectors with language models
Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh. 2023 · 2023
Cited alongside, same era.
MAGE: Machine-generated Text Detection in the Wild
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, and Yue Zhang. 2024 · 2024
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Large language models can be guided to evade ai-generated text detection
Ning Lu, Shengcai Liu, Ruidan He, and Ke Tang. 2023 · 2024
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OpenAI. 2024 · 2024
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Ai-generated poetry is indistinguishable from human-written poetry and is rated more favorably
B Porter and Edouard Machery. 2024 · 2024
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AI "News" Content Farms Are Easy to Make and Hard to Detect: A Case Study in Italian
Giovanni Puccetti, Anna Rogers, Chiara Alzetta, Felice Dell’Orletta, and Andrea Esuli. 2024 · 2024
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Gptzero: Towards detection of ai-generated text using zero-shot and supervised methods"
Edward Tian and Alexander Cui. 2023 · 2023
Cited alongside, same era.
Ghostbuster: Detecting text ghostwritten by large language models
Vivek Kumar Verma, Eve Fleisig, Nicholas Tomlin, and Dan Klein. 2023 · 2023
Cited alongside, same era.
Claude 3 model card addendum
Anthropic. 2024 · 2024
Cited alongside, same era.
Art or artifice? large language models and the false promise of creativity
Tuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan, and Chien-Sheng Wu. 2024 · 2024
Cited alongside, same era.
PostMark: A robust blackbox watermark for large language models
Yapei Chang, Kalpesh Krishna, Amir Houmansadr, John Frederick Wieting, and Mohit Iyyer. 2024 · 2024
Cited alongside, same era.
RAID: A shared benchmark for robust evaluation of machine-generated text detectors
Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito, and Chris Callison-Burch. 2024 · 2024
Cited alongside, same era.
Technical Report on the Pangram AI-Generated Text Classifier
Bradley Emi and Max Spero. 2024 · 2024
Cited alongside, same era.
Can AI-Generated Text be Reliably Detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2024 · 2024
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Detection and measurement of syntactic templates in generated text
Chantal Shaib, Yanai Elazar, Junyi Jessy Li, and Byron C. Wallace. 2024 · 2024
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Llm-as-a-coauthor: Can mixed human-written and machine-generated text be detected?
Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, and Lichao Sun. 2024 · 2024
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Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, Chao Cao, Hanqi Jiang, Hanxu Chen, Yiwei Li, Junhao Chen, Huawen Hu, Yihen Liu, Huaqin Zhao, Shaochen Xu, Haixing Dai, Lin Zhao, Ruidong Zhang, Wei Zhao, Zhenyuan Yang, Jingyuan Chen, Peilong Wang, Wei Ruan, Hui Wang, Huan Zhao, Jing Zhang, Yiming Ren, Shihuan Qin, Tong Chen, Jiaxi Li, Arif Hassan Zidan, Afrar Jahin, Minheng Chen, Sichen Xia, Jason Holmes, Yan Zhuang, Jiaqi Wang, Bochen Xu, Weiran Xia, Jichao Yu, Kaibo Tang, Yaxuan Yang, Bolun Sun, Tao Yang, Guoyu Lu, Xianqiao Wang, Lilong Chai, He Li, Jin Lu, Lichao Sun, Xin Zhang, Bao Ge, Xintao Hu, Lian Zhang, Hua Zhou, Lu Zhang, Shu Zhang, Ninghao Liu, Bei Jiang, Linglong Kong, Zhen Xiang, Yudan Ren, Jun Liu, Xi Jiang, Yu Bao, Wei Zhang, Xiang Li, Gang Li, Wei Liu, Dinggang Shen, Andrea Sikora, Xiaoming Zhai, Dajiang Zhu, and Tianming Liu. 2024 · 2024
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Humanizing machine-generated content: Evading ai-text detection through adversarial attack
Ying Zhou, Ben He, and Le Sun. 2024 · 2024
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Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students
Tiffany Zhu, Kexun Zhang, and William Yang Wang. 2024 · 2024
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Claude: A language model by anthropic
Anthropic. 2023 · 2025
Closest in time.
Exploring the Limitations of Detecting Machine-Generated Text
Jad Doughman, Osama Mohammed Afzal, Hawau Olamide Toyin, Shady Shehata, Preslav Nakov, and Zeerak Talat. 2025 · 2025
Closest in time.
DAMAGE: Detecting Adversarially Modified AI Generated Text
Elyas Masrour, Bradley Emi, and Max Spero. 2025 · 2025
Closest in time.
Is human-like text liked by humans? multilingual human detection and preference against ai
Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saad El Dine Ahmed, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, and Preslav Nakov. 2025 · 2025
Closest in time.