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Large language models (LLMs) have opened up enormous opportunities while simultaneously posing ethical dilemmas.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
Elizabeth R DeLong, David M DeLong, and Daniel L Clarke-Pearson · 1988
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The box-cox transformation technique: a review
Remi M Sakia · 1992
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The infinite gaussian mixture model
Carl Rasmussen · 1999
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Machine learning in automated text categorization
Fabrizio Sebastiani · 2002
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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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Gaussian mixture models
Douglas A Reynolds et al · 2009
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Intelligent selection of language model training data
Robert C Moore and William Lewis · 2010
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Computer-generated text detection using machine learning: A systematic review
Daria Beresneva · 2016
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi · 2019
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Gltr: Statistical detection and visualization of generated text, 2019
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M. Rush · 2019
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu · 2019
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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
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A comparative analysis of logistic regression, random forest and knn models for the text classification
Kanish Shah, Henil Patel, Devanshi Sanghvi, and Manan Shah · 2020
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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Cited alongside, same era.
Machine generated text: A comprehensive survey of threat models and detection methods
Evan Crothers, Nathalie Japkowicz, and Herna Viktor · 2022
Cited alongside, same era.
“so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy
Yogesh K Dwivedi, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M Baabdullah, Alex Koohang, Vishnupriya Raghavan, Manju Ahuja, et al · 2023
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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Efficient detection of llm-generated texts with a bayesian surrogate model
Zhijie Deng, Hongcheng Gao, Yibo Miao, and Hao Zhang · 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
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On the reliability of watermarks for large language models, 2023
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein · 2023
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Cited alongside, same era.
Generating scholarly content with chatgpt: ethical challenges for medical publishing
Michael Liebrenz, Roman Schleifer, Anna Buadze, Dinesh Bhugra, and Alexander Smith · 2023
Cited alongside, same era.
Mgtbench: Benchmarking machine-generated text detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang · 2023
Cited alongside, same era.
Sandra Mitrović, Davide Andreoletti, and Omran Ayoub · 2023
Cited alongside, same era.
Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
Cited alongside, same era.
On the possibilities of ai-generated text detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang · 2023
Cited alongside, same era.
The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu · 2023
Cited alongside, same era.
Distinguishing human generated text from chatgpt generated text using machine learning
Niful Islam, Debopom Sutradhar, Humaira Noor, Jarin Tasnim Raya, Monowara Tabassum Maisha, and Dewan Md Farid · 2023
Cited alongside, same era.
Travis Munyer and Xin Zhong · 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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Christoforos Vasilatos, Manaar Alam, Talal Rahwan, Yasir Zaki, and Michail Maniatakos · 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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Denoising diffusion models for out-of-distribution detection
Mark S. Graham, Walter H.L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, and Jorge Cardoso · 2023
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Dr. llama: Improving small language models in domain-specific qa via generative data augmentation
Zhen Guo, Peiqi Wang, Yanwei Wang, and Shangdi Yu · 2023
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