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Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks.
Detecting fake content with relative entropy scoring
Thomas Lavergne, Tanguy Urvoy, and François Yvon · 2008
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A review of digital watermarking techniques for text documents
Zunera Jalil and Anwar M Mirza · 2009
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Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, et al · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
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A review of text watermarking: theory, methods, and applications
Nurul Shamimi Kamaruddin, Amirrudin Kamsin, Lip Yee Por, and Hameedur Rahman · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
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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
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Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush · 2019
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Unifying human and statistical evaluation for natural language generation
Tatsunori B Hashimoto, Hugh Zhang, and Percy Liang · 2019
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu · 2019
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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
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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
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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
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Generating sentiment-preserving fake online reviews using neural language models and their human-and machine-based detection
David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H Nguyen, Junichi Yamagishi, and Isao Echizen · 2020
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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, et al · 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
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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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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and VS Laks Lakshmanan · 2020
Cited alongside, same era.
Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee · 2020
Cited alongside, same era.
Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Sahar Abdelnabi and Mario Fritz · 2021
Cited alongside, same era.
Detecting fake news using machine learning: A systematic literature review
Alim Al Ayub Ahmed, Ayman Aljabouh, Praveen Kumar Donepudi, and Myung Suh Choi · 2021
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman · 2021
Cited alongside, same era.
mgpt: Few-shot learners go multilingual
Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Anastasia Kozlova, and Tatiana Shavrina · 2022
Later among the works it cites.
Wordcraft: story writing with large language models
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito · 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, et al · 2022
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Combating misinformation in the age of llms: Opportunities and challenges
Canyu Chen and Kai Shu · 2023
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Cnet secretly used ai on articles that didn’t disclose that fact, staff say
Jon Christian · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, Antonio Martella, and Maurizio Tesconi · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model
Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al · 2022
Cited alongside, same era.
Stanford CRFM Introduces PubMedGPT 2.7B
Elliot Bolton, David Hall, Michihiro Yasunaga, Tony Lee, Chris Manning, and Percy Liang · 2022
Cited alongside, same era.
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, et al · 2022
Cited alongside, same era.
Watermarking pre-trained language models with backdooring
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein · 2023
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Do language models plagiarize?
Jooyoung Lee, Thai Le, Jinghui Chen, and Dongwon Lee · 2023
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Deepfake text detection in the wild
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Longyue Wang, Linyi Yang, Shuming Shi, and Yue Zhang · 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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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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Sandra Mitrović, Davide Andreoletti, and Omran Ayoub · 2023
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OpenAI · 2023
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Deepfake text detection: Limitations and opportunities
Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah, Abdullah Rehman, Yoonjin Kim, Parantapa Bhattacharya, Mobin Javed, and Bimal Viswanath · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
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Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text
Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov · 2023
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Gptzero: Towards detection of ai-generated text using zero-shot and supervised methods, 2023
Edward Tian and Alexander Cui · 2023
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Detection of ai-generated essays in writing assessment
Duanli Yan, Michael Fauss, Jiangang Hao, and Wenju Cui · 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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