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The power of natural language generation models has provoked a flurry of interest in automatic methods to detect if a piece of text is human or machine-authored.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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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 · 1908
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Read, attend and comment: a deep architecture for automatic news comment generation
Ze Yang, Can Xu, Wei Wu, and Zhoujun Li. 2019 · 1909
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Suffix arrays: a new method for on-line string searches
Udi Manber and Gene Myers. 1993 · 1993
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Algorithms on stings, trees, and sequences: Computer science and computational biology
Dan Gusfield. 1997 · 1997
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Building natural language generation systems
Ehud Reiter and Robert Dale. 2000 · 2000
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Replacing suffix trees with enhanced suffix arrays
Mohamed Ibrahim Abouelhoda, Stefan Kurtz, and Enno Ohlebusch. 2004 · 2004
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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, et al. 2020 · 2005
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Transformers are better than humans at identifying generated text
Antonis Maronikolakis, Mark Stevenson, and Hinrich Schutze. 2020 · 2009
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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 · 2010
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A distributional approach to controlled text generation
Muhammad Khalifa, Hady Elsahar, and Marc Dymetman. 2020 · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Cited alongside, same era.
GLTR: Statistical detection and visualization of generated text
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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Trump ou biden : l’intelligence articielle a-t-elle joué un rôle dans l’élection américaine ?
Hugo Leroux. 2020 · 2020
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How decoding strategies affect the verifiability of generated text
Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, and Sebastian Riedel. 2020 · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
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Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019 · 2019
Cited alongside, same era.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2020
Cited alongside, same era.
Fˆ2-softmax: Diversifying neural text generation via frequency factorized softmax
Byung-Ju Choi, Jimin Hong, David Park, and Sang Wan Lee. 2020 · 2020
Cited alongside, same era.
Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020 · 2020
Later among the works it cites.
Residual energy-based models for text
Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc’Aurelio Ranzato, and Arthur Szlam. 2021 · 2021
Closest in time.
Truth, lies, and automation: How language models could change disinformation
Ben Buchanan, Andrew Lohn, Micah Musser, and Katerina Sedova. 2021 · 2021
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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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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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Turingbench: A benchmark environment for turing test in the age of neural text generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee. 2021 · 2021
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