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Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.
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 · 1901
Earlier work this paper cites.
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 · 1906
Earlier work this paper cites.
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 · 1907
Earlier work this paper cites.
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
Earlier work this paper cites.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
Earlier work this paper cites.
Attention, intentions, and the structure of discourse
Barbara J Grosz and Candace L Sidner. 1986 · 1986
Earlier work this paper cites.
Rhetorical structure theory: A theory of text organization
William C Mann and Sandra A Thompson. 1987 · 1987
Earlier work this paper cites.
Planning coherent multisentential text
Eduard H Hovy. 1988 · 1988
Earlier work this paper cites.
Experiments using stochastic search for text planning
Chris Mellish, Alistair Knott, Jon Oberlander, and Mick O’Donnell. 1998 · 1998
Earlier work this paper cites.
Stochastic text structuring using the principle of continuity
Nikiforos Karamanis and Hisar Maruli Manurung. 2002 · 2002
Earlier work this paper cites.
Learning to extract keyphrases from text
Peter D Turney. 2002 · 2002
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020 · 2003
Earlier work this paper cites.
Probabilistic text structuring: Experiments with sentence ordering
Mirella Lapata. 2003 · 2003
Earlier work this paper cites.
Computing locally coherent discourses
Ernst Althaus, Nikiforos Karamanis, and Alexander Koller. 2004 · 2004
Earlier work this paper cites.
Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
Improving language generation with sentence coherence objective
Ruixiao Sun, Jie Yang, and Mehrdad Yousefzadeh. 2020 · 2009
Earlier work this paper cites.
Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos. 2020 · 2010
Earlier work this paper cites.
Graph-based term weighting for information retrieval
Roi Blanco and Christina Lioma. 2011 · 2011
Earlier work this paper cites.
Spammer behavior analysis and detection in user generated content on social networks
Enhua Tan, Lei Guo, Songqing Chen, Xiaodong Zhang, and Yihong Zhao. 2012 · 2012
Earlier work this paper cites.
Graph-based term weighting for text categorization
Fragkiskos D Malliaros and Konstantinos Skianis. 2015 · 2015
Earlier work this paper cites.
Fake news on twitter during the 2016 us presidential election
Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. 2019 · 2016
Earlier work this paper cites.
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 · 2016
Earlier work this paper cites.
Semi-supervised sequence tagging with bidirectional language models
Matthew E Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Max Welling and Thomas N Kipf. 2016 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush. 2019 · 2019
Cited alongside, same era.
Syntax-aware aspect level sentiment classification with graph attention networks
Binxuan Huang and Kathleen M Carley. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Supervised contrastive learning for pre-trained language model fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2021 · 2021
Later among the works it cites.
Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification
Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, and Bowen Zhou. 2021 · 2021
Later among the works it cites.
Few-shot bot: Prompt-based learning for dialogue systems
Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, and Pascale Fung. 2021 · 2021
Later among the works it cites.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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Later among the works it cites.
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