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Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles.
Language models are few-shot learners
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Cross-lingual language model pretraining
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Gltr: Statistical detection and visualization of generated text
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Facebook fair’s wmt19 news translation task submission
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Sentence-bert: Sentence embeddings using siamese bert-networks
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Ctrl: A conditional transformer language model for controllable generation
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Automatic detection of generated text is easiest when humans are fooled
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Signature verification using a" siamese" time delay neural network
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On the influence of the kernel on the consistency of support vector machines
Ingo Steinwart. 2001 · 2001
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Neural unsupervised domain adaptation in nlp—a survey
Alan Ramponi and Barbara Plank. 2020 · 2006
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Detecting cross-modal inconsistency to defend against neural fake news
Reuben Tan, Bryan A Plummer, and Kate Saenko. 2020 · 2009
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. 2010 · 2010
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Domain adaptation for large-scale sentiment classification: A deep learning approach
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks VS Lakshmanan. 2020 · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012 · 2012
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Robustness and generalization
Huan Xu and Shie Mannor. 2012 · 2012
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015 · 2015
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Learning text similarity with siamese recurrent networks
Paul Neculoiu, Maarten Versteegh, and Mihai Rotaru. 2016 · 2016
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Learning transferrable representations for unsupervised domain adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena, and Silvio Savarese. 2016 · 2016
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Online human-bot interactions: Detection, estimation, and characterization
Onur Varol, Emilio Ferrara, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
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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 · 2019
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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 · 2019
Contrastive learning for prompt-based few-shot language learners
Yiren Jian, Chongyang Gao, and Soroush Vosoughi. 2022 · 2022
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Are you robert or roberta? deceiving online authorship attribution models using neural text generators
Keenan Jones, Jason RC Nurse, and Shujun Li. 2022 · 2022
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Generalizable implicit hate speech detection using contrastive learning
Youngwook Kim, Shinwoo Park, and Yo-Sub Han. 2022 · 2022
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Improved text classification via contrastive adversarial training
Lin Pan, Chung-Wei Hang, Avirup Sil, and Saloni Potdar. 2022 · 2022
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Contrastive learning from label distribution: A case study on text classification
Tao Qian, Fei Li, Meishan Zhang, Guonian Jin, Ping Fan, and Wenhua Dai. 2022 · 2022
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Contrastive learning improves model robustness under label noise
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Unsupervised domain adaptation for text classification via meta self-paced learning
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