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The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work.
Language Models are Few-Shot Learners
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Bertscore: Evaluating text generation with bert
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Roberta: A robustly optimized bert pretraining approach
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The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability
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The components of paraphrase evaluations
Philip M McCarthy, Rebekah H Guess, and Danielle S McNamara. 2009 · 2009
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Adam J. Berinsky, Gregory A. Huber, and Gabriel S. Lenz. 2012 · 2012
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Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection
Alberto Barrón-Cedeño, Marta Vila, M. Martí, and Paolo Rosso. 2013 · 2013
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Squibs: What is a paraphrase?
Rahul Bhagat and Eduard Hovy. 2013 · 2013
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An analysis of automated detection techniques for textual similarity in research documents
Ranjeet Kumar and RC Tripathi. 2013 · 2013
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Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Neural paraphrase generation with stacked residual lstm networks
Aaditya Prakash, Sadid A. Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, and Oladimeji Farri. 2016 · 2016
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Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, and Hang Li. 2017 · 2017
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
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The hitchhiker’s guide to testing statistical significance in natural language processing
Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018 · 2018
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Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Scaling laws for neural language models
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S2ORC: The semantic scholar open research corpus
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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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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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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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Academic Plagiarism Detection: A Systematic Literature Review
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Bi-directional differentiable input reconstruction for low-resource neural machine translation
Xing Niu, Weijia Xu, and Marine Carpuat. 2019 · 2019
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Exploring diverse expressions for paraphrase generation
Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, and Yong Yu. 2019 · 2019
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Paraphrasing with large language models
Sam Witteveen and Martin Andrews. 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
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2020
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With Little Power Comes Great Responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky. 2020 · 2020
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Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3)
N Dehouche. 2021 · 2021
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Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
Norman Meuschke. 2021 · 2021
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Coping with noisy training data labels in paraphrase detection
Teemu Vahtola, Mathias Creutz, Eetu Sjöblom, and Sami Itkonen. 2021 · 2021
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Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
Jan Philip Wahle, Terry Ruas, Norman Meuschke, and Bela Gipp. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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Paraphrase generation: A survey of the state of the art
Jianing Zhou and Suma Bhat. 2021 · 2021
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Neural Language Models are Effective Plagiarists
Stella Biderman and Edward Raff. 2022 · 2022
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