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Paraphrasing of offensive content is a better alternative to content removal and helps improve civility in a communication environment.
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
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1904
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Detecting offensive tweets via topical feature discovery over a large scale twitter corpus
Guang Xiang, Bin Fan, Ling Wang, Jason Hong, and Carolyn Rose. 2012 · 1984
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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The harm in hate speech
Jeremy Waldron. 2012 · 2012
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Detecting hate speech on the world wide web
William Warner and Julia Hirschberg. 2012 · 2012
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A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013 · 2013
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Locate the hate: Detecting tweets against blacks
Irene Kwok and Yuzhou Wang. 2013 · 2013
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Cursing in english on twitter
Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, and Amit P Sheth. 2014 · 2014
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Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making
Pete Burnap and Matthew L Williams. 2015 · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Opensubtitles2016: Extracting large parallel corpora from movie and tv subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
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Abusive language detection in online user content
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016 · 2016
Cited alongside, same era.
Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
John Wieting and Kevin Gimpel. 2017 · 2017
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Moderation practices as emotional labor in sustaining online communities: The case of aapi identity work on reddit
Bryan Dosono and Bryan Semaan. 2019 · 2019
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Appdia: A discourse-aware transformer-based style transfer model for offensive social media conversations
Katherine Atwell, Sabit Hassan, and Malihe Alikhani. 2022 · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant. 2022 · 2022
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Studying the effect of moderator biases on the diversity of online discussions: A computational cross-linguistic study
Sabit Hassan, Katherine J Atwell, and Malihe Alikhani. 2022 · 2022
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Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. 2022 · 2022
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A unified deep learning architecture for abuse detection
Antigoni Maria Founta, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, and Ilias Leontiadis. 2019 · 2019
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Did you suspect the post would be removed? understanding user reactions to content removals on reddit
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Detoxify
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What makes good in-context examples for gpt- 3 3 ?
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Paradetox: Detoxification with parallel data
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Complementary explanations for effective in-context learning
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Ves Stoyanov, Greg Durrett, and Ramakanth Pasunuru. 2022 · 2022
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Active example selection for in-context learning
Yiming Zhang, Shi Feng, and Chenhao Tan. 2022 · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
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Mixture of soft prompts for controllable data generation
Derek Chen, Celine Lee, Yunan Lu, Domenic Rosati, and Zhou Yu. 2023 · 2023
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
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Multilingual content moderation: A case study on reddit
Meng Ye, Karan Sikka, Katherine Atwell, Sabit Hassan, Ajay Divakaran, and Malihe Alikhani. 2023 · 2023
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