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Adversarial attacks against language models(LMs) are a significant concern.
Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019 · 1903
Earlier work this paper cites.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1904
Earlier work this paper cites.
Robust neural machine translation with doubly adversarial inputs
Yong Cheng, Lu Jiang, and Wolfgang Macherey. 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.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
Earlier work this paper cites.
Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 1908
Earlier work this paper cites.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 1909
Earlier work this paper cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
Earlier work this paper cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 1909
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
Earlier work this paper cites.
A new measure of rank correlation
Maurice G Kendall. 1938 · 1938
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2004
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Parade: A new dataset for paraphrase identification requiring computer science domain knowledge
Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, and James Caverlee. 2020 · 2010
Earlier work this paper cites.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2010
Earlier work this paper cites.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, and Ed Chi. 2020a · 2010
Earlier work this paper cites.
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, and Soroush Vosoughi. 2020 · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
The stanford corenlp natural language processing toolkit
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
Cited alongside, same era.
SemEval-2015 task 1: Paraphrase and semantic similarity in Twitter (PIT)
Wei Xu, Chris Callison-Burch, and Bill Dolan. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2019 · 2019
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LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
Jingjing Xu, Liang Zhao, Hanqi Yan, Qi Zeng, Yun Liang, and Xu Sun. 2019 · 2019
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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First quora dataset release: Question pairs
Shankar Iyer, Nikhil Dandekar, Kornél Csernai, et al. 2017 · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
Cited alongside, same era.
A continuously growing dataset of sentential paraphrases
Wuwei Lan, Siyu Qiu, Hua He, and Wei Xu. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
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Trl: Transformer reinforcement learning
Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, and Nathan Lambert. 2020 · 2020
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Parrot: Paraphrase generation for nlu
Prithiviraj Damodaran. 2021 · 2021
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Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Improving paraphrase detection with the adversarial paraphrasing task
Animesh Nighojkar and John Licato. 2021 · 2021
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Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
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Towards improving adversarial training of nlp models
Jin Yong Yoo and Yanjun Qi. 2021 · 2021
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Impact of adversarial training on robustness and generalizability of language models
Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar, Safa Messaoud, and Sanjay Chawla. 2022 · 2022
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ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model
Chuyun Deng, Mingxuan Liu, Yue Qin, Jia Zhang, Hai-Xin Duan, and Donghong Sun. 2022 · 2022
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Semi-supervised adversarial text generation based on Seq2Seq models
Hieu Le, Dieu-thu Le, Verena Weber, Chris Church, Kay Rottmann, Melanie Bradford, and Peter Chin. 2022 · 2022
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Mteb: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2022 · 2022
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Adversarial attack and defense technologies in natural language processing: A survey
Shilin Qiu, Qihe Liu, Shijie Zhou, and Wen Huang. 2022 · 2022
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Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. 2022 · 2022
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2019 · 2022
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Symbolic discovery of optimization algorithms
Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, et al. 2023 · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023 · 2023
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Chatgpt outperforms crowd-workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
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Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models
Aly Kassem, Omar Mahmoud, and Sherif Saad. 2023 · 2023
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Petter Törnberg. 2023 · 2023
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