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Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. 2022 · 1914
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Label-only membership inference attacks
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 1974
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Wordnet: a lexical database for english
George A Miller. 1995 · 1995
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Looking for a few good metrics: Rouge and its evaluation
Chin-Yew Lin and FJ Och. 2004 · 2004
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Differential privacy: A survey of results
Cynthia Dwork. 2008 · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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A boundary tilting persepective on the phenomenon of adversarial examples
Thomas Tanay and Lewis Griffin. 2016 · 2016
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Logan: Evaluating information leakage of generative models using generative adversarial networks
Jamie Hayes, Luca Melis, George Danezis, and ED Cristofaro. 2017 · 2017
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Differential privacy in the wild: A tutorial on current practices & open challenges
Ashwin Machanavajjhala, Xi He, and Michael Hay. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
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A new defense against adversarial images: Turning a weakness into a strength
Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, and Kilian Q Weinberger. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov. 2019 · 2019
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Privacy risks of securing machine learning models against adversarial examples
Liwei Song, Reza Shokri, and Prateek Mittal. 2019 · 2019
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2021 · 2021
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Membership inference on word embedding and beyond
Saeed Mahloujifar, Huseyin A Inan, Melissa Chase, Esha Ghosh, and Marcello Hasegawa. 2021 · 2021
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Quantifying the privacy risks of learning high-dimensional graphical models
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos. 2021 · 2021
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A thorough evaluation of task-specific pretraining for summarization
Sascha Rothe, Joshua Maynez, and Shashi Narayan. 2021 · 2021
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Membership inference attacks against nlp classification models
Virat Shejwalkar, Huseyin A Inan, Amir Houmansadr, and Robert Sim. 2021 · 2021
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Robustness to adversarial examples can be improved with overfitting
Oscar Deniz, Anibal Pedraza, Noelia Vallez, Jesus Salido, and Gloria Bueno. 2020 · 2020
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Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Sorami Hisamoto, Matt Post, and Kevin Duh. 2020 · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Overview of the mediqa 2021 shared task on summarization in the medical domain
Asma Ben Abacha, Yassine M’rabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, and Dina Demner-Fushman. 2021 · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
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Automatic text summarization: A comprehensive survey
Wafaa S El-Kassas, Cherif R Salama, Ahmed A Rafea, and Hoda K Mohamed. 2021 · 2021
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Sequence length is a domain: Length-based overfitting in transformer models
Dusan Varis and Ondřej Bojar. 2021 · 2021
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The-x: Privacy-preserving transformer inference with homomorphic encryption
Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, and Furu Wei. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022 · 2022
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Sentence-level privacy for document embeddings
Casey Meehan, Khalil Mrini, and Kamalika Chaudhuri. 2022 · 2022
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Quantifying privacy risks of masked language models using membership inference attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri. 2022 · 2022
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Training text-to-text transformers with privacy guarantees
Natalia Ponomareva, Jasmijn Bastings, and Sergei Vassilvitskii. 2022 · 2022
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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D Manning, and Curtis Langlotz. 2020b · 2022
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