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Large pre-trained language models achieve impressive results across many tasks.
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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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans. 2019 · 1912
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Zlib compression library
Jean-loup Gailly and Mark Adler. 2004 · 2004
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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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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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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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
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Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov. 2019 · 2019
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Analyzing information leakage of updates to natural language models
Santiago Zanella Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt. 2020 · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman. 2020 · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang. 2020 · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 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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Does BERT pretrained on clinical notes reveal sensitive data?
Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, and Byron C. Wallace. 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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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2021
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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
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Cited alongside, same era.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2022a
Cited in the paper.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022b
Cited in the paper.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 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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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
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