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Ensuring the privacy of Large Language Models (LLMs) is becoming increasingly important.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019 · 1901
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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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
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten. 2019 · 1911
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019 · 1911
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Differential privacy
Cynthia Dwork. 2006 · 2006
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2007 · 2007
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The trade-offs of private prediction
Laurens van der Maaten and Awni Hannun. 2020 · 2007
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
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Rényi divergence and kullback-leibler divergence
Tim Van Erven and Peter Harremos. 2014 · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke. 2016 · 2016
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2016 · 2016
Cited alongside, same era.
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. 2021 · 2016
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017 · 2017
Cited alongside, same era.
Rényi differential privacy
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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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2021 · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlin. 2021 · 2021
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Opacus: User-friendly differential privacy library in pytorch
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Ilya Mironov. 2017 · 2017
Cited alongside, same era.
Privacy-preserving prediction
Cynthia Dwork and Vitaly Feldman. 2018 · 2018
Cited alongside, same era.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato. 2020 · 2020
Cited alongside, same era.
Local differential privacy for sampling
Hisham Husain, Borja Balle, Zac Cranko, and Richard Nock. 2020 · 2020
Cited alongside, same era.
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, et al. 2021 · 2021
Later among the works it cites.
Submix: Practical private prediction for large-scale language models
Antonio Ginart, Laurens van der Maaten, James Zou, and Chuan Guo. 2022 · 2022
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Differentially private decoding in large language models
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, and Richard Zemel. 2022 · 2022
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Composition of differential privacy & privacy amplification by subsampling
Thomas Steinke. 2022 · 2022
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta. 2023 · 2023
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Generalized ptr: User-friendly recipes for data-adaptive algorithms with differential privacy
Rachel Redberg, Yuqing Zhu, and Yu-Xiang Wang. 2023 · 2023
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Safe, secure, and trustworthy development and use of artificial intelligence
US. 2023 · 2023
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Learning to generate image embeddings with user-level differential privacy
Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, and H. Brendan McMahan. 2023 · 2023
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The artificial intelligence act
EU. 2024 · 2024
Closest in time.
Ethos: Rectifying language models in orthogonal parameter space
Lei Gao, Yue Niu, Tingting Tang, Salman Avestimehr, and Murali Annavaram. 2024 · 2024
Closest in time.