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The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users.
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
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, A. Terzis, and Florian Tramèr. 2021a · 1914
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2011 · 2011
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Broadening the scope of differential privacy using metrics
Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. 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 Ng, and Christopher Potts. 2013 · 2013
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth. 2014 · 2014
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. B. McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Plausible deniability for privacy-preserving data synthesis
Vincent Bindschaedler, Reza Shokri, and Carl Gunter. 2017 · 2017
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Membership inference attacks against machine learning models
R. Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2016 · 2017
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 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
Earlier work this paper cites.
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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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Privacy- and utility-preserving textual analysis via calibrated multivariate perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020 · 2020
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020 · 2020
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Privacy risks of general-purpose language models
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 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.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, David W. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, Igor Babuschkin, S. Arun Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew M. Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Cited alongside, same era.
Characterization of mpc-based private inference for transformer-based models
Yongqin Wang, G Edward Suh, Wenjie Xiong, Benjamin Lefaudeux, Brian Knott, Murali Annavaram, and Hsien-Hsin S Lee. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
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, Sergey Yekhanin, and Huishuai Zhang. 2022 · 2022
Later among the works it cites.
Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman S. M. Chow, and Huan Sun. 2023 · 2023
Closest in time.
Flocks of stochastic parrots: Differentially private prompt learning for large language models
Haonan Duan, Adam Dziedzic, Nicolas Papernot, and Franziska Boenisch. 2023 · 2023
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ADePT: Auto-encoder based differentially private text transformation
Satyapriya Krishna, Rahul Gupta, and Christophe Dupuy. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Natural language understanding with privacy-preserving bert
Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, and Marc Najork. 2021 · 2021
Cited alongside, same era.
Milvus: A purpose-built vector data management system
Jianguo Wang, Xiaomeng Yi, Rentong Guo, Hai Jin, Peng Xu, Shengjun Li, Xiangyu Wang, Xiangzhou Guo, Chengming Li, Xiaohai Xu, et al. 2021 · 2021
Cited alongside, same era.
Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin 0001, and Tie-Yan Liu. 2021 · 2021
Cited alongside, same era.
The-x: Privacy-preserving transformer inference with homomorphic encryption
Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, and Jianxin Li. 2022 · 2022
Cited alongside, same era.
Iron: Private inference on transformers
Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang. 2022 · 2022
Cited alongside, same era.
Closest in time.
Towards sentence level inference attack against pre-trained language models
Kang Gu, Ehsanul Kabir, Neha Ramsurrun, Soroush Vosoughi, and Shagufta Mehnaz. 2023 · 2023
Closest in time.
Dp-bart for privatized text rewriting under local differential privacy
Timour Igamberdiev and Ivan Habernal. 2023 · 2023
Closest in time.
Sentence embedding leaks more information than you expect: Generative embedding inversion attack to recover the whole sentence
Haoran Li, Mingshi Xu, and Yangqiu Song. 2023b · 2023
Closest in time.
Analyzing leakage of personally identifiable information in language models
Nils Lukas, A. Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-B’eguelin. 2023 · 2023
Closest in time.
Practical privacy-preserving gaussian process regression via secret sharing
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, and Zenglin Xu. 2023 · 2023
Closest in time.
Text embeddings reveal (almost) as much as text
John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M. Rush. 2023 · 2023
Closest in time.
Tight auditing of differentially private machine learning
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Survey of vector database management systems
James Jie Pan, Jianguo Wang, and Guoliang Li. 2023 · 2023
Closest in time.
Vector database management systems: Fundamental concepts, use-cases, and current challenges
Toni Taipalus. 2023 · 2023
Closest in time.
Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin A Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, and Robert Sim. 2022 · 2023
Closest in time.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
Closest in time.
Secformer: Towards fast and accurate privacy-preserving inference for large language models
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, and Zenglin Xu. 2024 · 2024
Closest in time.
Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang. 2022 · 2047
Closest in time.