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Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 1914
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Differential privacy
Cynthia Dwork · 2006
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Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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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
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Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
Mark Bun and Thomas Steinke · 2016
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Gdpr, 2016
GDPR · 2016
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Rényi differential privacy
Ilya Mironov · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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A deep ensemble model with slot alignment for sequence-to-sequence natural language generation
Juraj Juraska, Panagiotis Karagiannis, Kevin K Bowden, and Marilyn A Walker · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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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 R Bowman · 2018
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Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum · 2019
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Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Noam Shazeer, Peter J Liu, Sharan Narang, Wei Li, and Yanqi Zhou · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe · 2020
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
Cited alongside, same era.
A differentially private text perturbation method using a regularized mahalanobis metric
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, and Nathanael Teissier · 2020
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2022
Later among the works it cites.
Adaptive private-k-selection with adaptive k and application to multi-label pate
Yuqing Zhu and Yu-Xiang Wang · 2022
Later among the works it cites.
Tem: High utility metric differential privacy on text
Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan, and Ke Wang · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman SM Chow, and Huan Sun · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
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
Cited alongside, same era.
Ensembles and cocktails: Robust finetuning for natural language generation
John Hewitt, Xiang Lisa Li, Sang Michael Xie, Benjamin Newman, and Percy Liang · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2021
Cited alongside, same era.
Adaprompt: Adaptive model training for prompt-based nlp
Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, and Yue Zhang · 2022
Cited alongside, same era.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Cited alongside, same era.
Zhenhua He, Aditi Saluja, Richard Lawrence, Dhruva Chakravorty, Francis Dang, Lisa Perez, and Honggao Liu · 2023
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Promptboosting: Black-box text classification with ten forward passes
Bairu Hou, Joe O’connor, Jacob Andreas, Shiyu Chang, and Yang Zhang · 2023
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The entire prompt of microsoft bing chat?! (hi, sydney.), 2023
Kevin Liu · 2023
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Analyzing leakage of personally identifiable information in language models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Béguelin · 2023
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Gpt-4 technical report
R OpenAI · 2023
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Prompting ai art: An investigation into the creative skill of prompt engineering
Jonas Oppenlaender, Rhema Linder, and Johanna Silvennoinen · 2023
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Boosted prompt ensembles for large language models
Silviu Pitis, Michael R Zhang, Andrew Wang, and Jimmy Ba · 2023
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Tan without a burn: Scaling laws of dp-sgd
Tom Sander, Pierre Stock, and Alexandre Sablayrolles · 2023
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Deep language networks: Joint prompt training of stacked llms using variational inference
Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, and Nicolas Le Roux · 2023
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Privacy-preserving in-context learning with differentially private few-shot generation
Xinyu Tang, Richard Shin, Huseyin A Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Locally differentially private document generation using zero shot prompting
Saiteja Utpala, Sara Hooker, and Pin Yu Chen · 2023
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Privacy-preserving in-context learning for large language models
Tong Wu, Ashwinee Panda, Jiachen T Wang, and Prateek Mittal · 2023
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Offsite-tuning: Transfer learning without full model
Guangxuan Xiao, Ji Lin, and Song Han · 2023
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