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This is the first survey of the active area of AI research that focuses on privacy issues in Large Language Models (LLMs).
Publicly available clinical BERT embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott · 1909
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 1911
Earlier work this paper cites.
The influence curve and its role in robust estimation
Frank R. Hampel · 1974
Earlier work this paper cites.
Brown corpus manual
W. N. Francis and H. Kucera · 1979
Earlier work this paper cites.
Influential observations in linear regression
R Dennis Cook · 1979
Earlier work this paper cites.
Feist publications, inc. v. rural telephone service company, inc., 1991
499 U.S. 340 US Supreme Court · 1991
Earlier work this paper cites.
zlib compression library, 1995
Jean-Loup Gailly and Mark Adler · 1995
Earlier work this paper cites.
Building a question answering test collection
Ellen M. Voorhees and Dawn M. Tice · 2000
Earlier work this paper cites.
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2003
Earlier work this paper cites.
URL http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html
Antonio Gulli, 2004 · 2004
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn Treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 2004
Earlier work this paper cites.
Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics
Chin-Yew Lin and Franz Josef Och · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Europarl: A parallel corpus for statistical machine translation
Philipp Koehn · 2005
Earlier work this paper cites.
The creative mind: Myths and mechanisms
Margaret A. Boden · 2005
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson · 2007
Earlier work this paper cites.
Common crawl, 2008
Common Crawl · 2008
Earlier work this paper cites.
Sentiment classification using distant supervision
Alec Go · 2009
Earlier work this paper cites.
Large text compression benchmark, 2009
Matt Mahoney · 2009
Earlier work this paper cites.
Wikicorpus: A word-sense disambiguated multilingual Wikipedia corpus
Samuel Reese, Gemma Boleda, Montse Cuadros, Lluís Padró, and German Rigau · 2010
Earlier work this paper cites.
What can we learn privately?, 2010
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2010
Earlier work this paper cites.
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Cristian Danescu-Niculescu-Mizil and Lillian Lee · 2011
Earlier work this paper cites.
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality, 2013
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate · 2013
Earlier work this paper cites.
Comunication-efficient algorithms for statistical optimization, 2013
Yuchen Zhang, John C. Duchi, and Martin Wainwright · 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
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling, 2014
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2014
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, 2014
Haşim Sak, Andrew Senior, and Françoise Beaufays · 2014
Earlier work this paper cites.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books, 2015
Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend, 2015
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Earlier work this paper cites.
The Ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems
Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau · 2015
Earlier work this paper cites.
Recurrent neural network regularization, 2015
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2015
Earlier work this paper cites.
Customer simulation for direct marketing experiments
Yegor Tkachenko, Mykel J. Kochenderfer, and Krzysztof Kluza · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark · 2016
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data, 2016
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Smartphone ownership and internet usage continues to climb in emerging economies, Feb 2016
Jacob Poushter · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation, 2016
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units, 2016
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Earlier work this paper cites.
Conversational contextual cues: The case of personalization and history for response ranking, 2016
Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun hsuan Sung, Brian Strope, and Ray Kurzweil · 2016
Earlier work this paper cites.
Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016
European Parliament and Council of the European Union · 2016
Earlier work this paper cites.
Humans forget, machines remember: Artificial intelligence and the right to be forgotten
Eduard Fosch Villaronga, Peter Kieseberg, and Tiffany Li · 2017
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization, 2017
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models, 2017
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
Earlier work this paper cites.
Efficient natural language response suggestion for smart reply, 2017
Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil · 2017
Earlier work this paper cites.
The e2e dataset: New challenges for end-to-end generation, 2017
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser · 2017
Earlier work this paper cites.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics, 2017
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey F. Naughton · 2017
Earlier work this paper cites.
Artificial intelligence and the copyright dilemma, May 2017
Kalin Hristov · 2017
Earlier work this paper cites.
How copyright law can fix artificial intelligence’s implicit bias problem
Amanda Levendowski · 2017
Earlier work this paper cites.
Findings of the 2018 conference on machine translation (WMT18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, and Christof Monz · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Earlier work this paper cites.
Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting, 2018
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Earlier work this paper cites.
An efficient framework for learning sentence representations, 2018
Lajanugen Logeswaran and Honglak Lee · 2018
Earlier work this paper cites.
Learning semantic textual similarity from conversations
Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil · 2018
Earlier work this paper cites.
Sockeye: A toolkit for neural machine translation, 2018
Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post · 2018
Earlier work this paper cites.
Privacy-preserving prediction, 2018
Cynthia Dwork and Vitaly Feldman · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Scalable private learning with pate, 2018
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Earlier work this paper cites.
Learning differentially private recurrent language models, 2018
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Earlier work this paper cites.
Invited paper: Local differential privacy on metric spaces: Optimizing the trade-off with utility
Mário Alvim, Konstantinos Chatzikokolakis, Catuscia Palamidessi, and Anna Pazii · 2018
Earlier work this paper cites.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising, 2018
Borja Balle and Yu-Xiang Wang · 2018
Earlier work this paper cites.
Mvg mechanism: Differential privacy under matrix-valued query
Thee Chanyaswad, Alex Dytso, H. Vincent Poor, and Prateek Mittal · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference, 2018
Adina Williams, Nikita Nangia, and Samuel R. Bowman · 2018
Earlier work this paper cites.
A call for clarity in reporting BLEU scores
Matt Post · 2018
Earlier work this paper cites.
Bigpatent: A large-scale dataset for abstractive and coherent summarization, 2019
Eva Sharma, Chen Li, and Lu Wang · 2019
Earlier work this paper cites.
Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
Earlier work this paper cites.
Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets
Yifan Peng, Shankai Yan, and Zhiyong Lu · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Stack overflow data, Mar 2019
Stack Overflow · 2019
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks, 2019
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 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
Earlier work this paper cites.
White-box vs black-box: Bayes optimal strategies for membership inference, 2019
Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier, Cordelia Schmid, and Hervé Jégou · 2019
Earlier work this paper cites.
Learning cross-lingual sentence representations via a multi-task dual-encoder model
Muthu Chidambaram, Yinfei Yang, Daniel Cer, Steve Yuan, Yunhsuan Sung, Brian Strope, and Ray Kurzweil · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Auditing data provenance in text-generation models, 2019
Congzheng Song and Vitaly Shmatikov · 2019
Cited alongside, same era.
Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov · 2019
Cited alongside, same era.
Federated learning for mobile keyboard prediction, 2019
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2019
Cited alongside, same era.
The new legal landscape for text mining and machine learning
Matthew Sag · 2019
Kart: Parameterization of privacy leakage scenarios from pre-trained language models, 2022
Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki · 2022
Later among the works it cites.
Sequential Good-Turing and the missing species problem
Oskar Andersson · 2022
Later among the works it cites.
Provable membership inference privacy, 2022
Zachary Izzo, Jinsung Yoon, Sercan O. Arik, and James Zou · 2022
Later among the works it cites.
Selective differential privacy for language modeling, 2022
Weiyan Shi, Aiqi Cui, Evan Li, Ruoxi Jia, and Zhou Yu · 2022
Later among the works it cites.
Large language models can be strong differentially private learners, 2022
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
Later among the works it cites.
Differentially private fine-tuning of language models, 2022
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
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Cited alongside, same era.
Algorithmic fair use
Dan L Burk · 2019
Cited alongside, same era.
Americans and privacy: Concerned, confused and feeling lack of control over their personal information, Nov 2019
Brooke Auxier, Lee Rainie, Monica Anderson, Andrew Perrin, Madhu Kumar, and Erica Turner · 2019
Cited alongside, same era.
Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and lstm recurrent neural networks
C. Okan Sakar, S. Polat, Mete Katircioglu, and Yomi Kastro · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
Cited alongside, same era.
SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer · 2019
Cited alongside, same era.
Later among the works it cites.
An efficient dp-sgd mechanism for large scale nlp models, 2022
Christophe Dupuy, Radhika Arava, Rahul Gupta, and Anna Rumshisky · 2022
Later among the works it cites.
Submix: Practical private prediction for large-scale language models, 2022
Antonio Ginart, Laurens van der Maaten, James Zou, and Chuan Guo · 2022
Later among the works it cites.
Differentially private decoding in large language models, 2022
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, and Richard Zemel · 2022
Later among the works it cites.
The scary truth about ai copyright is nobody knows what will happen next, Nov 2022
James Vincent · 2022
Later among the works it cites.
Formalizing human ingenuity: A quantitative framework for copyright law’s substantial similarity, 2022
Sarah Scheffler, Eran Tromer, and Mayank Varia · 2022
Later among the works it cites.
Deepcreativity: Measuring creativity with deep learning techniques, 2022
Giorgio Franceschelli and Mirco Musolesi · 2022
Later among the works it cites.
If influence functions are the answer, then what is the question?, 2022
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger Grosse · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe · 2022
Later among the works it cites.
Privacy adhering machine un-learning in nlp, 2022
Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, and Dan Roth · 2022
Later among the works it cites.
Fortuitous forgetting in connectionist networks, 2022
Hattie Zhou, Ankit Vani, Hugo Larochelle, and Aaron Courville · 2022
Later among the works it cites.
URL https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
Oct 2023 · 2023
Closest in time.
Chatgpt: A case study on copyright challenges for generative artificial intelligence systems
Nicola Lucchi · 2023
Closest in time.
The secret behind large language models: Memorization over understanding?, Apr 2023
SCRT Labs · 2023
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F.t.c. opens investigation into chatgpt maker over technology’s potential harms, Jul 2023
Cecilia Kang and Cade Metz · 2023
Closest in time.
Preventing verbatim memorization in language models gives a false sense of privacy, 2023
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, and Nicholas Carlini · 2023
Closest in time.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, John A. Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuan-Fang Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
Closest in time.
Scalable extraction of training data from (production) language models, 2023
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee · 2023
Closest in time.
Measuring forgetting of memorized training examples, 2023
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Chiyuan Zhang · 2023
Closest in time.
Do localization methods actually localize memorized data in llms?, 2023
Ting-Yun Chang, Jesse Thomason, and Robin Jia · 2023
Closest in time.
Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
Closest in time.
Detecting pretraining data from large language models, 2023
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer · 2023
Closest in time.
Who’s harry potter? approximate unlearning in llms, 2023
Ronen Eldan and Mark Russinovich · 2023
Closest in time.
MoPe: Model perturbation based privacy attacks on language models
Marvin Li, Jason Wang, Jeffrey Wang, and Seth Neel · 2023
Closest in time.
Using membership inference attacks to evaluate privacy-preserving language modeling fails for pseudonymizing data
Thomas Vakili and Hercules Dalianis · 2023
Closest in time.
Practical membership inference attacks against fine-tuned large language models via self-prompt calibration, 2023
Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, and Tao Jiang · 2023
Closest in time.
Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
Closest in time.
Did the neurons read your book? document-level membership inference for large language models, 2023
Matthieu Meeus, Shubham Jain, Marek Rei, and Yves-Alexandre de Montjoye · 2023
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Redpajama: an open dataset for training large language models, 2023
Together Computer · 2023
Closest in time.
Openllama: An open reproduction of llama, May 2023
Xinyang Geng and Hao Liu · 2023
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Tmi! finetuned models leak private information from their pretraining data, 2023
John Abascal, Stanley Wu, Alina Oprea, and Jonathan Ullman · 2023
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Scalable membership inference attacks via quantile regression, 2023
Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, and Zhiwei Steven Wu · 2023
Closest in time.
Bag of tricks for training data extraction from language models, 2023
Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, and Shuicheng Yan · 2023
Closest in time.
Does fine-tuning gpt-3 with the openai api leak personally-identifiable information?, 2023
Albert Yu Sun, Eliott Zemour, Arushi Saxena, Udith Vaidyanathan, Eric Lin, Christian Lau, and Vaikkunth Mugunthan · 2023
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Can sensitive information be deleted from llms? objectives for defending against extraction attacks, 2023
Vaidehi Patil, Peter Hase, and Mohit Bansal · 2023
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Traces of memorisation in large language models for code, 2023
Ali Al-Kaswan, Maliheh Izadi, and Arie van Deursen · 2023
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Beyond memorization: Violating privacy via inference with large language models, 2023
Robin Staab, Mark Vero, Mislav Balunović, and Martin Vechev · 2023
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Data portraits: Recording foundation model training data, 2023
Marc Marone and Benjamin Van Durme · 2023
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Prompts should not be seen as secrets: Systematically measuring prompt extraction attack success, 2023
Yiming Zhang and Daphne Ippolito · 2023
Closest in time.
Privacy-preserving in-context learning with differentially private few-shot generation, 2023
Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim · 2023
Closest in time.
Knowledge sanitization of large language models, 2023
Yoichi Ishibashi and Hidetoshi Shimodaira · 2023
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Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman S. M. Chow, and Huan Sun · 2023
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Dp-forward: Fine-tuning and inference on language models with differential privacy in forward pass
Minxin Du, Xiang Yue, Sherman S. M. Chow, Tianhao Wang, Chenyu Huang, and Huan Sun · 2023
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Large language models can be good privacy protection learners, 2023
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, and Wei Cheng · 2023
Closest in time.
Direct preference optimization: Your language model is secretly a reward model, 2023
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.
Medalpaca – an open-source collection of medical conversational ai models and training data, 2023
Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K. Bressem · 2023
Closest in time.
Scrubadub: A python library for cleaning sensitive data from text
LeapBeyond · 2023
Closest in time.
This artist is dominating ai-generated art. and he’s not happy about it., Nov 2022b
Melissa Heikkila · 2023
Closest in time.
Sarah silverman is suing openai and meta for copyright infringement, Jul 2023b
Wes Davis · 2023
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Copyright violations and large language models, 2023
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard · 2023
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Ai can’t replace humans yet - but if the wga writers don’t win, it might not matter, May 2023
Ryan Broderick · 2023
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Data rivers: Re-balancing the data ecosystem that makes generative ai possible, Apr 2023
Sylvie Delacroix · 2023
Closest in time.
Foundation models and fair use, 2023
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, and Percy Liang · 2023
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Copyright registration guidance: Works containing material generated by artificial intelligence 2023, 2023
US Copyright · 2023
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Provable copyright protection for generative models, 2023
Nikhil Vyas, Sham Kakade, and Boaz Barak · 2023
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Can copyright be reduced to privacy?, 2023
Niva Elkin-Koren, Uri Hacohen, Roi Livni, and Shay Moran · 2023
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California consumer privacy act, 2023
State of California Department of Justice · 2023
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Senate bill s365b, 2023
State of New York · 2023
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Consumer privacy protection act, 2023
Government of Canada · 2023
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Consumers take data control into their own hands amid rising privacy concerns, Apr 2023
Help Net Security · 2023
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Towards unbounded machine unlearning, 2023
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2023
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Knowledge unlearning for mitigating privacy risks in language models
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Mass-editing memory in a transformer, 2023
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau · 2023
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense, 2023
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer · 2023
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Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models, 2023
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun · 2023
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Large language model unlearning, 2023
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2023
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Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models
Aly Kassem, Omar Mahmoud, and Sherif Saad · 2023
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In-context unlearning: Language models as few shot unlearners, 2023
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2023
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Bloom: A 176b-parameter open-access multilingual language model, 2023
BigScience Workshop, :, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. 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Unlearn what you want to forget: Efficient unlearning for llms, 2023
Jiaao Chen and Diyi Yang · 2023
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Palm 2 technical report, 2023
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Rethinking machine unlearning for large language models, 2024
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, and Yang Liu · 2024
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Interpreting gpt: The logit lens, 2020
nostalgebraist · 2024
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Unlearnable algorithms for in-context learning, 2024
Andrei Muresanu, Anvith Thudi, Michael R. Zhang, and Nicolas Papernot · 2024
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Inexact unlearning needs more careful evaluations to avoid a false sense of privacy, 2024
Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, and Nicolas Papernot · 2024
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Universal sentence encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil · 2029
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Artificial intelligence’s fair use crisis
Benjamin L. W. Sobel · 2036
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