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Numerous studies have highlighted the privacy risks associated with pretrained large language models.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Mathematics and the picturing of data
John W Tukey. 1975 · 1975
Earlier work this paper cites.
Can pseudonymity really guarantee privacy?
Josyula R Rao, Pankaj Rohatgi, et al. 2000 · 2000
Earlier work this paper cites.
N-gram-based author profiles for authorship attribution
Vlado Kešelj, Fuchun Peng, Nick Cercone, and Calvin Thomas. 2003 · 2003
Earlier work this paper cites.
A face is exposed for aol searcher no. 4417749
Michael Barbaro and Tom Zeller Jr. 2006 · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
A picture of search
Greg Pass, Abdur Chowdhury, and Cayley Torgeson. 2006 · 2006
Earlier work this paper cites.
I know what you did last summer: query logs and user privacy
Rosie Jones, Ravi Kumar, Bo Pang, and Andrew Tomkins. 2007 · 2007
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar. 2007 · 2007
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2011 · 2011
Earlier work this paper cites.
The median hypothesis
Ran Gilad-Bachrach and Chris J.C. Burges. 2012 · 2012
Earlier work this paper cites.
Geo-indistinguishability: Differential privacy for location-based systems
Miguel E Andrés, Nicolás E Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013 · 2013
Earlier work this paper cites.
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.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright. 2013 · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
User review sites as a resource for large-scale sociolinguistic studies
Dirk Hovy, Anders Johannsen, and Anders Søgaard. 2015 · 2015
Earlier work this paper cites.
An analysis of the user occupational class through twitter content
Daniel Preoţiuc-Pietro, Vasileios Lampos, and Nikolaos Aletras. 2015 · 2015
Earlier work this paper cites.
Learning with privacy at scale
D Apple. 2017 · 2017
Earlier work this paper cites.
Convolutional neural networks for authorship attribution of short texts
Prasha Shrestha, Sebastian Sierra, Fabio A González, Manuel Montes, Paolo Rosso, and Thamar Solorio. 2017 · 2017
Earlier work this paper cites.
The US census bureau adopts differential privacy
John M Abowd. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Differential privacy at scale: Uber and Berkeley collaboration
Joe Near. 2018 · 2018
Cited alongside, same era.
{ \{ A4NT } \} : Author attribute anonymity by adversarial training of neural machine translation
Rakshith Shetty, Bernt Schiele, and Mario Fritz. 2018 · 2018
Cited alongside, same era.
Heuristic authorship obfuscation
Janek Bevendorff, Martin Potthast, Matthias Hagen, and Benno Stein. 2019 · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Differentially private imaging via latent space manipulation
Tao Li and Chris Clifton. 2021 · 2021
Later among the works it cites.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2021 · 2021
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, et al. 2021 · 2021
Later among the works it cites.
Large-scale differentially private bert
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. 2022 · 2022
Later among the works it cites.
Lamp: Extracting text from gradients with language model priors
Mislav Balunovic, Dimitar Iliev Dimitrov, Nikola Jovanović, and Martin Vechev. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans. 2019 · 2019
Cited alongside, same era.
Misleading authorship attribution of source code using adversarial learning
Erwin Quiring, Alwin Maier, and Konrad Rieck. 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.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Paraphrasing with large language models
Sam Witteveen and Martin Andrews. 2019 · 2019
Cited alongside, same era.
Paws: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
Cited alongside, same era.
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al. 2022 · 2022
Later among the works it cites.
A critical review on the use (and misuse) of differential privacy in machine learning
Alberto Blanco-Justicia, David Sánchez, Josep Domingo-Ferrer, and Krishnamurty Muralidhar. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
Later among the works it cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Later among the works it cites.
Differentially private language models for secure data sharing
Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, and Mrinmaya Sachan. 2022a · 2022
Later among the works it cites.
The limits of word level differential privacy
Justus Mattern, Benjamin Weggenmann, and Florian Kerschbaum. 2022b · 2022
Later among the works it cites.
Sentence-level privacy for document embeddings
Casey Meehan, Khalil Mrini, and Kamalika Chaudhuri. 2022 · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
Later among the works it cites.
Upton: Unattributable authorship text via data poisoning
Ziyao Wang, Thai Le, and Dongwon Lee. 2022 · 2022
Later among the works it cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022 · 2022
Later among the works it cites.
A customized text sanitization mechanism with differential privacy
Sai Chen, Fengran Mo, Yanhao Wang, Cen Chen, Jian-Yun Nie, Chengyu Wang, and Jamie Cui. 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.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Closest in time.
Textbooks are all you need ii: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
On the challenges of using black-box apis for toxicity evaluation in research
Luiza Pozzobon, Beyza Ermis, Patrick Lewis, and Sara Hooker. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Closest in time.