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Retrieval-based language models (LMs) have demonstrated improved interpretability, factuality, and adaptability compared to their parametric counterparts, by incorporating retrieved text from external datastores.
The Health Insurance Portability and Accountability Act of 1996 (HIPAA)
Centers for Medicare & Medicaid Services. 1996 · 1996
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The enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang. 2004 · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov. 2008 · 2008
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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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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 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
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Gmail smart compose: Real-time assisted writing
Mia Xu Chen, Benjamin N Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M Dai, Zhifeng Chen, et al. 2019 · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
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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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Generalization through Memorization: Nearest Neighbor Language Models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
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Kart: Privacy leakage framework of language models pre-trained with clinical records
Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki. 2020 · 2020
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Privacy risks of general-purpose language models
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 2020 · 2020
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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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Recovering private text in federated learning of language models
Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, and Danqi Chen. 2022 · 2022
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Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang. 2022 · 2022
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Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2022 · 2022
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Efficient nearest neighbor language models
Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2021 · 2021
Cited alongside, same era.
Does bert pretrained on clinical notes reveal sensitive data?
Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, and Byron C Wallace. 2021 · 2021
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Understanding unintended memorization in language models under federated learning
Om Dipakbhai Thakkar, Swaroop Ramaswamy, Rajiv Mathews, and Francoise Beaufays. 2021 · 2021
Cited alongside, same era.
Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini. 2021 · 2021
Cited alongside, same era.
Reasoning over public and private data in retrieval-based systems
Simran Arora, Patrick Lewis, Angela Fan, Jacob Kahn, and Christopher Ré. 2022 · 2022
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022 · 2022
Cited alongside, same era.
Anonymization of german financial documents using neural network-based language models with contextual word representations
David Biesner, Rajkumar Ramamurthy, Robin Stenzel, Max Lübbering, Lars Hillebrand, Anna Ladi, Maren Pielka, Rüdiger Loitz, Christian Bauckhage, and Rafet Sifa
Cited in the paper.
Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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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. 2022 · 2022
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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 · 2022
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Training language models with memory augmentation
Zexuan Zhong, Tao Lei, and Danqi Chen. 2022 · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2023 · 2023
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Challenges towards the next frontier in privacy
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, et al. 2023 · 2023
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Why do nearest neighbor language models work?
Frank F Xu, Uri Alon, and Graham Neubig. 2023 · 2023
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