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Large language models (LLMs) trained on web-scale datasets raise substantial concerns regarding permissible data usage.
Regulation (eu) 2016/679 of the european parliament and of the council
European Union · 2016
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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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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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The pile: An 800gb dataset of diverse text for language modeling, 2020
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2021
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Dataset inference: Ownership resolution in machine learning
Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot · 2021
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Ccpa regulations: Final regulation text
CA OAG · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A Rossi, and Raman Arora · 2021
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Adversarial attacks on gpt-4 via simple random search
Maksym Andriushchenko · 2023
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
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Quantifying memorization across neural language models, 2023
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
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Report of the 1st workshop on generative ai and law
A Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, et al · 2023
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Language modeling is compression
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, et al · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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Autodan: Automatic and interpretable adversarial attacks on large language models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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Tools for verifying neural models’ training data
Dami Choi, Yonadav Shavit, and David K Duvenaud · 2024
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Blind baselines beat membership inference attacks for foundation models
Debeshee Das, Jie Zhang, and Florian Tramèr · 2024
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Copyright fundamentals for ai researchers
Kate Downing · 2024
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Micah Goldblum, Marc Finzi, Keefer Rowan, and Andrew Gordon Wilson · 2023
Cited alongside, same era.
Preventing generation of verbatim memorization in language models gives a false sense of privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A Choquette-Choo, and Nicholas Carlini · 2023
Cited alongside, same era.
Llmlingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu · 2023
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Textbooks are all you need ii: phi-1.5
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee · 2023
Cited alongside, same era.
Grokking as compression: A nonlinear complexity perspective
Ziming Liu, Ziqian Zhong, and Max Tegmark · 2023
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Scalable extraction of training data from (production) language models
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
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In-context unlearning: Language models as few shot unlearners
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi · 2024
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Coercing llms to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, and Tom Goldstein · 2024
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Eight methods to evaluate robust unlearning in llms
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, and Dylan Hadfield-Menell · 2024
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Tofu: A task of fictitious unlearning for llms
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C Lipton, and J Zico Kolter · 2024
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Openai seeks to dismiss parts of the new york times’s lawsuit
Cade Metz and Katie Robertson · 2024
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Robust concept erasure using task vectors
Minh Pham, Kelly O Marshall, Chinmay Hegde, and Niv Cohen · 2024
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Smaz: Small strings compression library
Salvatore Sanfilippo · 2024
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Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann · 2024
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