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"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyright, safety, and more.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Y. Hannun, and Laurens van der Maaten · 1911
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Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 1912
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741 F.2d 896 (7th Cir. 1984)
Selle v. Gibb · 1984
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17 U.S. Code § 102 - Subject matter of copyright: In general, December 1990
Copyright Law of the United States · 1990
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Toward a Fair Use Standard
Pierre N. Leval · 1990
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499 U.S. 340 (1991)
Feist Publications v. Rural Telephone Service Company · 1991
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17 U.S. Code § 107 - Limitations on exclusive rights: Fair use, October 1992
Copyright Law of the United States · 1992
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510 U.S. 569 (1994)
Campbell v. Acuff-Rose Music, Inc · 1994
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Incremental and Decremental Support Vector Machine Learning
Gert Cauwenberghs and Tomaso Poggio · 2001
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Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith · 2002
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The Fallacy of the Almost-General-Purpose Computer, October 2002
Ed Felten · 2002
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Learn to Forget: Machine Unlearning via Neuron Masking, 2021
Yang Liu, Zhuo Ma, Ximeng Liu, Jian Liu, Zhongyuan Jiang, Jianfeng Ma, Philip Yu, and Kui Ren · 2003
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17 U.S. Code § 506 - Criminal offenses, October 2008
Copyright Law of the United States · 2008
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The Future of the Internet–And How to Stop It
Jonathan Zittrain · 2008
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581 F.3d 1138, 1143 (9th Cir. 2009)
Art Attacks Ink, LLC v. MGA Ent. Inc · 2009
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Big Data’s Disparate Impact, 2014
Solon Barocas and Andrew D. Selbst · 2014
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804 F.3d 202 (2d Cir. 2015)
Authors Guild v. Google, Inc · 2015
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Towards Making Systems Forget with Machine Unlearning
Yinzhi Cao and Junfeng Yang · 2015
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An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks, 2015
Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2015
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Concrete Problems in AI Safety, 2016
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Copyright for Literate Robots
James Grimmelmann · 2016
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Regulation (EU) 2016/679 (General Data Protection Regulation), April 2016
Official Journal of the European Union · 2016
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Facebook launching tools to tackle revenge porn
Alex Hern · 2017
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A theory of formal synthesis via inductive learning
Susmit Jha and Sanjit A Seshia · 2017
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Identifying and Controlling Important Neurons in Neural Machine Translation, 2018
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass · 2018
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The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, 2018
Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, Hyrum Anderson, Heather Roff, Gregory C. Allen, Jacob Steinhardt, Carrick Flynn, Seán Ó hÉigeartaigh, Simon Beard, Haydn Belfield, Sebastian Farquhar, Clare Lyle, Rebecca Crootof, Owain Evans, Michael Page, Joanna Bryson, Roman Yampolskiy, and Dario Amodei · 2018
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California Consumer Privacy Act of 2018 (CCPA), 2018
California State Legislature · 2018
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883 F.3d 1111 (9th Cir. 2018)
Rentmeester v. Nike, Inc · 2018
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Judgment in Case C-507/17 Google LLC, successor in law to Google Inc. v Commission nationale de l’informatique et des libertés (CNIL), September 2019
Court of Justice of the European Union · 2019
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Making AI forget you: data deletion in machine learning
Antonio A. Ginart, Melody Y. Guan, Gregory Valiant, and James Zou · 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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Atomic: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi · 2019
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The Right to Be Forgotten in the Digital Age: The Challenges of Data Protection Beyond Borders
Federico Fabbrini and Edoardo Celeste · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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Descent-to-Delete: Gradient-Based Methods for Machine Unlearning, 2020
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2020
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Towards Probabilistic Verification of Machine Unlearning, 2020
David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal · 2020
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Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems
A. Feder Cooper, Karen Levy, and Christopher De Sa · 2021
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Transformer Feed-Forward Layers Are Key-Value Memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy · 2021
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Fair Learning
Mark Lemley and Bryan Casey · 2021
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Certifiable Machine Unlearning for Linear Models, 2021
Ananth Mahadevan and Michael Mathioudakis · 2021
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Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models, 2021
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli · 2021
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Virginia consumer data protection act of 2021 (vcdpa), 2021
Virginia State Legislature · 2021
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What Does it Mean for a Language Model to Preserve Privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, and Florian Tramèr · 2022
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Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles
A. Feder Cooper and Karen Levy · 2022
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Non-Determinism and the Lawlessness of Machine Learning Code
A. Feder Cooper, Jonathan Frankle, and Christopher De Sa · 2022
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Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
A. Feder Cooper, Emanuel Moss, Benjamin Laufer, and Helen Nissenbaum · 2022
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Knowledge Neurons in Pretrained Transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2022
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Unsolved Problems in ML Safety, 2022
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2022
Cited alongside, same era.
Knowledge Unlearning for Mitigating Privacy Risks in Language Models, 2022
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2022
Cited alongside, same era.
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining
Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li · 2022
Cited alongside, same era.
Quark: Controllable text generation with reinforced unlearning, 2022
Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi · 2022
Cited alongside, same era.
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang · 2024
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Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act), 2024
European Union · 2024
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Red-Teaming for Generative AI: Silver Bullet or Security Theater?, 2024
Michael Feffer, Anusha Sinha, Wesley Hanwen Deng, Zachary C. Lipton, and Hoda Heidari · 2024
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Issue brief: Components of frontier ai safety frameworks, 2024
Frontier Model Forum · 2024
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Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
Cited alongside, same era.
Memory-Based Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
Cited alongside, same era.
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models, 2022
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2022
Cited alongside, same era.
AlphaFold predictions are valuable hypotheses, and accelerate but do not replace experimental structure determination
Thomas C. Terwilliger, Dorothee Liebschner, Tristan I. Croll, Christopher J. Williams, Airlie J. McCoy, Billy K. Poon, Pavel V. Afonine, Robert D. Oeffner, Jane S. Richardson, Randy J. Read, and Paul D. Adams · 2022
Cited alongside, same era.
Emergent Abilities of Large Language Models, 2022
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
Cited alongside, same era.
Arcane: An efficient architecture for exact machine unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022
Cited alongside, same era.
AI Model Disgorgement: Methods and Choices, 2023
Alessandro Achille, Michael Kearns, Carson Klingenberg, and Stefano Soatto · 2023
Cited alongside, same era.
What can we learn from Data Leakage and Unlearning for Law?, 2023
Jaydeep Borkar · 2023
Cited alongside, same era.
David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, and Nicolas Papernot · 2024
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Corrective Machine Unlearning, 2024
Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, and Amartya Sanyal · 2024
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Introduction to Mechanistic Interpretability, 2024
Sarah Hastings-Woodhouse · 2024
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Measuring memorization through probabilistic discoverable extraction
Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, and Ilia Shumailov · 2024
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Supporting Trustworthy AI Through Machine Unlearning
Emmie Hine, Claudio Novelli, Mariarosaria Taddeo, and Luciano Floridi · 2024
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Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy, 2024
Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, and Chiyuan Zhang · 2024
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SoK: Challenges and Opportunities in Federated Unlearning, 2024
Hyejun Jeong, Shiqing Ma, and Amir Houmansadr · 2024
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SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning, 2024
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu · 2024
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Adversaries Can Misuse Combinations of Safe Models, 2024
Erik Jones, Anca Dragan, and Jacob Steinhardt · 2024
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Position: on the societal impact of open foundation models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, and Arvind Narayanan · 2024
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Acceptable Use Policies for Foundation Models
Kevin Klyman · 2024
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How to Make AI ‘Forget’ All the Private Data It Shouldn’t Have
Rachel Layne · 2024
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The Llama 3 Herd of Models, 2024
AI Meta Llama Team · 2024
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Eight Methods to Evaluate Robust Unlearning in LLMs, 2024
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, 2024
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter · 2024
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On the standardization of behavioral use clauses and their adoption for responsible licensing of ai
Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Muñoz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, and Danish Contractor · 2024
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Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, and Yejin Choi · 2024
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AI’s Next Challenge: how to forget
Christine Mui · 2024
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White House Announces New Private Sector Voluntary Commitments to Combat Image-Based Sexual Abuse, September 2024
Office of Science and Technology Policy · 2024
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In-Context Unlearning: Language Models as Few Shot Unlearners, 2024
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2024
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Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon, 2024
USVSN Sai Prashanth, Alvin Deng, Kyle O’Brien, Jyothir S V au2, Mohammad Aflah Khan, Jaydeep Borkar, Christopher A. Choquette-Choo, Jacob Ray Fuehne, Stella Biderman, Tracy Ke, Katherine Lee, and Naomi Saphra · 2024
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How to Think About Remedies in the Generative AI Copyright Cases
Pamela Samuelson · 2024
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Machine forgetting: How difficult it is to get AI to forget
Alison Snyder · 2024
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The Great Scrape: The Clash Between Scraping and Privacy, 2024
Daniel J. Solove and Woodrow Hartzog · 2024
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Tamper-Resistant Safeguards for Open-Weight LLMs, 2024
Rishub Tamirisa, Bhrugu Bharathi, Long Phan, Andy Zhou, Alice Gatti, Tarun Suresh, Maxwell Lin, Justin Wang, Rowan Wang, Ron Arel, Andy Zou, Dawn Song, Bo Li, Dan Hendrycks, and Mantas Mazeika · 2024
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Guardrail baselines for unlearning in llms, 2024
Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith · 2024
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Department of Commerce Announces New Guidance, Tools 270 Days Following President Biden’s Executive Order on AI, July 2024
U.S. Department of Commerce · 2024
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Machine ‘Unlearning’ Helps Generative AI ‘Forget’ Copyright-Protected and Violent Content, 2024
UT News · 2024
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Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nicholas Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alexandra Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, and Abigail Z. Jacobs · 2024
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Evaluating Copyright Takedown Methods for Language Models, 2024
Boyi Wei, Weijia Shi, Yangsibo Huang, Noah A. Smith, Chiyuan Zhang, Luke Zettlemoyer, Kai Li, and Peter Henderson · 2024
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Making AI Models Forget Undesirable Data Hurts Their Performance
Kyle Wiggers · 2024
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An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping, July 2024
Boming Xia, Qinghua Lu, Liming Zhu, and Zhenchang Xing · 2024
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Large language model unlearning, 2024
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2024
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Reinforcement Unlearning, 2024
Dayong Ye, Tianqing Zhu, Congcong Zhu, Derui Wang, Zewei Shi, Sheng Shen, Wanlei Zhou, and Minhui Xue · 2024
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AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies, 2024
Yi Zeng, Kevin Klyman, Andy Zhou, Yu Yang, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, and Bo Li · 2024
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An Adversarial Perspective on Machine Unlearning for AI Safety, 2024
Jakub Łucki, Boyi Wei, Yangsibo Huang, Peter Henderson, Florian Tramèr, and Javier Rando · 2024
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Towards Source-Free Machine Unlearning, 2025
Sk Miraj Ahmed, Umit Yigit Basaran, Dripta S. Raychaudhuri, Arindam Dutta, Rohit Kundu, Fahim Faisal Niloy, Basak Guler, and Amit K. Roy-Chowdhury · 2025
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Extracting memorized pieces of (copyrighted) books from open-weight language models
A. Feder Cooper, Aaron Gokaslan, Amy B. Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, and Percy Liang · 2025
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Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, George Kaissis, Katherine Lee, Milad Nasr, Sahra Ghalebikesabi, Niloofar Mireshghallah, Meenatchi Sundaram Mutu Selva Annamalai, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Franziska Boenisch, Adam Dziedzic, and A. Feder Cooper · 2025
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The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, John Kirchenbauer, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, and Tyler Murray · 2025
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Common elements of frontier ai safety policies, 2025
METR · 2025
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The Deletion Remedy
Daniel Wilf-Townsend · 2025
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SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning, 2025
Tianyang Xu, Xiaoze Liu, Feijie Wu, Xiaoqian Wang, and Jing Gao · 2025
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