Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability to unintentionally generate copyrighted content has already prompted several prominent lawsuits.
Orthogonal gradient descent for continual learning, 2019
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 1910
Earlier work this paper cites.
On the Mathematical Foundations of Theoretical Statistics
Ronald A Fisher · 1922
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, et al · 2017
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
Earlier work this paper cites.
The european union general data protection regulation: what it is and what it means
Chris Jay Hoofnagle, Bart Van Der Sloot, and Frederik Zuiderveen Borgesius · 2019
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Earlier work this paper cites.
Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Earlier work this paper cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, et al · 2021
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, et al · 2021
Earlier work this paper cites.
Memorization vs. generalization: Quantifying data leakage in nlp performance evaluation
Aparna Elangovan, Jiayuan He, and Karin Verspoor · 2021
Earlier work this paper cites.
Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
Earlier work this paper cites.
Gradient projection memory for continual learning, 2021
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
Earlier work this paper cites.
Remember what you want to forget: Algorithms for machine unlearning
Ashwath Sekhari, Jayadev Acharya, Gautam Kamath, et al · 2021
Earlier work this paper cites.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
Earlier work this paper cites.
Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Lajanugen Logeswaran, and Minjoon Seo · 2022
Earlier work this paper cites.
Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chris Callison-Burch, and Nicholas Carlini · 2022
Earlier work this paper cites.
Memory-assisted prompt editing to improve gpt-3 after deployment
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang · 2022
Earlier work this paper cites.
Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
Earlier work this paper cites.
A survey of machine unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
Earlier work this paper cites.
Sarah silverman sues meta and openai
Abigail Adams · 2023
Earlier work this paper cites.
Emergent and predictable memorization in large language models
Stella Biderman, USVSN Sai Prashanth, Hailey Schoelkopf, et al · 2023
Cited alongside, same era.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang · 2023
Cited alongside, same era.
Speak, memory: An archaeology of books known to chatgpt/gpt-4
Kent Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman · 2023
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
Cited alongside, same era.
Safe: Machine unlearning with shard graphs
Yonatan Dukler, Benjamin Bowman, Alessandro Achille, Aditya Golatkar, Ashwin Swaminathan, and Stefano Soatto · 2023
Cited alongside, same era.
Composing parameter-efficient modules with arithmetic operation
Jinghan Zhang, Junteng Liu, Junxian He, et al · 2023
Later among the works it cites.
Tong Chen, Akari Asai, Niloofar Mireshghallah, Sewon Min, James Grimmelmann, Yejin Choi, Hannaneh Hajishirzi, Luke Zettlemoyer, and Pang Wei Koh · 2024
Later among the works it cites.
Challenging forgets: Unveiling the worst-case forget sets in machine unlearning
Chongyu Fan, Jiancheng Liu, Alfred Hero, and Sijia Liu · 2024
Later among the works it cites.
Cpr: Retrieval augmented generation for copyright protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, and Stefano Soatto · 2024
Later among the works it cites.
Not all similarities are created equal: Leveraging data-driven biases to inform genai copyright disputes, 2024
Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman, Niva Elkin-Koren, Roi Livni, and Amit H Bermano · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The chatbot and the canon: Poetry memorization in llms
Lyra D’Souza and David Mimno · 2023
Cited alongside, same era.
Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun · 2023
Cited alongside, same era.
Foundation models and fair use
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A Lemley, and Percy Liang · 2023
Cited alongside, same era.
Preventing generation of verbatim memorization in language models gives a false sense of privacy
Daphne Ippolito, Florian Tramer, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher Choquette Choo, and Nicholas Carlini · 2023
Cited alongside, same era.
Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard · 2023
Cited alongside, same era.
Propile: Probing privacy leakage in large language models
Siwon Kim, Sangdoo Yun, Hwaran Lee, et al · 2023
Cited alongside, same era.
Prominent authors sue openai over chatbot technology
Sapna Maheshwari and Marc Tracy · 2023
Cited alongside, same era.
Later among the works it cites.
Be like a goldfish, don’t memorize! mitigating memorization in generative llms
Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, et al · 2024
Later among the works it cites.
Lora+: Efficient low rank adaptation of large models, 2024
Soufiane Hayou, Nikhil Ghosh, and Bin Yu · 2024
Later among the works it cites.
Trustagent: Towards safe and trustworthy llm-based agents through agent constitution
Wenyue Hua, Xianjun Yang, Mingyu Jin, Zelong Li, Wei Cheng, Ruixiang Tang, and Yongfeng Zhang · 2024
Later among the works it cites.
Demystifying verbatim memorization in large language models
Jing Huang, Diyi Yang, and Christopher Potts · 2024
Later among the works it cites.
Soul: Unlocking the power of second-order optimization for llm unlearning
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu · 2024
Later among the works it cites.
Digger: Detecting copyright content mis-usage in large language model training
Haodong Li, Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, Yang Liu, Guoai Xu, Guosheng Xu, and Haoyu Wang · 2024
Later among the works it cites.
Tofu: A task of fictitious unlearning for llms
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter · 2024
Later among the works it cites.
Llms and memorization: On quality and specificity of copyright compliance
Felix B Mueller, Rebekka Görge, Anna K Bernzen, Janna C Pirk, and Maximilian Poretschkin · 2024
Later among the works it cites.
Unlearnable algorithms for in-context learning
Andrei Muresanu, Anvith Thudi, Michael R Zhang, and Nicolas Papernot · 2024
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model, 2024
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2024
Later among the works it cites.
Rethinking llm memorization through the lens of adversarial compression, 2024
Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, and J. Zico Kolter · 2024
Later among the works it cites.
Guardrail baselines for unlearning in llms
Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith · 2024
Later among the works it cites.
Evaluating copyright takedown methods for language models
Baolin Wei, Weijia Shi, Yaru Huang, Noah A. Smith, Chunting Zhang, Luke Zettlemoyer, Kai Li, and Peter Henderson · 2024
Later among the works it cites.
Negative preference optimization: From catastrophic collapse to effective unlearning, 2024
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei · 2024
Later among the works it cites.
Avoiding copyright infringement via large language model unlearning, 2025
Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, and Eric Wong · 2025
Closest in time.
On the trustworthiness of generative foundation models: Guideline, assessment, and perspective
Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, et al · 2025
Closest in time.
Rethinking machine unlearning for large language models
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, et al · 2025
Closest in time.