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Large Language Models (LLMs) have demonstrated strong capabilities in memorizing vast amounts of knowledge across diverse domains.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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General data protection regulation (gdpr)
European Union · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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news-please - a generic news crawler and extractor
Felix Hamborg, Norman Meuschke, Corinna Breitinger, and Bela Gipp · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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On the measure of intelligence
François Chollet · 2019
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Machine unlearning, 2020
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 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
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Quantifying the privacy risks of learning high-dimensional graphical models
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2021
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
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Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2022
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Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone · 2022
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Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
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Memorization without overfitting: Analyzing the training dynamics of large language models
Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, and Armen Aghajanyan · 2022
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2023
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The times sues openai and microsoft over ai use of copyrighted work
Michael M Grynbaum and Ryan Mac · 2023
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Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
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Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard · 2023
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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
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Yucheng Li, Frank Geurin, and Chenghua Lin · 2023
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Trustworthy llms: a survey and guideline for evaluating large language models’ alignment
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
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Large language model unlearning via embedding-corrupted prompts
Chris Liu, Yaxuan Wang, Jeffrey Flanigan, and Yang Liu · 2024
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Automatic dataset construction (adc): Sample collection, data curation, and beyond
Minghao Liu, Zonglin Di, Jiaheng Wei, Zhongruo Wang, Hengxiang Zhang, Ruixuan Xiao, Haoyu Wang, Jinlong Pang, Hao Chen, Ankit Shah, et al · 2024
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On the generalization ability of machine-generated text detectors
Yule Liu, Zhiyuan Zhong, Yifan Liao, Zhen Sun, Jingyi Zheng, Jiaheng Wei, Qingyuan Gong, Fenghua Tong, Yang Chen, Yang Zhang, et al · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li · 2023
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Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, and Yejin Choi · 2023
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In-context unlearning: Language models as few shot unlearners
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Harry Potter and the sorcerer’s stone
Joanne K Rowling · 2023
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer · 2023
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Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al · 2023
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Beyond memorization: Violating privacy via inference with large language models
Robin Staab, Mark Vero, Mislav Balunović, and Martin Vechev · 2023
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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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Llm dataset inference: Did you train on my dataset?
Pratyush Maini, Hengrui Jia, Nicolas Papernot, and Adam Dziedzic · 2024
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Alternate preference optimization for unlearning factual knowledge in large language models
Anmol Mekala, Vineeth Dorna, Shreya Dubey, Abhishek Lalwani, David Koleczek, Mukund Rungta, Sadid Hasan, and Elita Lobo · 2024
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Unlearnable algorithms for in-context learning
Andrei Muresanu, Anvith Thudi, Michael R Zhang, and Nicolas Papernot · 2024
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Muse: Machine unlearning six-way evaluation for language models
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A Smith, and Chiyuan Zhang · 2024
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The falcon 3 family of open models, December 2024
TII Team · 2024
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Guardrail baselines for unlearning in llms
Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith · 2024
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Llm unlearning via loss adjustment with only forget data
Yaxuan Wang, Jiaheng Wei, Chris Yuhao Liu, Jinlong Pang, Quan Liu, Ankit Parag Shah, Yujia Bao, Yang Liu, and Wei Wei · 2024
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Measuring and reducing llm hallucination without gold-standard answers
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, and Yang Liu · 2024
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Machine unlearning for traditional models and large language models: A short survey
Yi Xu · 2024
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An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
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Machine unlearning of pre-trained large language models
Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, and Xiang Yue · 2024
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Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2024
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Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei · 2024
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Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models
Yihua Zhang, Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jiancheng Liu, Xiaoming Liu, and Sijia Liu · 2024
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To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images… for now
Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, and Sijia Liu · 2024
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Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, and Xuming Hu · 2024
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Uoe: Unlearning one expert is enough for mixture-of-experts llms
Haomin Zhuang, Yihua Zhang, Kehan Guo, Jinghan Jia, Gaowen Liu, Sijia Liu, and Xiangliang Zhang · 2024
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Open problems in machine unlearning for ai safety
Fazl Barez, Tingchen Fu, Ameya Prabhu, Stephen Casper, Amartya Sanyal, Adel Bibi, Aidan O’Gara, Robert Kirk, Ben Bucknall, Tim Fist, et al · 2025
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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, and Xuming Hu · 2025
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Security and privacy challenges of large language models: A survey
Badhan Chandra Das, M Hadi Amini, and Yanzhao Wu · 2025
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Chongyu Fan, Jinghan Jia, Yihua Zhang, Anil Ramakrishna, Mingyi Hong, and Sijia Liu · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Jiahao Huo, Yibo Yan, Xu Zheng, Yuanhuiyi Lyu, Xin Zou, Zhihua Wei, and Xuming Hu · 2025
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Martin Kuo, Jingyang Zhang, Jianyi Zhang, Minxue Tang, Louis DiValentin, Aolin Ding, Jingwei Sun, William Chen, Amin Hass, Tianlong Chen, et al · 2025
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Rethinking machine unlearning for large language models
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Token cleaning: Fine-grained data selection for llm supervised fine-tuning
Jinlong Pang, Na Di, Zhaowei Zhu, Jiaheng Wei, Hao Cheng, Chen Qian, and Yang Liu · 2025
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Improving data efficiency via curating LLM-driven rating systems
Jinlong Pang, Jiaheng Wei, Ankit Shah, Zhaowei Zhu, Yaxuan Wang, Chen Qian, Yang Liu, Yujia Bao, and Wei Wei · 2025
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Lume: Llm unlearning with multitask evaluations
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, and Rahul Gupta · 2025
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Yibo Yan, Shen Wang, Jiahao Huo, Philip S Yu, Xuming Hu, and Qingsong Wen · 2025
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Towards robust knowledge unlearning: An adversarial framework for assessing and improving unlearning robustness in large language models
Hongbang Yuan, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao · 2025
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