The Frontier of Data Erasure: Machine Unlearning for Large Language Models

Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE symposium on security and privacy . IEEE, 2015, pp. 463–480

2015

Earlier work this paper cites.

J. Jang, D. Yoon, S. Yang, S. Cha, M. Lee, L. Logeswaran, and M. Seo, “Knowledge unlearning for mitigating privacy risks in language models,” arXiv preprint arXiv:2210.01504 , 2022

Original

2022

Earlier work this paper cites.

X. Lu, S. Welleck, J. Hessel, L. Jiang, L. Qin, P. West, P. Ammanabrolu, and Y. Choi, “Quark: Controllable text generation with reinforced unlearning,” Advances in neural information processing systems , vol. 35, pp. 27 591–27 609, 2022

2022

Earlier work this paper cites.

Z. Ma, Y. Liu, X. Liu, J. Liu, J. Ma, and K. Ren, “Learn to forget: Machine unlearning via neuron masking,” IEEE Transactions on Dependable and Secure Computing , 2022

2022

Earlier work this paper cites.

K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl et al. , “Large language models encode clinical knowledge,” Nature , vol. 620, no. 7972, pp. 172–180, 2023

2023

Earlier work this paper cites.

D. Zhang, P. Finckenberg-Broman, T. Hoang, S. Pan, Z. Xing, M. Staples, and X. Xu, “Right to be forgotten in the era of large language models: Implications, challenges, and solutions,” arXiv preprint arXiv:2307.03941 , 2023

Original

2023

Cited alongside, same era.

R. Eldan and M. Russinovich, “Who’s harry potter? approximate unlearning in llms,” arXiv preprint arXiv:2310.02238 , 2023

Original

2023

Cited alongside, same era.

A. Karamolegkou, J. Li, L. Zhou, and A. Søgaard, “Copyright violations and large language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 7403–7412

2023

Cited alongside, same era.

M. Pawelczyk, S. Neel, and H. Lakkaraju, “In-context unlearning: Language models as few shot unlearners,” arXiv preprint arXiv:2310.07579 , 2023

Original

2023

Cited alongside, same era.

J. Chen and D. Yang, “Unlearn what you want to forget: Efficient unlearning for llms,” arXiv preprint arXiv:2310.20150 , 2023

Original

2023

Later among the works it cites.

L. Wang, T. Chen, W. Yuan, X. Zeng, K.-F. Wong, and H. Yin, “Kga: A general machine unlearning framework based on knowledge gap alignment,” arXiv preprint arXiv:2305.06535 , 2023

Original

2023

Later among the works it cites.

C. Yu, S. Jeoung, A. Kasi, P. Yu, and H. Ji, “Unlearning bias in language models by partitioning gradients,” in Findings of the Association for Computational Linguistics: ACL 2023 , 2023, pp. 6032–6048

2023

Later among the works it cites.

A. Kassem, O. Mahmoud, and S. Saad, “Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 4360–4379

2023

Later among the works it cites.