Fetching the paper…
Reading the bibliography…
In this paper, we investigate knowledge forgetting in large language models with a focus on its generalisation, ensuring that models forget not only specific training samples but also related implicit knowledge.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, May 17-21, 2015 . IEEE Computer Society, 2015, pp. 463–480
2015
Earlier work this paper cites.
O. Levy, M. Seo, E. Choi, and L. Zettlemoyer, “Zero-shot relation extraction via reading comprehension,” in Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, August 3-4, 2017 , R. Levy and L. Specia, Eds. Association for Computational Linguistics, 2017, pp. 333–342
2017
Earlier work this paper cites.
Y. Zhang, M. Galley, J. Gao, Z. Gan, X. Li, C. Brockett, and B. Dolan, “Generating informative and diverse conversational responses via adversarial information maximization,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada , S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., 2018, pp. 1815–1825
2018
Earlier work this paper cites.
A. Ginart, M. Y. Guan, G. Valiant, and J. Zou, “Making AI forget you: Data deletion in machine learning,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 3513–3526
2019
Earlier work this paper cites.
C. Zhao, P. T. Fletcher, M. Yu, Y. Peng, G. Zhang, and C. Shen, “The adversarial attack and detection under the fisher information metric,” in AAAI2019, IAAI 2019, EAAI 2019 . AAAI Press, 2019, pp. 5869–5876
2019
Earlier work this paper cites.
K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in GPT,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., 2022
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,” in Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., 2022
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
OpenAI, “GPT-4 technical report,” CoRR , vol. abs/2303.08774, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
X. Wu, J. Li, M. Xu, W. Dong, S. Wu, C. Bian, and D. Xiong, “DEPN: detecting and editing privacy neurons in pretrained language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 2875–2886
2023
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,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , A. Rogers, J. L. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, 2023, pp. 14 389–14 408
2023
Earlier work this paper cites.
G. Ilharco, M. T. Ribeiro, M. Wortsman, L. Schmidt, H. Hajishirzi, and A. Farhadi, “Editing models with task arithmetic,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
Earlier work this paper cites.
J. Chen and D. Yang, “Unlearn what you want to forget: Efficient unlearning for llms,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 12 041–12 052
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
Y. Yao, P. Wang, B. Tian, S. Cheng, Z. Li, S. Deng, H. Chen, and N. Zhang, “Editing large language models: Problems, methods, and opportunities,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 10 222–10 240
2023
Cited alongside, same era.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” in Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., 2023
2023
Cited alongside, same era.
V. Patil, P. Hase, and M. Bansal, “Can sensitive information be deleted from llms? objectives for defending against extraction attacks,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
X. Hu, D. Li, B. Hu, Z. Zheng, Z. Liu, and M. Zhang, “Separate the wheat from the chaff: Model deficiency unlearning via parameter-efficient module operation,” in Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada , M. J. Wooldridge, J. G. Dy, and S. Natarajan, Eds. AAAI Press, 2024, pp. 18 252–18 260
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Wen, Z. Li, W. Du, and L. Mou, “f-divergence minimization for sequence-level knowledge distillation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , A. Rogers, J. L. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, 2023, pp. 10 817–10 834
2023
Cited alongside, same era.
M. Geva, J. Bastings, K. Filippova, and A. Globerson, “Dissecting recall of factual associations in auto-regressive language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 12 216–12 235
2023
Cited alongside, same era.
——, “Dissecting recall of factual associations in auto-regressive language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 12 216–12 235. [Online]. Available: https://doi.org/10.18653/v1/2023.emnlp-main.751
2023
Cited alongside, same era.
M. Pawelczyk, S. Neel, and H. Lakkaraju, “In-context unlearning: Language models as few-shot unlearners,” in Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 . OpenReview.net, 2024
2024
Cited alongside, same era.
C. Y. Liu, Y. Wang, J. Flanigan, and Y. Liu, “Large language model unlearning via embedding-corrupted prompts,” in Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 , A. Globersons, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. M. Tomczak, and C. Zhang, Eds., 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Jin, P. Cao, C. Wang, Z. He, H. Yuan, J. Li, Y. Chen, K. Liu, and J. Zhao, “RWKU: benchmarking real-world knowledge unlearning for large language models,” in Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 , 2024. [Online]. Available: http://papers.nips.cc/paper\_files/paper/2024/hash/b1f78dfc9ca0156498241012aec4efa0-Abstract-Datasets\_and\_Benchmarks\_Track.html
2024
Later among the works it cites.
V. Patil, P. Hase, and M. Bansal, “Can sensitive information be deleted from llms? objectives for defending against extraction attacks,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Yao, E. Chien, M. Du, X. Niu, T. Wang, Z. Cheng, and X. Yue, “Machine unlearning of pre-trained large language models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 8403–8419
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
M. Sclar, Y. Choi, Y. Tsvetkov, and A. Suhr, “Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting,” in The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024
2024
Later among the works it cites.
H. Wang, H. Sun, J. Wang, Q. Qi, Z. Xia, M. Zhang, and J. Liao, “SSS: editing factual knowledge in language models towards semantic sparse space,” in Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 5559–5570
2024
Later among the works it cites.
2024
Later among the works it cites.