Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models

Dohyun Lee, Daniel Rim, Minseok Choi, Jaegul Choo


Anthology ID:
2024.findings-acl.936
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15820–15839
Language:
URL:
https://aclanthology.org/2024.findings-acl.936/
DOI:
10.18653/v1/2024.findings-acl.936
Bibkey:
Cite (ACL):
Dohyun Lee, Daniel Rim, Minseok Choi, and Jaegul Choo. 2024. Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 15820–15839, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models (Lee et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-acl.936.pdf