@inproceedings{jia-etal-2024-soul,
title = "{SOUL}: Unlocking the Power of Second-Order Optimization for {LLM} Unlearning",
author = "Jia, Jinghan and
Zhang, Yihua and
Zhang, Yimeng and
Liu, Jiancheng and
Runwal, Bharat and
Diffenderfer, James and
Kailkhura, Bhavya and
Liu, Sijia",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.245",
doi = "10.18653/v1/2024.emnlp-main.245",
pages = "4276--4292",
abstract = "Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning.",
}
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<abstract>Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning.</abstract>
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%0 Conference Proceedings
%T SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
%A Jia, Jinghan
%A Zhang, Yihua
%A Zhang, Yimeng
%A Liu, Jiancheng
%A Runwal, Bharat
%A Diffenderfer, James
%A Kailkhura, Bhavya
%A Liu, Sijia
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F jia-etal-2024-soul
%X Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning.
%R 10.18653/v1/2024.emnlp-main.245
%U https://aclanthology.org/2024.emnlp-main.245
%U https://doi.org/10.18653/v1/2024.emnlp-main.245
%P 4276-4292
Markdown (Informal)
[SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://aclanthology.org/2024.emnlp-main.245) (Jia et al., EMNLP 2024)
ACL
- Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu. 2024. SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4276–4292, Miami, Florida, USA. Association for Computational Linguistics.