@inproceedings{wang-etal-2024-taylor,
title = "{T}aylor Unswift: Secured Weight Release for Large Language Models via {T}aylor Expansion",
author = "Wang, Guanchu and
Chuang, Yu-Neng and
Tang, Ruixiang and
Zhong, Shaochen and
Yuan, Jiayi and
Jin, Hongye and
Liu, Zirui and
Chaudhary, Vipin and
Xu, Shuai and
Caverlee, James and
Hu, Xia",
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.393",
doi = "10.18653/v1/2024.emnlp-main.393",
pages = "6928--6941",
abstract = "Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.",
}
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<abstract>Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.</abstract>
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%0 Conference Proceedings
%T Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
%A Wang, Guanchu
%A Chuang, Yu-Neng
%A Tang, Ruixiang
%A Zhong, Shaochen
%A Yuan, Jiayi
%A Jin, Hongye
%A Liu, Zirui
%A Chaudhary, Vipin
%A Xu, Shuai
%A Caverlee, James
%A Hu, Xia
%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 wang-etal-2024-taylor
%X Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.
%R 10.18653/v1/2024.emnlp-main.393
%U https://aclanthology.org/2024.emnlp-main.393
%U https://doi.org/10.18653/v1/2024.emnlp-main.393
%P 6928-6941
Markdown (Informal)
[Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion](https://aclanthology.org/2024.emnlp-main.393) (Wang et al., EMNLP 2024)
ACL
- Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, and Xia Hu. 2024. Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6928–6941, Miami, Florida, USA. Association for Computational Linguistics.