@inproceedings{yao-etal-2025-gradot,
title = "{G}rad{OT}: Training-free Gradient-preserving Offsite-tuning for Large Language Models",
author = "Yao, Kai and
Tan, Zhaorui and
Gao, Penglei and
Li, Lichun and
Wu, Kaixin and
Wang, Yinggui and
Zhao, Yuan and
Ji, Yixin and
Zhu, Jianke and
Wang, Wei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.255/",
doi = "10.18653/v1/2025.acl-long.255",
pages = "5115--5130",
ISBN = "979-8-89176-251-0",
abstract = "The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yielding privacy risks for both model and data owners. One promising solution, called offsite-tuning (OT), is proposed to address these challenges, where a weaker emulator is compressed from the original model and further fine-tuned with adapter to enhance privacy. However, the existing OT-based methods require high computational costs and lack theoretical analysis. This paper introduces a novel OT approach based on gradient-preserving compression. By analyzing the OT problem through the lens of optimization, we propose a method that selectively applies compression techniques such as rank compression and channel pruning, preserving the gradients of fine-tuned adapters while ensuring privacy. Extensive experiments demonstrate that our approach surpasses existing OT methods, both in terms of privacy protection and model performance. Our method provides a theoretical foundation for OT and offers a practical, training-free solution for offsite-tuning of large-scale LLMs."
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%0 Conference Proceedings
%T GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
%A Yao, Kai
%A Tan, Zhaorui
%A Gao, Penglei
%A Li, Lichun
%A Wu, Kaixin
%A Wang, Yinggui
%A Zhao, Yuan
%A Ji, Yixin
%A Zhu, Jianke
%A Wang, Wei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yao-etal-2025-gradot
%X The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yielding privacy risks for both model and data owners. One promising solution, called offsite-tuning (OT), is proposed to address these challenges, where a weaker emulator is compressed from the original model and further fine-tuned with adapter to enhance privacy. However, the existing OT-based methods require high computational costs and lack theoretical analysis. This paper introduces a novel OT approach based on gradient-preserving compression. By analyzing the OT problem through the lens of optimization, we propose a method that selectively applies compression techniques such as rank compression and channel pruning, preserving the gradients of fine-tuned adapters while ensuring privacy. Extensive experiments demonstrate that our approach surpasses existing OT methods, both in terms of privacy protection and model performance. Our method provides a theoretical foundation for OT and offers a practical, training-free solution for offsite-tuning of large-scale LLMs.
%R 10.18653/v1/2025.acl-long.255
%U https://aclanthology.org/2025.acl-long.255/
%U https://doi.org/10.18653/v1/2025.acl-long.255
%P 5115-5130
Markdown (Informal)
[GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models](https://aclanthology.org/2025.acl-long.255/) (Yao et al., ACL 2025)
ACL
- Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Jianke Zhu, and Wei Wang. 2025. GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5115–5130, Vienna, Austria. Association for Computational Linguistics.