@inproceedings{liang-etal-2025-yes,
title = "``Yes, My {L}o{RD}.'' Guiding Language Model Extraction with Locality Reinforced Distillation",
author = "Liang, Zi and
Ye, Qingqing and
Wang, Yanyun and
Zhang, Sen and
Xiao, Yaxin and
Li, RongHua and
Xu, Jianliang and
Hu, Haibo",
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.73/",
doi = "10.18653/v1/2025.acl-long.73",
pages = "1441--1465",
ISBN = "979-8-89176-251-0",
abstract = "Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks (DNNs). They neglect the underlying inconsistency between the training tasks of MEA and LLM alignment, leading to suboptimal attack performance. To tackle this issue, we propose Locality Reinforced Distillation (LoRD), a novel model extraction algorithm specifically designed for LLMs. In particular, LoRD employs a newly defined policy-gradient-style training task that utilizes the responses of victim model as the signal to guide the crafting of preference for the local model. Theoretical analyses demonstrate that I) The convergence procedure of LoRD in model extraction is consistent with the alignment procedure of LLMs, and II) LoRD can reduce query complexity while mitigating watermark protection through our exploration-based stealing. Extensive experiments validate the superiority of our method in extracting various state-of-the-art commercial LLMs. Our code is available at: https://github.com/liangzid/LoRD-MEA."
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<abstract>Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks (DNNs). They neglect the underlying inconsistency between the training tasks of MEA and LLM alignment, leading to suboptimal attack performance. To tackle this issue, we propose Locality Reinforced Distillation (LoRD), a novel model extraction algorithm specifically designed for LLMs. In particular, LoRD employs a newly defined policy-gradient-style training task that utilizes the responses of victim model as the signal to guide the crafting of preference for the local model. Theoretical analyses demonstrate that I) The convergence procedure of LoRD in model extraction is consistent with the alignment procedure of LLMs, and II) LoRD can reduce query complexity while mitigating watermark protection through our exploration-based stealing. Extensive experiments validate the superiority of our method in extracting various state-of-the-art commercial LLMs. Our code is available at: https://github.com/liangzid/LoRD-MEA.</abstract>
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%0 Conference Proceedings
%T “Yes, My LoRD.” Guiding Language Model Extraction with Locality Reinforced Distillation
%A Liang, Zi
%A Ye, Qingqing
%A Wang, Yanyun
%A Zhang, Sen
%A Xiao, Yaxin
%A Li, RongHua
%A Xu, Jianliang
%A Hu, Haibo
%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 liang-etal-2025-yes
%X Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks (DNNs). They neglect the underlying inconsistency between the training tasks of MEA and LLM alignment, leading to suboptimal attack performance. To tackle this issue, we propose Locality Reinforced Distillation (LoRD), a novel model extraction algorithm specifically designed for LLMs. In particular, LoRD employs a newly defined policy-gradient-style training task that utilizes the responses of victim model as the signal to guide the crafting of preference for the local model. Theoretical analyses demonstrate that I) The convergence procedure of LoRD in model extraction is consistent with the alignment procedure of LLMs, and II) LoRD can reduce query complexity while mitigating watermark protection through our exploration-based stealing. Extensive experiments validate the superiority of our method in extracting various state-of-the-art commercial LLMs. Our code is available at: https://github.com/liangzid/LoRD-MEA.
%R 10.18653/v1/2025.acl-long.73
%U https://aclanthology.org/2025.acl-long.73/
%U https://doi.org/10.18653/v1/2025.acl-long.73
%P 1441-1465
Markdown (Informal)
[“Yes, My LoRD.” Guiding Language Model Extraction with Locality Reinforced Distillation](https://aclanthology.org/2025.acl-long.73/) (Liang et al., ACL 2025)
ACL
- Zi Liang, Qingqing Ye, Yanyun Wang, Sen Zhang, Yaxin Xiao, RongHua Li, Jianliang Xu, and Haibo Hu. 2025. “Yes, My LoRD.” Guiding Language Model Extraction with Locality Reinforced Distillation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1441–1465, Vienna, Austria. Association for Computational Linguistics.