@inproceedings{li-etal-2026-llm-safety,
title = "Can {LLM} Safety Be Ensured by Constraining Parameter Regions?",
author = "Li, Zongmin and
Su, Jian and
Benamara, Farah and
Sun, Aixin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1616/",
pages = "34979--35011",
ISBN = "979-8-89176-390-6",
abstract = "Large language models (LLMs) are often assumed to contain ``safety regions'' - parameter subsets whose modification directly influences safety behaviors. We conduct a systematic evaluation of four safety region identification methods spanning different parameter granularities, from individual weights to entire Transformer layers, across four families of backbone LLMs with varying sizes. Using ten safety identification datasets, we find that the identified safety regions exhibit only low to moderate overlap, as measured by IoU. The overlap drops significantly when the safety regions are further refined using utility datasets (i.e. non-harmful queries). These results suggest that current techniques fail to reliably identify a stable, dataset-agnostic safety region."
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<abstract>Large language models (LLMs) are often assumed to contain “safety regions” - parameter subsets whose modification directly influences safety behaviors. We conduct a systematic evaluation of four safety region identification methods spanning different parameter granularities, from individual weights to entire Transformer layers, across four families of backbone LLMs with varying sizes. Using ten safety identification datasets, we find that the identified safety regions exhibit only low to moderate overlap, as measured by IoU. The overlap drops significantly when the safety regions are further refined using utility datasets (i.e. non-harmful queries). These results suggest that current techniques fail to reliably identify a stable, dataset-agnostic safety region.</abstract>
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%0 Conference Proceedings
%T Can LLM Safety Be Ensured by Constraining Parameter Regions?
%A Li, Zongmin
%A Su, Jian
%A Benamara, Farah
%A Sun, Aixin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-llm-safety
%X Large language models (LLMs) are often assumed to contain “safety regions” - parameter subsets whose modification directly influences safety behaviors. We conduct a systematic evaluation of four safety region identification methods spanning different parameter granularities, from individual weights to entire Transformer layers, across four families of backbone LLMs with varying sizes. Using ten safety identification datasets, we find that the identified safety regions exhibit only low to moderate overlap, as measured by IoU. The overlap drops significantly when the safety regions are further refined using utility datasets (i.e. non-harmful queries). These results suggest that current techniques fail to reliably identify a stable, dataset-agnostic safety region.
%U https://aclanthology.org/2026.acl-long.1616/
%P 34979-35011
Markdown (Informal)
[Can LLM Safety Be Ensured by Constraining Parameter Regions?](https://aclanthology.org/2026.acl-long.1616/) (Li et al., ACL 2026)
ACL
- Zongmin Li, Jian Su, Farah Benamara, and Aixin Sun. 2026. Can LLM Safety Be Ensured by Constraining Parameter Regions?. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34979–35011, San Diego, California, United States. Association for Computational Linguistics.