@inproceedings{thakkar-etal-2025-combining,
title = "Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in {LLM}s",
author = "Thakkar, Megh and
Fournier, Quentin and
Riemer, Matthew and
Chen, Pin-Yu and
Zouaq, Amal and
Das, Payel and
Chandar, Sarath",
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 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.22/",
doi = "10.18653/v1/2025.acl-short.22",
pages = "268--277",
ISBN = "979-8-89176-252-7",
abstract = "There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models are not either explicitly trained to be safe, or experience a loss in their safety abilities in the process, making them capable of generating harmful content. We observe that simple interpolation between the domain and alignment delta parameters leads to safer domain-specific models that preserve their utility. Building on this, we introduce MergeAlign, a simple, efficient, and effective model merging-based alignment method. We apply MergeAlign on Llama3 models that are experts in medicine and finance, obtaining substantial safety alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged, as well as the applicability of MergeAlign on more general code and math expert models using the Qwen-2.5 series of models. We hope our findings open new research avenues towards efficient development and deployment of safe expert LLMs."
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<abstract>There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models are not either explicitly trained to be safe, or experience a loss in their safety abilities in the process, making them capable of generating harmful content. We observe that simple interpolation between the domain and alignment delta parameters leads to safer domain-specific models that preserve their utility. Building on this, we introduce MergeAlign, a simple, efficient, and effective model merging-based alignment method. We apply MergeAlign on Llama3 models that are experts in medicine and finance, obtaining substantial safety alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged, as well as the applicability of MergeAlign on more general code and math expert models using the Qwen-2.5 series of models. We hope our findings open new research avenues towards efficient development and deployment of safe expert LLMs.</abstract>
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%0 Conference Proceedings
%T Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs
%A Thakkar, Megh
%A Fournier, Quentin
%A Riemer, Matthew
%A Chen, Pin-Yu
%A Zouaq, Amal
%A Das, Payel
%A Chandar, Sarath
%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 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F thakkar-etal-2025-combining
%X There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models are not either explicitly trained to be safe, or experience a loss in their safety abilities in the process, making them capable of generating harmful content. We observe that simple interpolation between the domain and alignment delta parameters leads to safer domain-specific models that preserve their utility. Building on this, we introduce MergeAlign, a simple, efficient, and effective model merging-based alignment method. We apply MergeAlign on Llama3 models that are experts in medicine and finance, obtaining substantial safety alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged, as well as the applicability of MergeAlign on more general code and math expert models using the Qwen-2.5 series of models. We hope our findings open new research avenues towards efficient development and deployment of safe expert LLMs.
%R 10.18653/v1/2025.acl-short.22
%U https://aclanthology.org/2025.acl-short.22/
%U https://doi.org/10.18653/v1/2025.acl-short.22
%P 268-277
Markdown (Informal)
[Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs](https://aclanthology.org/2025.acl-short.22/) (Thakkar et al., ACL 2025)
ACL