@inproceedings{chen-etal-2025-alps,
title = "{ALPS}: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models",
author = "Chen, Hao and
Li, Haoze and
Xiao, Zhiqing and
Gao, Lirong and
Zhang, Qi and
Hu, Xiaomeng and
Wang, Ningtao and
Fu, Xing and
Zhao, Junbo",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.612/",
doi = "10.18653/v1/2025.findings-acl.612",
pages = "11764--11780",
ISBN = "979-8-89176-256-5",
abstract = "Aligning general-purpose large language models (LLMs) to downstream tasks often incurs significant training adjustment costs. Prior research has explored various avenues to enhance alignment efficiency, primarily through minimal-data training or data-driven activations to identify key attention heads. However, these approaches inherently introduce data dependency, which hinders generalization and reusability. To address this issue and enhance model alignment efficiency, we propose the Attention Localization and Pruning Strategy ALPS, an efficient algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. Experimental results demonstrate that our method activates only 10{\%} of attention parameters during fine-tuning while achieving a 2{\%} performance improvement over baselines on three tasks. Moreover, the identified task-specific heads are transferable across datasets and mitigate knowledge forgetting. Our work and findings provide a novel perspective on efficient LLM alignment."
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<abstract>Aligning general-purpose large language models (LLMs) to downstream tasks often incurs significant training adjustment costs. Prior research has explored various avenues to enhance alignment efficiency, primarily through minimal-data training or data-driven activations to identify key attention heads. However, these approaches inherently introduce data dependency, which hinders generalization and reusability. To address this issue and enhance model alignment efficiency, we propose the Attention Localization and Pruning Strategy ALPS, an efficient algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. Experimental results demonstrate that our method activates only 10% of attention parameters during fine-tuning while achieving a 2% performance improvement over baselines on three tasks. Moreover, the identified task-specific heads are transferable across datasets and mitigate knowledge forgetting. Our work and findings provide a novel perspective on efficient LLM alignment.</abstract>
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%0 Conference Proceedings
%T ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models
%A Chen, Hao
%A Li, Haoze
%A Xiao, Zhiqing
%A Gao, Lirong
%A Zhang, Qi
%A Hu, Xiaomeng
%A Wang, Ningtao
%A Fu, Xing
%A Zhao, Junbo
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-alps
%X Aligning general-purpose large language models (LLMs) to downstream tasks often incurs significant training adjustment costs. Prior research has explored various avenues to enhance alignment efficiency, primarily through minimal-data training or data-driven activations to identify key attention heads. However, these approaches inherently introduce data dependency, which hinders generalization and reusability. To address this issue and enhance model alignment efficiency, we propose the Attention Localization and Pruning Strategy ALPS, an efficient algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. Experimental results demonstrate that our method activates only 10% of attention parameters during fine-tuning while achieving a 2% performance improvement over baselines on three tasks. Moreover, the identified task-specific heads are transferable across datasets and mitigate knowledge forgetting. Our work and findings provide a novel perspective on efficient LLM alignment.
%R 10.18653/v1/2025.findings-acl.612
%U https://aclanthology.org/2025.findings-acl.612/
%U https://doi.org/10.18653/v1/2025.findings-acl.612
%P 11764-11780
Markdown (Informal)
[ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models](https://aclanthology.org/2025.findings-acl.612/) (Chen et al., Findings 2025)
ACL
- Hao Chen, Haoze Li, Zhiqing Xiao, Lirong Gao, Qi Zhang, Xiaomeng Hu, Ningtao Wang, Xing Fu, and Junbo Zhao. 2025. ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11764–11780, Vienna, Austria. Association for Computational Linguistics.