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    <abstract>Large Language Models (LLMs) have transformed natural language processing, yet their substantial model sizes often demand significant computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need expert models tailored to specific downstream scenarios. However, current pruning methods primarily focus on maintaining models’ general capabilities, either requiring extensive post-training or performing poorly due to coarse-grained pruning. In this work, we design a \underlineCustom \underlinePruning method (Cus-Prun) to prune a large general model into a smaller lightweight expert model, which is positioned along the “language”, “domain” and “task” dimensions. By identifying and pruning irrelevant neurons of each dimension, Cus-Prun creates expert models without any post-training. Our experiments demonstrate that Cus-Prun consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.</abstract>
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%0 Conference Proceedings
%T Pruning General Large Language Models into Customized Expert Models
%A Zhao, Yiran
%A Chen, Guizhen
%A Kawaguchi, Kenji
%A Bing, Lidong
%A Zhang, Wenxuan
%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 zhao-etal-2025-pruning
%X Large Language Models (LLMs) have transformed natural language processing, yet their substantial model sizes often demand significant computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need expert models tailored to specific downstream scenarios. However, current pruning methods primarily focus on maintaining models’ general capabilities, either requiring extensive post-training or performing poorly due to coarse-grained pruning. In this work, we design a \underlineCustom \underlinePruning method (Cus-Prun) to prune a large general model into a smaller lightweight expert model, which is positioned along the “language”, “domain” and “task” dimensions. By identifying and pruning irrelevant neurons of each dimension, Cus-Prun creates expert models without any post-training. Our experiments demonstrate that Cus-Prun consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.
%R 10.18653/v1/2025.findings-acl.1201
%U https://aclanthology.org/2025.findings-acl.1201/
%U https://doi.org/10.18653/v1/2025.findings-acl.1201
%P 23377-23391

Markdown (Informal)

[Pruning General Large Language Models into Customized Expert Models](https://aclanthology.org/2025.findings-acl.1201/) (Zhao et al., Findings 2025)

• Pruning General Large Language Models into Customized Expert Models (Zhao et al., Findings 2025)

ACL

• Yiran Zhao, Guizhen Chen, Kenji Kawaguchi, Lidong Bing, and Wenxuan Zhang. 2025. Pruning General Large Language Models into Customized Expert Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23377–23391, Vienna, Austria. Association for Computational Linguistics.