@inproceedings{zhang-etal-2025-parameter,
title = "Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace",
author = "Zhang, Jia-Chen and
Xiong, Yu-Jie and
Xia, Chun-Ming and
Zhu, Dong-Hai and
Qiu, Xi-He",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.265/",
pages = "3924--3935",
abstract = "This paper proposes a novel parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-tuning by 8 times . Experimental results demonstrate that our method achieves superior training efficiency and performance compared to existing models."
}
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%0 Conference Proceedings
%T Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace
%A Zhang, Jia-Chen
%A Xiong, Yu-Jie
%A Xia, Chun-Ming
%A Zhu, Dong-Hai
%A Qiu, Xi-He
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-etal-2025-parameter
%X This paper proposes a novel parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-tuning by 8 times . Experimental results demonstrate that our method achieves superior training efficiency and performance compared to existing models.
%U https://aclanthology.org/2025.coling-main.265/
%P 3924-3935
Markdown (Informal)
[Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace](https://aclanthology.org/2025.coling-main.265/) (Zhang et al., COLING 2025)
ACL