@inproceedings{zhao-etal-2025-tiny,
title = "Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning",
author = "Zhao, Jinman and
Zhang, Xueyan and
Li, Jiaru and
Niu, Jingcheng and
Hu, Yulan and
Min, Erxue and
Penn, Gerald",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.321/",
pages = "6326--6344",
ISBN = "979-8-89176-332-6",
abstract = "In this work, we propose FoRA-UA, a novel method that, using only 1{--}5{\%} of the standard LoRA{'}s parameters, achieves state-of-the-art performance across a wide range of tasks. Specifically, we explore scenarios with extremely limited parameter budgets and derive two key insights: (1) fix-sized sparse frequency representations approximate small matrices more accurately; and (2) with a fixed number of trainable parameters, introducing a smaller intermediate representation to approximate larger matrices results in lower construction error. These findings form the foundation of our FoRA-UA method. By inserting a small intermediate parameter set, we achieve greater model compression without sacrificing performance. We evaluate FoRA-UA across diverse tasks, including natural language understanding (NLU), natural language generation (NLG), instruction tuning, and image classification, demonstrating strong generalisation and robustness under extreme compression."
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%0 Conference Proceedings
%T Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning
%A Zhao, Jinman
%A Zhang, Xueyan
%A Li, Jiaru
%A Niu, Jingcheng
%A Hu, Yulan
%A Min, Erxue
%A Penn, Gerald
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhao-etal-2025-tiny
%X In this work, we propose FoRA-UA, a novel method that, using only 1–5% of the standard LoRA’s parameters, achieves state-of-the-art performance across a wide range of tasks. Specifically, we explore scenarios with extremely limited parameter budgets and derive two key insights: (1) fix-sized sparse frequency representations approximate small matrices more accurately; and (2) with a fixed number of trainable parameters, introducing a smaller intermediate representation to approximate larger matrices results in lower construction error. These findings form the foundation of our FoRA-UA method. By inserting a small intermediate parameter set, we achieve greater model compression without sacrificing performance. We evaluate FoRA-UA across diverse tasks, including natural language understanding (NLU), natural language generation (NLG), instruction tuning, and image classification, demonstrating strong generalisation and robustness under extreme compression.
%U https://aclanthology.org/2025.emnlp-main.321/
%P 6326-6344
Markdown (Informal)
[Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning](https://aclanthology.org/2025.emnlp-main.321/) (Zhao et al., EMNLP 2025)
ACL
- Jinman Zhao, Xueyan Zhang, Jiaru Li, Jingcheng Niu, Yulan Hu, Erxue Min, and Gerald Penn. 2025. Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6326–6344, Suzhou, China. Association for Computational Linguistics.