Parameter-Efficient Fine-Tuning with Controls

Chi Zhang, Cheng Jingpu, Yanyu Xu, Qianxiao Li
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:59066-59079, 2024.

Abstract

In contrast to the prevailing interpretation of Low-Rank Adaptation (LoRA) as a means of simulating weight changes in model adaptation, this paper introduces an alternative perspective by framing it as a control process. Specifically, we conceptualize lightweight matrices in LoRA as control modules tasked with perturbing the original, complex, yet frozen blocks on downstream tasks. Building upon this new understanding, we conduct a thorough analysis on the controllability of these modules, where we identify and establish sufficient conditions that facilitate their effective integration into downstream controls. Moreover, the control modules are redesigned by incorporating nonlinearities through a parameter-free attention mechanism. This modification allows for the intermingling of tokens within the controllers, enhancing the adaptability and performance of the system. Empirical findings substantiate that, without introducing any additional parameters, this approach surpasses the existing LoRA algorithms across all assessed datasets and rank configurations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-zhang24y, title = {Parameter-Efficient Fine-Tuning with Controls}, author = {Zhang, Chi and Jingpu, Cheng and Xu, Yanyu and Li, Qianxiao}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {59066--59079}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24y/zhang24y.pdf}, url = {https://proceedings.mlr.press/v235/zhang24y.html}, abstract = {In contrast to the prevailing interpretation of Low-Rank Adaptation (LoRA) as a means of simulating weight changes in model adaptation, this paper introduces an alternative perspective by framing it as a control process. Specifically, we conceptualize lightweight matrices in LoRA as control modules tasked with perturbing the original, complex, yet frozen blocks on downstream tasks. Building upon this new understanding, we conduct a thorough analysis on the controllability of these modules, where we identify and establish sufficient conditions that facilitate their effective integration into downstream controls. Moreover, the control modules are redesigned by incorporating nonlinearities through a parameter-free attention mechanism. This modification allows for the intermingling of tokens within the controllers, enhancing the adaptability and performance of the system. Empirical findings substantiate that, without introducing any additional parameters, this approach surpasses the existing LoRA algorithms across all assessed datasets and rank configurations.} }
Endnote
%0 Conference Paper %T Parameter-Efficient Fine-Tuning with Controls %A Chi Zhang %A Cheng Jingpu %A Yanyu Xu %A Qianxiao Li %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-zhang24y %I PMLR %P 59066--59079 %U https://proceedings.mlr.press/v235/zhang24y.html %V 235 %X In contrast to the prevailing interpretation of Low-Rank Adaptation (LoRA) as a means of simulating weight changes in model adaptation, this paper introduces an alternative perspective by framing it as a control process. Specifically, we conceptualize lightweight matrices in LoRA as control modules tasked with perturbing the original, complex, yet frozen blocks on downstream tasks. Building upon this new understanding, we conduct a thorough analysis on the controllability of these modules, where we identify and establish sufficient conditions that facilitate their effective integration into downstream controls. Moreover, the control modules are redesigned by incorporating nonlinearities through a parameter-free attention mechanism. This modification allows for the intermingling of tokens within the controllers, enhancing the adaptability and performance of the system. Empirical findings substantiate that, without introducing any additional parameters, this approach surpasses the existing LoRA algorithms across all assessed datasets and rank configurations.
APA
Zhang, C., Jingpu, C., Xu, Y. & Li, Q.. (2024). Parameter-Efficient Fine-Tuning with Controls. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:59066-59079 Available from https://proceedings.mlr.press/v235/zhang24y.html.

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