@inproceedings{yu-etal-2025-ssmlora,
title = "{SSML}o{RA}: Enhancing Low-Rank Adaptation with State Space Model",
author = "Yu, Jiayang and
Zhang, Yihang and
Wang, Bin and
Lin, Peiqin and
Liu, YongKang and
Feng, Shi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.230/",
doi = "10.18653/v1/2025.naacl-long.230",
pages = "4493--4506",
ISBN = "979-8-89176-189-6",
abstract = "Fine-tuning is a key approach for adapting language models to specific downstream tasks, but updating all model parameters becomes impractical as model sizes increase.Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), address this challenge by introducing additional adaptation parameters into pre-trained weight matrices.However, LoRA{'}s performance varies across different insertion points within the model, highlighting potential parameter inefficiency due to unnecessary insertions. To this end, we propose SSMLoRA (**S**tate **S**pace **M**odel **L**ow-**R**ank **A**daptation), an extension of LoRA that incorporates a State Space Model (SSM) to interconnect low-rank matrices. SSMLoRA ensures that performance is maintained even with sparser insertions. SSMLoRA allows the model to not only map inputs to a low-rank space for better feature extraction but also leverage the computations from the previous low-rank space. Our method achieves comparable performance to LoRA on the General Language Understanding Evaluation (GLUE) benchmark while using only half the parameters. Additionally, due to its structure, SSMLoRA shows promise in handling tasks with longer input sequences."
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<abstract>Fine-tuning is a key approach for adapting language models to specific downstream tasks, but updating all model parameters becomes impractical as model sizes increase.Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), address this challenge by introducing additional adaptation parameters into pre-trained weight matrices.However, LoRA’s performance varies across different insertion points within the model, highlighting potential parameter inefficiency due to unnecessary insertions. To this end, we propose SSMLoRA (**S**tate **S**pace **M**odel **L**ow-**R**ank **A**daptation), an extension of LoRA that incorporates a State Space Model (SSM) to interconnect low-rank matrices. SSMLoRA ensures that performance is maintained even with sparser insertions. SSMLoRA allows the model to not only map inputs to a low-rank space for better feature extraction but also leverage the computations from the previous low-rank space. Our method achieves comparable performance to LoRA on the General Language Understanding Evaluation (GLUE) benchmark while using only half the parameters. Additionally, due to its structure, SSMLoRA shows promise in handling tasks with longer input sequences.</abstract>
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%0 Conference Proceedings
%T SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model
%A Yu, Jiayang
%A Zhang, Yihang
%A Wang, Bin
%A Lin, Peiqin
%A Liu, YongKang
%A Feng, Shi
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F yu-etal-2025-ssmlora
%X Fine-tuning is a key approach for adapting language models to specific downstream tasks, but updating all model parameters becomes impractical as model sizes increase.Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), address this challenge by introducing additional adaptation parameters into pre-trained weight matrices.However, LoRA’s performance varies across different insertion points within the model, highlighting potential parameter inefficiency due to unnecessary insertions. To this end, we propose SSMLoRA (**S**tate **S**pace **M**odel **L**ow-**R**ank **A**daptation), an extension of LoRA that incorporates a State Space Model (SSM) to interconnect low-rank matrices. SSMLoRA ensures that performance is maintained even with sparser insertions. SSMLoRA allows the model to not only map inputs to a low-rank space for better feature extraction but also leverage the computations from the previous low-rank space. Our method achieves comparable performance to LoRA on the General Language Understanding Evaluation (GLUE) benchmark while using only half the parameters. Additionally, due to its structure, SSMLoRA shows promise in handling tasks with longer input sequences.
%R 10.18653/v1/2025.naacl-long.230
%U https://aclanthology.org/2025.naacl-long.230/
%U https://doi.org/10.18653/v1/2025.naacl-long.230
%P 4493-4506
Markdown (Informal)
[SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model](https://aclanthology.org/2025.naacl-long.230/) (Yu et al., NAACL 2025)
ACL
- Jiayang Yu, Yihang Zhang, Bin Wang, Peiqin Lin, YongKang Liu, and Shi Feng. 2025. SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4493–4506, Albuquerque, New Mexico. Association for Computational Linguistics.