@inproceedings{patil-etal-2025-stacked,
title = "Stacked {L}o{RA}: Isolated Low-Rank Adaptation for Lifelong Knowledge Management",
author = "Patil, Heramb Vivek and
Sanam, Vaishnavee and
Atre, Minakshi Pradeep",
editor = "T.y.s.s, Santosh and
Shimizu, Shuichiro and
Gong, Yifan",
booktitle = "The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-srw.4/",
pages = "36--46",
ISBN = "979-8-89176-304-3",
abstract = "Continual learning (CL) presents a significant challenge for large pre-trained models, primarily due to catastrophic forgetting and the high computational cost of sequential knowledge updating. Parameter-Efficient Transfer Learning (PETL) methods offer reduced computational burdens but often struggle to effectively mitigate forgetting. This paper introduces Stacked Low-Rank Adaptation (SLoRA), a novel parameter-efficient approach that leverages the additive composition of task-specific, frozen low-rank adapters to enable modular continual learning with inherent support for explicit knowledge modification. SLoRA was evaluated on vision benchmarks, BERT-base, and the 1-billion-parameter Llama-3.2-1B model. Experiments demonstrated that SLoRA almost completely eliminated catastrophic forgetting, achieving a final average accuracy of 92.75{\%} on Llama-3.2-1B while perfectly preserving prior task performance. Furthermore, SLoRA is computationally efficient, enabling up to a 15x training speed-up over full fine-tuning with 99.7{\%} fewer trainable parameters per update. SLoRA offers a compelling balance of forgetting mitigation, parameter efficiency, and modularity, representing a promising direction for developing adaptable and efficient lifelong knowledgeable foundation models."
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<abstract>Continual learning (CL) presents a significant challenge for large pre-trained models, primarily due to catastrophic forgetting and the high computational cost of sequential knowledge updating. Parameter-Efficient Transfer Learning (PETL) methods offer reduced computational burdens but often struggle to effectively mitigate forgetting. This paper introduces Stacked Low-Rank Adaptation (SLoRA), a novel parameter-efficient approach that leverages the additive composition of task-specific, frozen low-rank adapters to enable modular continual learning with inherent support for explicit knowledge modification. SLoRA was evaluated on vision benchmarks, BERT-base, and the 1-billion-parameter Llama-3.2-1B model. Experiments demonstrated that SLoRA almost completely eliminated catastrophic forgetting, achieving a final average accuracy of 92.75% on Llama-3.2-1B while perfectly preserving prior task performance. Furthermore, SLoRA is computationally efficient, enabling up to a 15x training speed-up over full fine-tuning with 99.7% fewer trainable parameters per update. SLoRA offers a compelling balance of forgetting mitigation, parameter efficiency, and modularity, representing a promising direction for developing adaptable and efficient lifelong knowledgeable foundation models.</abstract>
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%0 Conference Proceedings
%T Stacked LoRA: Isolated Low-Rank Adaptation for Lifelong Knowledge Management
%A Patil, Heramb Vivek
%A Sanam, Vaishnavee
%A Atre, Minakshi Pradeep
%Y T.y.s.s, Santosh
%Y Shimizu, Shuichiro
%Y Gong, Yifan
%S The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-304-3
%F patil-etal-2025-stacked
%X Continual learning (CL) presents a significant challenge for large pre-trained models, primarily due to catastrophic forgetting and the high computational cost of sequential knowledge updating. Parameter-Efficient Transfer Learning (PETL) methods offer reduced computational burdens but often struggle to effectively mitigate forgetting. This paper introduces Stacked Low-Rank Adaptation (SLoRA), a novel parameter-efficient approach that leverages the additive composition of task-specific, frozen low-rank adapters to enable modular continual learning with inherent support for explicit knowledge modification. SLoRA was evaluated on vision benchmarks, BERT-base, and the 1-billion-parameter Llama-3.2-1B model. Experiments demonstrated that SLoRA almost completely eliminated catastrophic forgetting, achieving a final average accuracy of 92.75% on Llama-3.2-1B while perfectly preserving prior task performance. Furthermore, SLoRA is computationally efficient, enabling up to a 15x training speed-up over full fine-tuning with 99.7% fewer trainable parameters per update. SLoRA offers a compelling balance of forgetting mitigation, parameter efficiency, and modularity, representing a promising direction for developing adaptable and efficient lifelong knowledgeable foundation models.
%U https://aclanthology.org/2025.ijcnlp-srw.4/
%P 36-46
Markdown (Informal)
[Stacked LoRA: Isolated Low-Rank Adaptation for Lifelong Knowledge Management](https://aclanthology.org/2025.ijcnlp-srw.4/) (Patil et al., IJCNLP 2025)
ACL
- Heramb Vivek Patil, Vaishnavee Sanam, and Minakshi Pradeep Atre. 2025. Stacked LoRA: Isolated Low-Rank Adaptation for Lifelong Knowledge Management. In The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 36–46, Mumbai, India. Association for Computational Linguistics.