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        <title>Shears: Unstructured Sparsity with Neural Low-rank Adapter Search</title>
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    <abstract>Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, we introduce a novel approach called Shears, demonstrating how the integration of cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS) algorithm can further improve the efficiency of PEFT approaches. Results demonstrate the benefits of Shears compared to other methods, reaching high sparsity levels while improving or with little drop in accuracy, utilizing a single GPU for a pair of hours.</abstract>
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%0 Conference Proceedings
%T Shears: Unstructured Sparsity with Neural Low-rank Adapter Search
%A Muñoz, J. Pablo
%A Yuan, Jinjie
%A Jain, Nilesh
%Y Yang, Yi
%Y Davani, Aida
%Y Sil, Avi
%Y Kumar, Anoop
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F munoz-etal-2024-shears
%X Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, we introduce a novel approach called Shears, demonstrating how the integration of cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS) algorithm can further improve the efficiency of PEFT approaches. Results demonstrate the benefits of Shears compared to other methods, reaching high sparsity levels while improving or with little drop in accuracy, utilizing a single GPU for a pair of hours.
%R 10.18653/v1/2024.naacl-industry.34
%U https://aclanthology.org/2024.naacl-industry.34
%U https://doi.org/10.18653/v1/2024.naacl-industry.34
%P 395-405

Markdown (Informal)

[Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://aclanthology.org/2024.naacl-industry.34) (Muñoz et al., NAACL 2024)

• Shears: Unstructured Sparsity with Neural Low-rank Adapter Search (Muñoz et al., NAACL 2024)

ACL

• J. Pablo Muñoz, Jinjie Yuan, and Nilesh Jain. 2024. Shears: Unstructured Sparsity with Neural Low-rank Adapter Search. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 395–405, Mexico City, Mexico. Association for Computational Linguistics.