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    <abstract>In this paper, we study parameter-efficient fine-tuning methods for large pre-trained models. Specifically, we improve LoRA approaches to alleviate the performance loss from the constrained adapter by introducing a non-linear transformation (call it LoRAN). For a better adaptation, we also design a new non-linear function to appropriately fit the accumulated weight updates. We test our method in multiple advanced large language models. Experimental results show that our LoRAN significantly outperforms a strong baseline on SAMSum and 20 Newsgroups tasks. Moreover, when a lower rank is applied, our approach even yields a 1.95-point improvement in the classification task.</abstract>
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%0 Conference Proceedings
%T LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation
%A Li, Yinqiao
%A Song, Linqi
%A Hou, Hanxu
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-loran
%X In this paper, we study parameter-efficient fine-tuning methods for large pre-trained models. Specifically, we improve LoRA approaches to alleviate the performance loss from the constrained adapter by introducing a non-linear transformation (call it LoRAN). For a better adaptation, we also design a new non-linear function to appropriately fit the accumulated weight updates. We test our method in multiple advanced large language models. Experimental results show that our LoRAN significantly outperforms a strong baseline on SAMSum and 20 Newsgroups tasks. Moreover, when a lower rank is applied, our approach even yields a 1.95-point improvement in the classification task.
%U https://aclanthology.org/2024.findings-emnlp.177
%P 3134-3143

Markdown (Informal)

[LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation](https://aclanthology.org/2024.findings-emnlp.177) (Li et al., Findings 2024)

• LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation (Li et al., Findings 2024)

ACL

• Yinqiao Li, Linqi Song, and Hanxu Hou. 2024. LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3134–3143, Miami, Florida, USA. Association for Computational Linguistics.