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        <title>Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization</title>
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    <abstract>Low-rank adaptation (LoRA) achieves parameter efficient fine-tuning for large language models (LLMs) by decomposing the model weight update into a pair of low-rank projection matrices. Yet, the memory overhead restricts it to scale up when the model size increases. We propose Randomized LoRA (RLoRA) which adopts Randomized Walsh-Hadamard Transform to achieve significant reduction in the size of trainable parameters compared to LoRA. At the same time, it allows a PAC-Bayes regularizer to be efficiently incorporated to improve generalization. We evaluate the effectiveness of RLoRA on LLMs RoBERTa, GPT-2 and LLaMA-7B using GLUE, E2E and math reasoning benchmarks. With a much lower memory requirement, RLoRA can give similar performance as the SOTA low-rank adaptation methods for these three tasks and significantly better performance under few-shot settings.</abstract>
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%0 Conference Proceedings
%T Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization
%A Lei, Zijian
%A Qian, Dong
%A Cheung, William
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F lei-etal-2024-fast
%X Low-rank adaptation (LoRA) achieves parameter efficient fine-tuning for large language models (LLMs) by decomposing the model weight update into a pair of low-rank projection matrices. Yet, the memory overhead restricts it to scale up when the model size increases. We propose Randomized LoRA (RLoRA) which adopts Randomized Walsh-Hadamard Transform to achieve significant reduction in the size of trainable parameters compared to LoRA. At the same time, it allows a PAC-Bayes regularizer to be efficiently incorporated to improve generalization. We evaluate the effectiveness of RLoRA on LLMs RoBERTa, GPT-2 and LLaMA-7B using GLUE, E2E and math reasoning benchmarks. With a much lower memory requirement, RLoRA can give similar performance as the SOTA low-rank adaptation methods for these three tasks and significantly better performance under few-shot settings.
%U https://aclanthology.org/2024.findings-acl.310
%P 5236-5249

Markdown (Informal)

[Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization](https://aclanthology.org/2024.findings-acl.310) (Lei et al., Findings 2024)

• Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization (Lei et al., Findings 2024)

ACL

• Zijian Lei, Dong Qian, and William Cheung. 2024. Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization. In Findings of the Association for Computational Linguistics ACL 2024, pages 5236–5249, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.