LoRA Dropout as a Sparsity Regularizer for Overfitting Reduction

Yang Lin, Xinyu Ma, Xu Chu, Yujie Jin, Zhibang Yang, Yasha Wang, Hong Mei

Keywords: Large Language Model, LoRA, Dropout

Abstract:

Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks.
However, fine-tuning LoRA-series models also faces the risk of overfitting on small training datasets, and there's still a lack of theoretical guidance and practical mechanisms to control overfitting on LoRA-based PEFT methods. This paper introduces a novel dropout-based sparsity regularizer for LoRA, dubbed LoRA Dropout, which mitigates overfitting by applying refined dropout to LoRA's low-rank matrices.
We establish a theoretical framework that models dropout in LoRA as a sparse fine-tuning process and derive a generalization error bound under this sparsity regularization.
Theoretical results show that appropriate sparsity can tighten the gap between empirical and generalization risks and thereby control overfitting. We further enhance the sparsity patterns in conventional dropout methods and propose an innovative LoRA Dropout method for more precise sparsity regularization to achieve better overfitting reduction.
Furthermore, we introduce a test-time ensemble strategy and provide theoretical evidence demonstrating that the ensemble method can further compress the error bound and lead to better performance.
Extensive experiments on various NLP tasks validate the effectiveness of our LoRA Dropout framework in improving the model's performance.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 10218