Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert

• URL: http://arxiv.org/abs/2505.23868v3
• Date: Mon, 09 Jun 2025 03:25:42 GMT
• Title: Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert
• Authors: Zhaokun Wang, Jinyu Guo, Jingwen Pu, Lingfeng Chen, Hongli Pu, Jie Ou, Libo Qin, Wenhong Tian,
• Abstract summary: Current fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data.<n>We propose a noise-robust adaptation method via asymmetric LoRA poisoning experts (LoPE)<n>LoPE achieves strong performance and robustness purely through the low-cost noise injection, which completely eliminates the requirement of data cleaning.
• Score: 7.501033048686552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-handling approaches either rely on laborious data pre-processing or employ model architecture modifications prone to error accumulation. In contrast to existing noise-process paradigms, we propose a noise-robust adaptation method via asymmetric LoRA poisoning experts (LoPE), a novel framework that enhances model robustness to noise only with generated noisy data. Drawing inspiration from the mixture-of-experts architecture, LoPE strategically integrates a dedicated poisoning expert in an asymmetric LoRA configuration. Through a two-stage paradigm, LoPE performs noise injection on the poisoning expert during fine-tuning to enhance its noise discrimination and processing ability. During inference, we selectively mask the dedicated poisoning expert to leverage purified knowledge acquired by normal experts for noise-robust output. Extensive experiments demonstrate that LoPE achieves strong performance and robustness purely through the low-cost noise injection, which completely eliminates the requirement of data cleaning.

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