Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA

• URL: http://arxiv.org/abs/2506.20856v1
• Date: Wed, 25 Jun 2025 22:01:25 GMT
• Title: Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
• Authors: Fei Wang, Baochun Li,
• Abstract summary: Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks.<n>We re-examine memorization in fine-tuning and uncover a surprising divergence from prior findings across different fine-tuning strategies.<n>Using a more relaxed similarity-based memorization metric, we demonstrate that LoRA significantly reduces memorization risks compared to full fine-tuning.
• Score: 35.64232606410778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method. In this work, we re-examine memorization in fine-tuning and uncover a surprising divergence from prior findings across different fine-tuning strategies. Factors such as model scale and data duplication, which strongly influence memorization in pre-training and full fine-tuning, do not follow the same trend in LoRA fine-tuning. Using a more relaxed similarity-based memorization metric, we demonstrate that LoRA significantly reduces memorization risks compared to full fine-tuning, while still maintaining strong task performance.

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