Impact of Fine-Tuning Methods on Memorization in Large Language Models

• URL: http://arxiv.org/abs/2507.00258v1
• Date: Mon, 30 Jun 2025 20:52:15 GMT
• Title: Impact of Fine-Tuning Methods on Memorization in Large Language Models
• Authors: Jie Hou, Chuxiong Wu, Lannan Luo, Qiang Zeng,
• Abstract summary: We categorize popular fine-tuning approaches and assess their impact on memorization through the lens of membership inference attacks (MIAs)<n>Our results show that, compared to parameter-based fine-tuning, prompt-based fine-tuning achieves competitive performance while exhibiting lower vulnerability to MIAs.<n>These findings suggest that parameter-based fine-tuning is more prone to leaking private information, whereas prompt-based fine-tuning serves as a more privacy-preserving option.
• Score: 8.334869916058746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the capabilities of pre-trained large language models (LLMs) continue to advance, the "pre-train and fine-tune" paradigm has become increasingly mainstream, leading to the development of various fine-tuning methods. However, the privacy risks arising from memorization during fine-tuning have received relatively little attention. To address this gap, we categorize popular fine-tuning approaches and assess their impact on memorization through the lens of membership inference attacks (MIAs). Our results show that, compared to parameter-based fine-tuning, prompt-based fine-tuning achieves competitive performance while exhibiting lower vulnerability to MIAs. Furthermore, prompt-based methods maintain low memorization regardless of model scale. These findings suggest that parameter-based fine-tuning is more prone to leaking private information, whereas prompt-based fine-tuning serves as a more privacy-preserving option.

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