Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models

• URL: http://arxiv.org/abs/2502.13313v1
• Date: Tue, 18 Feb 2025 22:16:03 GMT
• Title: Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
• Authors: Soumi Das, Camila Kolling, Mohammad Aflah Khan, Mahsa Amani, Bishwamittra Ghosh, Qinyuan Wu, Till Speicher, Krishna P. Gummadi,
• Abstract summary: We study the inherent trade-offs in minimizing privacy risks and maximizing utility, while maintaining high computational efficiency. We find that efficient fine-tuning methods like LoRA mitigate privacy risks similar to private fine-tuning methods like DP.
• Score: 12.635018411121413
• License:
• Abstract: We study the inherent trade-offs in minimizing privacy risks and maximizing utility, while maintaining high computational efficiency, when fine-tuning large language models (LLMs). A number of recent works in privacy research have attempted to mitigate privacy risks posed by memorizing fine-tuning data by using differentially private training methods (e.g., DP), albeit at a significantly higher computational cost (inefficiency). In parallel, several works in systems research have focussed on developing (parameter) efficient fine-tuning methods (e.g., LoRA), but few works, if any, investigated whether such efficient methods enhance or diminish privacy risks. In this paper, we investigate this gap and arrive at a surprising conclusion: efficient fine-tuning methods like LoRA mitigate privacy risks similar to private fine-tuning methods like DP. Our empirical finding directly contradicts prevailing wisdom that privacy and efficiency objectives are at odds during fine-tuning. Our finding is established by (a) carefully defining measures of privacy and utility that distinguish between memorizing sensitive and non-sensitive tokens in training and test datasets used in fine-tuning and (b) extensive evaluations using multiple open-source language models from Pythia, Gemma, and Llama families and different domain-specific datasets.

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