Privacy-Preserving Instructions for Aligning Large Language Models
Da Yu Peter Kairouz Sewoong Oh Zheng Xu
Service providers of large language model (LLM) applications collect user
instructions in the wild and use them in further aligning LLMs with users'
intentions. These instructions, which potentially contain sensitive
information, are annotated by human workers in the process. This poses a new
privacy risk not addressed by the typical private optimization. To this end, we
propose using synthetic instructions to replace real instructions in data
annotation and model fine-tuning. Formal differential privacy is guaranteed by
generating those synthetic instructions using privately fine-tuned generators.
Crucial in achieving the desired utility is our novel filtering algorithm that
matches the distribution of the synthetic instructions to that of the real
ones. In both supervised fine-tuning and reinforcement learning from human
feedback, our extensive experiments demonstrate the high utility of the final
set of synthetic instructions by showing comparable results to real
instructions. In supervised fine-tuning, models trained with private synthetic
instructions outperform leading open-source models such as Vicuna.