# Privacy-Preserving Instructions for Aligning Large Language Models

**Authors:** Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu  
**arXiv:** [2402.13659](https://arxiv.org/abs/2402.13659)  
**Date:** 2024-02-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.13659v1/

## Key points

- Authors: Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
- Privacy risks in collecting user instructions for large language model (LLM) applications
- Proposal of using synthetic instructions instead of real ones for data annotation and model fine-tuning
- Generation of synthetic instructions using privately fine-tuned generators to ensure formal differential privacy
- Novel filtering algorithm matching the distribution of synthetic instructions to real ones to preserve utility in the alignment process
- Demonstrated high utility of synthetic instructions through experiments in supervised fine-tuning and reinforcement learning from human feedback
- Models trained with private synthetic instructions outperform leading open-source models like Vicuna in supervised fine-tuning tasks

## Summary

In their paper titled "Privacy-Preserving Instructions for Aligning Large Language Models," authors Da Yu, Peter Kairouz, Sewoong Oh, and Zheng Xu address the privacy risks associated with collecting user instructions for large language model (LLM) applications. These instructions, which may contain sensitive information, are typically annotated by human workers during the alignment process. To mitigate this privacy risk, the authors propose using synthetic instructions instead of real ones for data annotation and model fine-tuning. By generating these synthetic instructions using privately fine-tuned generators, formal differential privacy is ensured. A key aspect of their approach is a novel filtering algorithm that matches the distribution of synthetic instructions to that of real ones, thereby preserving utility in the alignment process. Through extensive experiments involving supervised fine-tuning and reinforcement learning from human feedback, the authors demonstrate the high utility of synthetic instructions by achieving comparable results to real instructions. In fact, models trained with private synthetic instructions outperform leading open-source models like Vicuna in supervised fine-tuning tasks. Overall, this research highlights the importance of addressing privacy concerns in LLM applications and provides a promising solution through the use of synthetic instructions while maintaining performance levels comparable to real data annotation methods. <|endoftext|># -*- coding: utf-8 -*-
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Created on Wed Apr 29 18:48:49 2020

@Author: yichao.li
@Description    This script is used to generate fake data based on given parameters.