Related papers: Privacy-Preserving In-Context Learning for Large Language Models

Privacy-Preserving In-Context Learning for Large Language Models

URL: http://arxiv.org/abs/2305.01639v2
Date: Sat, 30 Sep 2023 12:33:13 GMT
Title: Privacy-Preserving In-Context Learning for Large Language Models
Authors: Tong Wu, Ashwinee Panda, Jiachen T. Wang, Prateek Mittal
Abstract summary: In-context learning (ICL) is an important capability of Large Language Models (LLMs) LLMs's responses may leak the sensitive private information contained in in-context exemplars. We propose Differentially Private In-context Learning (DP-ICL), a general paradigm for privatizing ICL tasks.
Score: 36.13851291571231
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In-context learning (ICL) is an important capability of Large Language Models (LLMs), enabling these models to dynamically adapt based on specific, in-context exemplars, thereby improving accuracy and relevance. However, LLM's responses may leak the sensitive private information contained in in-context exemplars. To address this challenge, we propose Differentially Private In-context Learning (DP-ICL), a general paradigm for privatizing ICL tasks. The key idea for DP-ICL paradigm is generating differentially private responses through a noisy consensus among an ensemble of LLM's responses based on disjoint exemplar sets. Based on the general paradigm of DP-ICL, we instantiate several techniques showing how to privatize ICL for text classification and language generation. We evaluate DP-ICL on four text classification benchmarks and two language generation tasks, and our empirical results show that DP-ICL achieves a strong utility-privacy tradeoff.

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