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.
Original language | English (US) |
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State | Published - 2024 |
Event | 12th International Conference on Learning Representations, ICLR 2024 - Hybrid, Vienna, Austria Duration: May 7 2024 → May 11 2024 |
Conference
Conference | 12th International Conference on Learning Representations, ICLR 2024 |
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Country/Territory | Austria |
City | Hybrid, Vienna |
Period | 5/7/24 → 5/11/24 |
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language