Skip to content

Code for the differential learning algorithms for soft and discrete prompts.

License

Notifications You must be signed in to change notification settings

cleverhans-lab/PrivatePrompts

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PrivatePrompts

Code for the differential learning algorithms for soft and discrete prompts.

Abstract

Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address this vulnerability, one could forego prompting and resort to fine-tuning LLMs with known algorithms for private gradient descent. However, this comes at the expense of the practicality and efficiency offered by prompting. Therefore, we propose to privately learn to prompt. We first show that soft prompts can be obtained privately through gradient descent on downstream data. However, this is not the case for discrete prompts. Thus, we orchestrate a noisy vote among an ensemble of LLMs presented with different prompts, i.e., a flock of stochastic parrots. The vote privately transfers the flock's knowledge into a single public prompt. We show that LLMs prompted with our private algorithms closely match the non-private baselines. For example, using GPT3 as the base model, we achieve a downstream accuracy of 92.7% on the sst2 dataset with eps=0.147,delta=10^{-6}-differential privacy vs. 95.2% for the non-private baseline. Through our experiments, we also show that our prompt-based approach is easily deployed with existing commercial APIs.

Links

OpenReview: https://openreview.net/forum?id=u6Xv3FuF8N

ArXiv: https://arxiv.org/abs/2305.15594

About

Code for the differential learning algorithms for soft and discrete prompts.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published