The code and data for "Are Large Pre-Trained Language Models Leaking Your Personal Information?" (Findings of EMNLP '22)

Are Large Pre-Trained Language Models Leaking Your Personal Information? We analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due to memorization. However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low.

How does GPT-3 answer this question?

See requirements.txt

python pred.py

After this step, the models' predictions are stored as .pkl files in results/

To analyze the results in csv files and get the scores:

python analysis.py

Note: The scripts test the 0-shot setting by default. Please edit the scripts, i.e., settings =, for evaluation on other settings.

Data are available at data/

context.pkl refers to the context setting

{k}_shot_non_domain.pkl refers to the setting when domain is unknown

{k}_shot.pkl refers to the setting when domain is known

email2name.pkl stores the mapping from email address to name

name2email.pkl stores the mapping from name to email address

email_freq.pkl stores the frequency of email address

The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:

@inproceedings{huang2022large,
  title={Are Large Pre-Trained Language Models Leaking Your Personal Information?},
  author={Huang, Jie and Shao, Hanyin and Chang, Kevin Chen-Chuan},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2022},
  year={2022}
}