Bag of Tricks for Training Data Extraction from Language Models

This work is published in ICML 2023, Paper.

Brief Introduction

The overall evaluation of the methods are as follows.

And we recommend the following hyperparameters provided by autotuning.

Hyperparameter	Top-k	Nucleus	Typical-p	Temperature	Repetition Penalty
Value	24	0.8	0.9	0.58	1.04

Requirements

Install the required packages by

python requirements.txt

Run the Code

Download the dataset from (https://github.com/google-research/lm-extraction-benchmark/datasets) and change the following _DATASET_DIR as yours. Run the baseline code by

python3 baseline_ori.py --_DATASET_DIR yours  _NUM_TRIALS 1

Run the corresponding method code by

python3 baseline_method.py --_DATASET_DIR yours  _NUM_TRIALS 1

Questions

If there are any questions, feel free to contact us at weichen.yu@cripac.ia.ac.cn.

Acknowledgement

We thank the effort of Training Data Extraction Challenge as well as the dataset and the baseline provided by them. Some methods described in the paper are similar to/inspired by the methods in the following nice works.

Carlini, Nicholas, et al. "Extracting Training Data from Large Language Models.".
Lehman, Eric, et al. "Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?".
Kandpal, Nikhil, et al. "Deduplicating training data mitigates privacy risks in language models".

References

If you find the code useful for your research, please consider citing

@article{yu2023bag,
  title={Bag of tricks for training data extraction from language models},
  author={Yu, Weichen and Pang, Tianyu and Liu, Qian and Du, Chao and Kang, Bingyi and Huang, Yan and Lin, Min and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2302.04460},
  year={2023}
}