Controlling LLM memorization

Setting up the environment

First setup your conda environment by running conda env create -f environment.yml. You also need to configure HF accelerate. The configuration used in final experiments is in accelerate_config.yaml. You can simply put this file under the cache folder for Accelerate (~/.cache/huggingface/accelerate). This configuration specifies distributed training over 8 GPUs with fp16 mixed-precision and DeepSpeed. You can also configure Accelerate differently for your needs (e.g., increase/decrease # of GPUs) with accelerate config command and answering the prompted questions in terminal.

Dataset

The code reads train and test data in numpy binary format from a folder datasets, which needs to be created. In this section, we describe how to obtain the data used in the paper.

Train split

We used the train split from the Pile dataset for training and evaluating our method. You can use the script load_dataset.py from the LM-extraction-benchmark to extract the 15000 examples used in the paper. The script converts the train split of the Pile data (also available within the repo above) into numpy binary files, then used by our code. The resulting files should be saved in a folder named datasets within the top-level of the Controlling-LLM-memorization repo.

Test split

The test split of the Pile dataset is used to compute perplexity of the models in order to assess performance degradation of the model. After downloading the data, you can use our convert_test_pile.py script to convert the test split into numpy binary file. This should also be saved inside the datasets folder.

Models

HF will pull and cache the models on your first run.

Replicating Results

Simply run src/runner.sh after uncommenting the desired experiment. For example, at the top of the script, we see commands for running the baselines. bash for i in {1..5} do accelerate launch baseline.py --model_size=small accelerate launch baseline.py --model_size=medium done Running this will create tensorboard files under a folder named logs. You can then process these logs, to obtain mean and standard deviation values by running scripts/tensorboard_log_read.py. This is going to create result files under logs/processed_logs where file names specify the experiment setting e.g., modelSize:medium', '(prefixSize:50', 'suffixSize:50', 'numBeams:1').txt contains results for the 1.3B model.

Running additional experiments

Simply extend the runner script as desired. For example, if you want to run an aligned CLM attack with prompt length=200, change the first line of the section below in src/runner.sh to for len_prompt in 200. bash for len_prompt in 1 5 20 100 150 do for i in {1..5} do accelerate launch promptLearn_attack.py --model_size=small --len_prompt=$len_prompt accelerate launch promptLearn_attack.py --model_size=medium --len_prompt=$len_prompt done done You can pass prompt length, prefix size etc. as arguments. Run -h command on python scripts to get comprehensive list of arguments (e.g., python promptLearn_attack.py -h) and see src/runner.sh for more usage.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the CC-BY-NC-4.0 License.

How to cite

The paper introducing the experiments conducted with this code is forthcoming. In the interim, you can cite it as: @Inproceedings{Ozdayi2023, author = {Mustafa Ozdayi and Charith Peris and Jack G. M. FitzGerald and Christophe Dupuy and Jimit Majmudar and Haidar Khan and Rahil Parikh and Rahul Gupta}, title = {Controlling the extraction of memorized data from large language models via Prompt-Tuning}, year = {2023}, url = {https://www.amazon.science/publications/controlling-the-extraction-of-memorized-data-from-large-language-models-via-prompt-tuning}, booktitle = {ACL 2023}, }