Knowledge Unlearning for Mitigating Privacy Risks in Langauge Models

paper link: https://arxiv.org/abs/2210.01504

In order to reproduce our results, take the following steps:

1. Create conda environment and install requirements

conda create -n ufl python=3.8
conda activate ufl
# Install the correct torch version depending on CUDA version from https://pytorch.org/
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch 
pip install -r requirements.txt

2. In order to run the basic code, use the following command

python run.py --config configs/example.json

3. Reproducing Experimental Results

Configs

• mode (string) : Either "unlearn" or "general_lm_eval"
  • "unlearn" will measure MA and EL for validation sets with valid_type_path == "target", for others it will run normal evaluation
  • "general_lm_eval" will run normal evaluation for all validation sets, only use when not evaulating the target data (the data that should be unlearned)
• check_validation_only (bool) : If true, a single validation loop will run without training
• do_init_eval (bool) : Whether to run a single validation loop before training
• train_set (string) : Path to train_set, should be a .csv file
• valid_sets (list[string]) : List containing validation set info
  • Could either be a .csv file path, or the dataset name on Huggingface hub
• valid_subset_path (list[string]) : Subset name of the dataset from HF hub
  • If it does not have a subset, or is a .csv file the string will be ignored
• valid_type_path (list[string]) : Type of the valdiation data
  • If it's the target data pass "target"
  • If it's a HF hub data pass the appropriate type
  • If it's a .csv file the string will be ignored
• el_n (list[int]) : list of n values for EL
• el_threshold (float) : The models EL score for unseen data, exact values for each models in paper
• ma_threshold (float) : The models MA score for unseen data, exact values for each models in paper
• min_train_epochs (int) : Guarantees the minimum amount of epochs
  • By default the model will stop training when it reaches both el_threshold and ma_threshold
  • This configuration will give some control over this behaviour
• target_length (int) : The token length of the unlearning target data
• input_length, output_length (int) : The token length of the input, output for LM evaluation tasks
• strategy : Strategy passed to Lightning Trainer()
  • The code was tested with "deepspeed_stage_2" and "deepspeed_stage_2_offload"

Note

• The effective batch size (train_batch_size * gradient_accumulation_steps * ngpu) should be identical to the train set size
  • We found that minimizing gradient updates is crucial for retaining LM performance
  • If "effective batch size" != "train set size" the code will throw an error
• The eval_batch_size will be replaced with train_batch_size only for "target" data, because "target" data are usually much smaller than LM eval data
  • This also speeds up the evaluation, because it guarantees a single eval step
• The code will save two .csv files to "outputs/". They contain MA and EL scores for each individual examples within the target data
  • One contains the validation results measured before training
  • The other contains the validation results throughout training