Alternate Preference Optimization for Unlearning Knowledge

Implementation for Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models", COLING 2025.

In all our experiments, we rely on TOFU checkpoints and eval logs (in the data folder) in our experiments. For Llama3.2 we train our own models with parameters as mentioned in the paths and configs.

Installation

conda create -n tofu python=3.12
conda activate tofu
pip install -r requirements.txt
pip install flash-attn --no-build-isolation

Quick Start

Generate Alternate Dataset

python generate.py dataset_config.dataset_kwargs.name=forget10
python generate.py dataset_config.dataset_kwargs.name=forget05
python generate.py dataset_config.dataset_kwargs.name=forget01

AltPO

python forget.py --config-name=unlearn_llama2.yaml forget_loss=subdpo beta=0.1 retain_wt=1 seed=0 lr=5e-05 num_epochs=2 augment_k=5 batch_size=5

NPO

python forget.py --config-name=unlearn_llama2.yaml forget_loss=npo beta=0.05 retain_wt=2 seed=0 lr=2e-05 num_epochs=10 batch_size=5

IdkDPO

python forget.py --config-name=unlearn_llama2.yaml forget_loss=idkdpo beta=0.1 retain_wt=1 seed=0 lr=2e-05 num_epochs=10 batch_size=5

You can find the stored results in paper_models/<MODEL>/<FORGET_SPLIT>/<METHOD>/<PARAMS>

Citing Our Work

If you find this repository or our method beneficial, please cite our work:

@article{mekala2024alternate,
  title={Alternate preference optimization for unlearning factual knowledge in large language models},
  author={Mekala, Anmol and Dorna, Vineeth and Dubey, Shreya and Lalwani, Abhishek and Koleczek, David and Rungta, Mukund and Hasan, Sadid and Lobo, Elita},
  journal={arXiv preprint arXiv:2409.13474},
  year={2024}
}