The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.

• Website: The landing page for TOFU
• arXiv Paper: Detailed information about the TOFU dataset and its significance in unlearning tasks.
• GitHub Repository: Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset.
• Dataset on Hugging Face: Direct link to download the TOFU dataset.
• Leaderboard on Hugging Face Spaces: Current rankings and submissions for the TOFU dataset challenges.
• Summary on Twitter: A concise summary and key takeaways from the project.

The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models.

conda create -n tofu python=3.10
conda activate tofu
conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
pip install -r requirements.txt
pip install flash-attn --no-build-isolation

To load the dataset, use the following code:

from datasets import load_dataset
dataset = load_dataset("locuslab/TOFU","full")

The code currently supports Phi-1.5, and Llama2-7b chat models. But newer models can directly be added in the model_config.yaml file. For the unlearning challenege, we fine-tuned Phi-1.5 for 5 epochs using a maximum learning rate of 2e-5, and the Llama2-7b chat model for the same duration at 1e-5. Finetuning can be done as follows:

master_port=18765
split=full
model=phi
lr=2e-5
CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=$master_port finetune.py --config-name=finetune.yaml split=${split} batch_size=4 gradient_accumulation_steps=4 model_family=${model} lr=${lr}

Make sure that the path of the model to be unlearned is correctly provided in the config/model_config.yaml file. To unlearn a model on a forget set, use the following command:

CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=$master_port forget.py --config-name=forget.yaml split=${split} batch_size=4 gradient_accumulation_steps=4 model_family=${model} lr=${lr}

• forget01: Forgetting 1% of the original dataset, all entries correspond to a single author.
• forget05: Forgetting 5% of the original dataset, all entries correspond to a single author.
• forget10: Forgetting 10% of the original dataset, all entries correspond to a single author.

Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model.

Head over to our Leaderboard on Hugging Face Spaces and drop your evaluated results file!

If you find our codebase and dataset beneficial, please cite our work:

@misc{tofu2024,
      title={TOFU: A Task of Fictitious Unlearning for LLMs}, 
      author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter},
      year={2024},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}