ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods 🔍

📝 Overview

This is the official repository for ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods. The repo contains the lightweight implementation on the WikiMIA benchmark. Check out our website and paper for more information.

🛠 Installation

pip install -r requirements.txt

🚀 Usage

Run ReCaLL with the following command:

cd src
python run.py --target_model <TARGET_MODEL> --ref_model <REFERENCE_MODEL> --output_dir <OUTPUT_PATH> --dataset <DATASET> --sub_dataset <SUB_DATASET> --num_shots <NUM_SHOTS>

Example:

python run.py --target_model "EleutherAI/pythia-6.9b" --ref_model "EleutherAI/pythia-70m" --output_dir ./output --dataset "wikimia" --sub_dataset "128" --num_shots 7

🔧 Parameters:

Parameter	Description
--target_model	Target model to evaluate (e.g., "EleutherAI/pythia-6.9b")
--ref_model	Reference model for comparison (e.g., "EleutherAI/pythia-70m")
--output_dir	Directory to save output files
--dataset	Dataset to use ("wikimia")
--sub_dataset	Subset of the dataset (e.g., "128" from wikimia dataset)
--num_shots	Number of shots for prefix
--pass_window	(Optional) exceed the context window
--synthetic_prefix	(Optional) Use synthetic prefixes generated by GPT-4o
--api_key_path	(Optional) Path to OpenAI API key file (required for synthetic prefixes)

📊 Example Output

The script will output results in JSON format and generates visualizations for:

• ReCaLL score
• Loss
• Reference
• Zlib
• Min-k%
• Min-k++

Example visualization from 1 - 28 shots:

🌟 Citation

If you find this work helpful, please consider citing:

@article{xie2024recall,
    title={ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods},
    author={Xie, Roy and Wang, Junlin and Huang, Ruomin and Zhang, Minxing and Ge, Rong and Pei, Jian and Gong, Neil Zhenqiang and Dhingra, Bhuwan},
    journal={arXiv preprint arXiv:2406.15968},
    year={2024}
}

📬 Contact

For questions or issues, please open an issue on GitHub or contact the authors directly.