This repository provides an original implementation of Detecting Pretraining Data from Large Language Models by *Weijia Shi, *Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu , Terra Blevins , Danqi Chen , Luke Zettlemoyer

Website | Paper | WikiMIA Benchmark | BookMIA Benchmark | Detection Method Min-K% Prob(see the following codebase)

We explore the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To faciliate the study, we built a dynamic benchmark WikiMIA to systematically evaluate detecting methods and proposed Min-K% Prob 🕵️, a method for detecting undisclosed pretraining data from large language models.

⭐ If you find our implementation and paper helpful, please consider citing our work ⭐ :

@misc{shi2023detecting,
    title={Detecting Pretraining Data from Large Language Models},
    author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
    year={2023},
    eprint={2310.16789},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

The WikiMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models. Access our WikiMIA datasets directly on Hugging Face.

from datasets import load_dataset
LENGTH = 64
dataset = load_dataset("swj0419/WikiMIA", split=f"WikiMIA_length{LENGTH}")

• Available Text Lengths: 32, 64, 128, 256.
• Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data.
• WikiMIA is applicable to all models released between 2017 to 2023 such as LLaMA1/2, GPT-Neo, OPT, Pythia, text-davinci-001, text-davinci-002 ...

The BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). Access our BookMIA datasets directly on Hugging Face.

The dataset contains non-member and member data:

• non-member data consists of text excerpts from books first published in 2023
• member data includes text excerpts from older books, as categorized by Chang et al. in 2023.

from datasets import load_dataset
dataset = load_dataset("swj0419/BookMIA")

• Available Text Lengths: 512.
• Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data.
• WikiMIA is applicable to OpenAI models that are released before 2023 text-davinci-003, text-davinci-002 ...

Our codebase supports many models: Whether you're using OpenAI models that offer logits or models from Huggingface, we've got you covered:

• OpenAI Models:

  • text-davinci-003
  • text-davinci-002
  • ...

• Huggingface Models:

  • meta-llama/Llama-2-70b
  • huggyllama/llama-70b
  • EleutherAI/gpt-neox-20b
  • ...

🔐 Important: When using OpenAI models, ensure to add your API key at Line 38 in run.py:

openai.api_key = "YOUR_API_KEY"

Use the following command to run the model:

python src/run.py --target_model text-davinci-003 --ref_model huggyllama/llama-7b --data swj0419/WikiMIA --length 64

🔍 Parameters Explained:

• Target Model: Set using --target_model. For instance, --target_model huggyllama/llama-70b.

• Reference Model: Defined using --ref_model. Example: --ref_model huggyllama/llama-7b.

• Data Length: Define the length for the WikiMIA benchmark with --length. Available options: 32, 54, 128, 256.

📌 Note: For optimal results, use fixed-length inputs with our Min-K% Prob method (When you evalaute Min-K% Prob method on your own dataset, make sure the input length of each example is the same.)

📊 Baselines: Our script comes with the following baselines: PPL, Calibration Method, PPL/zlib_compression, PPL/lowercase_ppl