metadata
license: mit
π BookMIA Datasets
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).
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.
π Applicability
The datasets can be applied to various OpenAI models released before 2023:
- text-davinci-001
- text-davinci-002
- ... and more.
Loading the datasets
To load the dataset:
from datasets import load_dataset
LENGTH = 64
dataset = load_dataset("swj0419/BookMIA")
- Text Lengths:
512
. - Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data.
π οΈ Codebase
For evaluating MIA methods on our datasets, visit our GitHub repository.
β Citing our Work
If you find our codebase and datasets beneficial, kindly cite 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}
}
[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023.