File size: 1,683 Bytes
f820e82 57fd20b 826ade3 57fd20b fbea488 57fd20b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
license: mit
---
# 📘 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.
### 📌 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:
```python
from datasets import load_dataset
dataset = load_dataset("swj0419/BookMIA")
```
* Text Lengths: `512`.
* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
## 🛠️ Codebase
For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code).
## ⭐ Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
```bibtex
@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. |