metadata
license: mit
language:
- en
size_categories:
- 1K<n<10K
Overview
The StakcMIAsub dataset serves as a benchmark for membership inference attack (MIA) topic. StackMIAsub is build based on the Stack Exchange corpus, which is widely used for pre-training. See our paper (to-be-released) for detailed description.
Data format
StakcMIAsub is formatted as a jsonlines file in the following manner:
{"snippet": "SNIPPET1", "label": 1 or 0}
{"snippet": "SNIPPET2", "label": 1 or 0}
...
- 📌 label 1 denotes to members, while label 0 denotes to non-members.
Applicability
Our dataset supports most white- and black-box models, which are released before May 2024 and pretrained with Stack Exchange corpus :
- Black-box OpenAI models:
- text-davinci-001
- text-davinci-002
- ...
- White-box models:
- LLaMA and LLaMA2
- Pythia
- GPT-Neo
- GPT-J
- OPT
- StableLM
- Falcon
- ...
Related repo
To run our PAC method to perform membership inference attack, visit our code repo
Cite our work
⭐️ If you find our dataset helpful, please kindly cite our work :
@misc{ye2024data,
title={Data Contamination Calibration for Black-box LLMs},
author={Wentao Ye and Jiaqi Hu and Liyao Li and Haobo Wang and Gang Chen and Junbo Zhao},
year={2024},
eprint={2405.11930},
archivePrefix={arXiv},
primaryClass={cs.LG}
}