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---
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](https://archive.org/details/stackexchange) 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:
```json
{"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 <span style="color:red;">released before May 2024 and pretrained with Stack Exchange corpus</span> :
- **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](https://github.com/yyy01/PAC)
## Cite our work
⭐️ If you find our dataset helpful, please kindly cite our work :
```bibtex
@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}
}
``` |