| --- |
| 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} |
| } |
| ``` |
