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license: mit |
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--- |
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# 📘 BookMIA Datasets |
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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). |
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The dataset contains non-member and member data: |
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- non-member data consists of text excerpts from books first published in 2023 |
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- member data includes text excerpts from older books, as categorized by Chang et al. in 2023. |
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### 📌 Applicability |
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The datasets can be applied to various OpenAI models released before **2023**: |
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- text-davinci-001 |
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- text-davinci-002 |
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- ... and more. |
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## Loading the datasets |
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To load the dataset: |
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```python |
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from datasets import load_dataset |
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LENGTH = 64 |
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dataset = load_dataset("swj0419/BookMIA") |
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``` |
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* Text Lengths: `512`. |
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* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data. |
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## 🛠️ Codebase |
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For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code). |
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## ⭐ Citing our Work |
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If you find our codebase and datasets beneficial, kindly cite our work: |
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```bibtex |
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@misc{shi2023detecting, |
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title={Detecting Pretraining Data from Large Language Models}, |
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author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer}, |
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year={2023}, |
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eprint={2310.16789}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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[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. |
