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--- |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Book from Project Gutenberg (after PG-19) |
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This dataset contains 9,542 books collected from [Project Gutenberg](https://www.gutenberg.org/), an online library for free e-books. |
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Specifically, we collect books that have been added to Project Gutenberg after the last book |
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that has been included in the widely used [PG-19 dataset](https://huggingface.co/datasets/deepmind/pg19). Of all books included in PG-19, the |
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latest release date on Project Gutenberg was February 10, 2019. We use an [open source library](https://github.com/kpully/gutenberg_scraper) to |
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download all English books that were added to Project Gutenberg after this date (and adapt the code [here](https://github.com/computationalprivacy/document-level-membership-inference/tree/main/data/raw_gutenberg)). |
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As preprocessing, we only consider the text between the explicit start and end of the uniformly formatted text files. |
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This data has been collected as part of the experimental setup of the paper *"Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models"* ([link](https://arxiv.org/pdf/2310.15007)). |
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The goal was to create a dataset containing representative *non-member* documents compared to PG-19 to develop and |
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evaluate a Membership Inference Attack (MIA) against a Large Language Model (LLM) trained on data containing PG-19. |
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We here release the data we have used to generate the results discussed in the paper, mainly to facilitate further research in similar directions. |
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Importantly, research beyond the study in the paper ([here](https://arxiv.org/pdf/2406.17975)) suggests that this dataset exhibits a serious |
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distribution shift in language compared to books in PG19. |
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Hence, it is not recommended to use this data -at least not in its current form- as non-member data to develop and evaluate post-hoc MIAs against LLMs. |
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Of course, the dataset also represents a rich source of natural language from the literature -most of which should be in the public domain in the US- |
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and could also be used for other purposes. |
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If you found this dataset helpful for your work, kindly cite us as |
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|
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``` |
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@article{meeus2023did, |
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title={Did the neurons read your book? document-level membership inference for large language models}, |
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author={Meeus, Matthieu and Jain, Shubham and Rei, Marek and de Montjoye, Yves-Alexandre}, |
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journal={arXiv preprint arXiv:2310.15007}, |
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year={2023} |
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} |
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``` |
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