Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+size_categories:
+- 1K<n<10K
+---
+
+# Book from Project Gutenberg (after PG-19)
+
+This dataset contains 9,542 books collected from [Project Gutenberg](https://www.gutenberg.org/), an online library for free e-books. 
+
+Specifically, we collect books that have been added to Project Gutenberg after the last book 
+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
+latest release date on Project Gutenberg was February 10, 2019. We use an [open source library](https://github.com/kpully/gutenberg_scraper) to 
+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)).
+As preprocessing, we only consider the text between the explicit start and end of the uniformly formatted text files. 
+
+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)). 
+The goal was to create a dataset containing representative *non-member* documents compared to PG-19 to develop and 
+evaluate a Membership Inference Attack (MIA) against a Large Language Model (LLM) trained on data containing PG-19. 
+
+We here release the data we have used to generate the results discussed in the paper, mainly to facilitate further research in similar directions. 
+Importantly, research beyond the study in the paper ([here](https://arxiv.org/pdf/2406.17975)) suggests that this dataset exhibits a serious
+distribution shift in language compared to books in PG19. 
+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. 
+
+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-
+and could also be used for other purposes. 
+
+If you found this dataset helpful for your work, kindly cite us as
+
+```
+@article{meeus2023did,
+  title={Did the neurons read your book? document-level membership inference for large language models},
+  author={Meeus, Matthieu and Jain, Shubham and Rei, Marek and de Montjoye, Yves-Alexandre},
+  journal={arXiv preprint arXiv:2310.15007},
+  year={2023}
+}
+```
+