dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 146613669
num_examples: 2000
download_size: 67134534
dataset_size: 146613669
ArXiv papers from The Pile for document-level MIAs against LLMs
This dataset contains full ArXiv papers randomly sampled from the train (members) and test (non-members) dataset from (the uncopyrighted version of) the Pile. We randomly sample 1,000 documents members and 1,000 non-members, ensuring that the selected documents have at least 5,000 words (any sequences of characters seperated by a white space). We also provide the dataset where each document is split into 25 sequences of 200 words here.
The dataset contains as columns:
- text: the raw text of the sequence
- label: binary label for membership (1=member)
The dataset can be used to develop and evaluate document-level MIAs against LLMs trained on The Pile. Target models include the suite of Pythia and GPTNeo models, to be found here. Our understanding is that the deduplication executed on the Pile to create the "Pythia-dedup" models has been only done on the training dataset, suggesting this dataset of members/non-members also to be valid for these models.
For more information we refer to the paper.