dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: doc_idx
dtype: string
splits:
- name: train
num_bytes: 76605258
num_examples: 50000
download_size: 36641532
dataset_size: 76605258
ArXiv papers from The Pile for document-level MIAs against for LLMs (split into sequences)
This dataset contains sequences from 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). This dataset contains the first 25 sequences of 200 words from all the documents made available in full here.
The dataset contains as columns:
- text: the raw text of the sequence
- label: binary label for membership (1=member)
- doc_idx: index allowing to group sequences to the same, original document
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.