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MIMIR
These datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models.
📌 Applicability
The datasets can be applied to any model trained on The Pile, including (but not limited to):
- GPTNeo
- Pythia
- OPT
Loading the datasets
To load the dataset:
from datasets import load_dataset
dataset = load_dataset("Al-not-AI/mimir", "pile_cc", split="ngram_7_0.2")
- Available Names:
arxiv,dm_mathematics,github,hackernews,pile_cc,pubmed_central,wikipedia_(en),full_pile,c4,temporal_arxiv,temporal_wiki - Available Splits:
ngram_7_0.2,ngram_13_0.2,ngram_13_0.8(for most sources), 'none' (for other sources) - Available Features:
member(str),nonmember(str),member_neighbors(List[str]),nonmember_neighbors(List[str])
This dataset is forked from a respository linked with this paper:
@inproceedings{duan2024membership,
title={Do Membership Inference Attacks Work on Large Language Models?},
author={Michael Duan and Anshuman Suri and Niloofar Mireshghallah and Sewon Min and Weijia Shi and Luke Zettlemoyer and Yulia Tsvetkov and Yejin Choi and David Evans and Hannaneh Hajishirzi},
year={2024},
booktitle={Conference on Language Modeling (COLM)},
}
The only cahange is in the processing script, the feature elements are now input and label (indicates whether the input datapoint is member or nonmember),
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