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.
π 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("iamgroot42/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])
π οΈ Codebase
For evaluating MIA methods on our datasets, visit our GitHub repository.
β Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
@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)},
}
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