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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.

πŸ“Œ 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])

πŸ› οΈ Codebase

For evaluating MIA methods on our datasets, visit our GitHub repository.

⭐ Citing our Work

If you find our codebase and datasets beneficial, kindly cite our work:

@article{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},
      journal={arXiv:2402.07841},
}
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