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("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])

🛠️ 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},
}