| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - membership inference |
| - privacy |
| pretty_name: MIMIR |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 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: |
|
|
| ```python |
| 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: |
|
|
| ```bibtex |
| @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), |
