  Although large language models (LLMs) are widely deployed, the data used to
train them is rarely disclosed. Given the incredible scale of this data, up to
trillions of tokens, it is all but certain that it includes potentially
problematic text such as copyrighted materials, personally identifiable
information, and test data for widely reported reference benchmarks. However,
we currently have no way to know which data of these types is included or in
what proportions. In this paper, we study the pretraining data detection
problem: given a piece of text and black-box access to an LLM without knowing
the pretraining data, can we determine if the model was trained on the provided
text? To facilitate this study, we introduce a dynamic benchmark WIKIMIA that
uses data created before and after model training to support gold truth
detection. We also introduce a new detection method Min-K% Prob based on a
simple hypothesis: an unseen example is likely to contain a few outlier words
with low probabilities under the LLM, while a seen example is less likely to
have words with such low probabilities. Min-K% Prob can be applied without any
knowledge about the pretraining corpus or any additional training, departing
from previous detection methods that require training a reference model on data
that is similar to the pretraining data. Moreover, our experiments demonstrate
that Min-K% Prob achieves a 7.4% improvement on WIKIMIA over these previous
methods. We apply Min-K% Prob to three real-world scenarios, copyrighted book
detection, contaminated downstream example detection and privacy auditing of
machine unlearning, and find it a consistently effective solution.
