Did the Neurons Read your Book? Document-level Membership Inference for
  Large Language Models
Matthieu Meeus Shubham Jain Marek Rei Yves-Alexandre de Montjoye
With large language models (LLMs) poised to become embedded in our daily
lives, questions are starting to be raised about the dataset(s) they learned
from. These questions range from potential bias or misinformation LLMs could
retain from their training data to questions of copyright and fair use of
human-generated text. However, while these questions emerge, developers of the
recent state-of-the-art LLMs become increasingly reluctant to disclose details
on their training corpus. We here introduce the task of document-level
membership inference for real-world LLMs, i.e. inferring whether the LLM has
seen a given document during training or not. First, we propose a procedure for
the development and evaluation of document-level membership inference for LLMs
by leveraging commonly used data sources for training and the model release
date. We then propose a practical, black-box method to predict document-level
membership and instantiate it on OpenLLaMA-7B with both books and academic
papers. We show our methodology to perform very well, reaching an impressive
AUC of 0.856 for books and 0.678 for papers. We then show our approach to
outperform the sentence-level membership inference attacks used in the privacy
literature for the document-level membership task. We finally evaluate whether
smaller models might be less sensitive to document-level inference and show
OpenLLaMA-3B to be approximately as sensitive as OpenLLaMA-7B to our approach.
Taken together, our results show that accurate document-level membership can be
inferred for LLMs, increasing the transparency of technology poised to change
our lives.