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LLM Dataset Inference: Did you train on my dataset?

The proliferation of large language models (LLMs) in the real world has come with a rise in copyright cases against companies for training their models on unlicensed data from the internet. Recent works have presented methods to identify if individual text sequences were members of the model’s training data, known as membership inference attacks (MIAs). We demonstrate that the apparent success of these MIAs is confounded by selecting non-members (text sequences not used for training) belonging to a different distribution from the members (e.g., temporally shifted recent Wikipedia articles compared with ones used to train the model). This distribution shift makes membership inference appear successful. However, most MIA methods perform no better than random guessing when discriminating between members and non-members from the same distribution (e.g., in this case, the same period of time). Even when MIAs work, we find that different MIAs succeed at inferring membership of samples from different distributions. Instead, we propose a new dataset inference method to accurately identify the datasets used to train large language models. This paradigm sits realistically in the modern-day copyright landscape, where authors claim that an LLM is trained over multiple documents (such as a book) written by them, rather than one particular paragraph. While dataset inference shares many of the challenges of membership inference, we solve it by selectively combining the MIAs that provide positive signal for a given distribution, and aggregating them to perform a statistical test on a given dataset. Our approach successfully distinguishes the train and test sets of different subsets of the Pile with statistically significant p-values < 0.1, without any false positives.

Data Used

This repository contains data different subsets of the PILE, divided into train and val sets. The data is in the form of a JSON file, with each entry containing the raw text, as well as various kinds of perturbations applied to it. The dataset is used to facilitate privacy research in language models, where the perturbed data can be used as reference detect the presence of a particular dataset in the training data of a language model.

Quick Links

• arXiv Paper: Detailed information about the Dataset Inference V2 project, including the dataset, results, and additional resources.
• GitHub Repository: Access the source code, evaluation scripts, and additional resources for Dataset Inference.
• Dataset on Hugging Face: Direct link to download the various versons of the PILE dataset.
• Summary on Twitter: A concise summary and key takeaways from the project.

Applicability 🚀

The dataset is in text format and can be loaded using the Hugging Face datasets library. It can be used to evaluate any causal or masked language model for the presence of specific datasets in its training pool. The dataset is not intended for direct use in training models, but rather for evaluating the privacy of language models. Please keep the validation sets, and the perturbed train sets private, and do not use them for training models.

Loading the Dataset

To load the dataset, use the following code:

from datasets import load_dataset
dataset = load_dataset("pratyushmaini/llm_dataset_inference", subset = "wikipedia", split = "train")

Available perturbations:

We use the NL-Augmenter library to apply the following perturbations to the data:

• synonym_substitution: Synonym substitution of words in the sentence.
• butter_fingers: Randomly changing characters from the sentence.
• random_deletion: Randomly deleting words from the sentence.
• change_char_case: Randomly changing the case of characters in the sentence.
• whitespace_perturbation: Randomly adding or removing whitespace from the sentence.
• underscore_trick: Adding underscores to the sentence.

Demo

Run Demo.ipynb to conduct LLM Dataset Inference attacks on your own data and/or understand the code.

Citing Our Work

If you find our codebase and dataset beneficial, please cite our work:

@misc{mainidi2024,
      title={LLM Dataset Inference: Did you train on my dataset?}, 
      author={Pratyush Maini and Hengrui Jia and Nicolas Papernot and Adam Dziedzic},
      year={2024},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}