Data Portraits: Recording Foundation Model Training Data

TL;DR

This paper introduces Data Portraits, a lightweight, efficient method for recording and inspecting training data of foundation models, enhancing transparency and enabling detection of data leakage and plagiarism.

Contribution

It proposes a novel data sketching approach for creating Data Portraits, facilitating fast, space-efficient inspection of training datasets for foundation models.

Findings

Enabled detection of test set leakage

Allowed identification of model plagiarism

Cost only 3% of dataset size in overhead

Abstract

Foundation models are trained on increasingly immense and opaque datasets. Even while these models are now key in AI system building, it can be difficult to answer the straightforward question: has the model already encountered a given example during training? We therefore propose a widespread adoption of Data Portraits: artifacts that record training data and allow for downstream inspection. First we outline the properties of such an artifact and discuss how existing solutions can be used to increase transparency. We then propose and implement a solution based on data sketching, stressing fast and space efficient querying. Using our tools, we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack). We show that our solution enables answering questions about test set leakage and model plagiarism. Our tool is lightweight and fast, costing only 3% of the dataset size in overhead. We release a live interface of our tools at https://dataportraits.org/ and call on dataset and model creators to release Data Portraits as a complement to current documentation practices.