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 tool, we document a popular large language modeling corpus (the Pile) and 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 demo of our tools at dataportraits.org and call on dataset and model creators to release Data Portraits as a complement to current documentation practices.

翻译：基础模型在日益庞大且不透明的数据集上进行训练。即使这些模型如今已成为人工智能系统构建的关键，我们仍难以回答一个直截了当的问题：模型在训练过程中是否已经遇到过某个给定样本？因此，我们建议广泛采用“数据肖像”：一种记录训练数据并允许下游检查的工件。首先，我们概述了这类工件的属性，并讨论了如何利用现有解决方案来提升透明度。随后，我们基于数据草图技术提出并实现了一种解决方案，强调快速且节省空间的查询能力。利用我们的工具，我们对一个流行的大型语言建模语料库（The Pile）进行了文档记录，并展示了该解决方案能够回答关于测试集泄露和模型抄袭的问题。我们的工具轻量且快速，仅占用数据集大小的3%作为额外开销。我们在dataportraits.org网站发布了工具演示，并呼吁数据集和模型创建者发布数据肖像，作为当前文档实践的补充。