Data Portraits: Recording Foundation Model Training Data (2303.03919v2)

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 LLMing 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.

• The paper introduces Data Portraits, a framework using Bloom filters to efficiently record and query model training data membership.
• It demonstrates that the method incurs only a 3% dataset overhead while mitigating test set leakage and potential plagiarism.
• The study advocates integrating Data Portraits into standard documentation practices to enhance transparency and accountability in AI research.

An Evaluation of Data Portraits in Foundation Model Transparency

The paper "Data Portraits: Recording Foundation Model Training Data" by Marc Marone and Benjamin Van Durme proposes a methodological advancement aimed at enhancing transparency in foundation models by recording their training data. With the increasing scale and opacity of datasets used in training state-of-the-art AI models, understanding whether a given example was part of the training data becomes non-trivial. This paper introduces Data Portraits as a viable solution to this problem.

Summary of Contributions

The research delineates the concept of Data Portraits, a framework designed for the inspection and recording of training data. It discusses the critical feature of membership inference, whereby one can determine if a specific example is included in the training set of a model. Data Portraits can serve various stakeholders, including content creators, scientists, and consumers, each of whom may have different motivations for understanding the composition of a dataset used by a foundation model.

A significant contribution of the paper is the implementation of a data sketching technique based on Bloom filters. This technique facilitates fast and efficient querying of training data without significantly increasing the overhead—approximately 3% of the dataset size. Furthermore, the authors present an empirical analysis of their tool on two extensive corpora: "The Pile" and "The Stack," demonstrating its utility in addressing issues such as test set leakage and potential plagiarism by models. The authors also advocate for the integration of Data Portraits into the standard practices for documentation of datasets and models.

Analytical Insights

This paper provides an insightful examination into how Data Portraits could improve datasets' transparency within AI research and applications. Data sketching using Bloom filters stands out as a notable approach due to its space efficiency and speed in membership queries. The authors detail the functionality of Bloom filters in recording strided n-grams, allowing for efficient membership inference with minimum latency.

A noteworthy element is the exploration of how Data Portraits can address the perennial issues of dataset contamination, test set leakage, and memorization in models like GPT-3. Specifically, the paper's findings underscore the challenge of model adherence to test data integrity, exemplified by the overlap between WMT test sets and The Pile. The implications of such test data overlap are profound, opening discussions around the reliability and credibility of trained foundation models in real-world applications.

Practical Impact and Theoretical Implications

Practically, the introduction of Data Portraits provides a tangible mechanism for model developers and users to ensure that foundation models do not depend on unauthorized or proprietary content or generate previously seen examples verbatim. This capability can lead to more robust and ethically sound AI models, adhering to data protection regulations.

Theoretically, Data Portraits propose a framework that can stimulate further research into developing transparent, accountable, and privacy-preserving machine learning artifacts. While Bloom filters provide an effective balance of space efficiency and processing speed, future work could explore alternative data structures that might offer enhanced functionalities such as fuzzy matching or direct context retrieval without compromising privacy.

Conclusion

In conclusion, the paper introduces Data Portraits as a critical tool for increasing transparency in the era of large-scale model training. By enabling stakeholders to query training datasets efficiently, it offers a path forward in documenting and understanding the intricate datasets that fuel today's AI advancements. Moving forward, such tools can inform discussions around model development, deployment transparency, and ethical AI practices. While preliminary, the focus on Data Portraits is a meaningful step towards a more transparent ecosystem in AI research and its applications.