Train Language Models Transparency Audit

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provenance information

MIT News

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Study: Transparency is often lacking in datasets used to train large language models

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87% Informative

MIT researchers trace the data provenance of more than 1,800 text datasets.

They found that 70 percent of these datasets omitted some licensing information, while about 50 percent had errors.

The Data Provenance Explorer could help AI practitioners build more effective models by enabling them to select training datasets that fit their model’s intended purpose.

Researchers saw a spike in restrictions placed on datasets created in 2023 and 2024 .

This might be driven by concerns that their datasets could be used for unintended commercial purposes.

To help others obtain this information without the need for a manual audit, the researchers built the Data Provenance Explorer .

The tool allows users to download a data provenance card that provides a structured overview of dataset characteristics.

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