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Training Data Disclosure Evidence Framework

Classify each statement as exact item disclosure, named dataset, source category, proportion, collection method, filtering method, deduplication method, token count, time

Research Note

Classify each statement as exact item disclosure, named dataset, source category, proportion, collection method, filtering method, deduplication method, token count, time

Aug 4, 20261 min readBy Dalton Anderson
In this article

Training Data Disclosure Evidence Framework

Classify each statement as exact item disclosure, named dataset, source category, proportion, collection method, filtering method, deduplication method, token count, time cutoff, synthetic-data description, post-training description, or undisclosed.

Record the publisher, artifact, version, date, quotation or faithful paraphrase, scope, exclusions, uncertainty, and what cannot be inferred.

A phrase such as publicly available online data does not identify domains, documents, rights, geography, language distribution, persistence, or removals. A filtering description does not prove every prohibited or low-quality item was removed.

Use missing rather than estimated when the source does not disclose a fact.

Sources

Follow the evidence.

  1. Introducing Llama 3.1ai.meta.com
  2. ai.meta.com: the llama 3 herd of modelsai.meta.com
  3. arxiv.org: 2001arxiv.org
  4. arxiv.org: 1810arxiv.org
  5. arxiv.org: 2203arxiv.org
  6. crfm.stanford.edu: indexcrfm.stanford.edu
  7. daltonanderson.ghost.io: metas llama 3 1 inside the ai research paperdaltonanderson.ghost.io
  8. Meta Llama models repositorygithub.com
  9. github.com: LICENSEgithub.com
  10. github.com: MODEL CARDgithub.com
  11. open.spotify.com: 0iRBPcPw9iYjpUVAVWSkRCopen.spotify.com
  12. NIST AI Risk Management Frameworknist.gov
  13. youtu.be: UMhmWCor1kYyoutu.be