Auditors compare prompts, answers, and close variants against known corpora or model behavior signals. Because training data is often only partially visible, findings indicate risk rather than proving complete absence of contamination.
A contamination audit looks for overlap between evaluation material and data available during model training or development.
Auditors compare prompts, answers, and close variants against known corpora or model behavior signals. Because training data is often only partially visible, findings indicate risk rather than proving complete absence of contamination.