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Morning Edition · Thursday, July 16, 2026Published at 1:44 AM EDT · New York
OriginBlame Proposes Record- and Token-Level Provenance to Locate an Author's Data Inside a Training Set
The method targets a gap that makes data-removal and unlearning requests impractical today: nobody can point to which training records belong to a given contributor.
A paper introducing OriginBlame tackles an operational gap behind data-removal and unlearning requests. When a contributor asks that their data be removed, model trainers face an obstacle: machine-unlearning algorithms need a defined "forge…
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