The new RVL-CDIP document-classification audit reports that about 10.5% of 400… · @ktwu01 · TwKit

@ktwu01

The new RVL-CDIP document-classification audit reports that about 10.5% of 400,000 documents have incorrect labels and 35.2% of the test set has a duplicate or near-duplicate in training. It also finds that page identification codes alone support 69% accuracy and that removing them reduces one model’s score by 11 points. If you use RVL-CDIP to select document models, its headline accuracy may partly measure label errors, repeated documents, and source-specific identifiers rather than transferable classification. The audit supports adding deduplication, corrected labels, identifier-masked tests, and external-document testing before making a model decision

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