document-classification training-data-quality benchmark compliance-ml label-leakage

Label leakage breaks sensitivity classification benchmarks

Document classification models exploit surface markers instead of content; Strategic 16K corpus removes three categories of residual classifiers to establish reliable baseline—BERT 89.14% accuracy, TF-IDF+LR much cheaper.

Summary

Training data integrity directly impacts production reliability for compliance and security workflows. Inflated metrics from label leakage mask real-world performance degradation and lead to false confidence in deployment decisions.

Why it matters

Implementation verdict

Replaces informal or WikiLeaks-sourced sensitivity classification datasets with a vetted, leakage-controlled alternative. Requires audit of existing training pipelines for similar marker artifacts. Ready now as research benchmark; production integration depends on domain adaptation to your document types.

Sources

1.label leakage: residual classification markers embedded within document bodies that allow models to exploit surface shortcuts rather than learning genuine content-based sensitivity signals
2.BERT achieves the strongest performance (Accuracy = 89.14%, F1 = 89.33%)
3.the first fully reproducible sensitivity classification benchmark constructed under explicit leakage-controlled conditions from WikiLeaks PlusD

Dev Signal

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