arXiv cs.LGPaper
Prevalence Determines Precision:Silent Contamination in Detector-Defined Datasets
This is methodologically rigorous and solves a real blind spot: teams building datasets often optimize detector quality while ignoring pool contamination, which turns out to be the actual lever. If you're constructing training data by running detectors over candidate pools, this paper gives you the framework to audit your contamination silently. Worth a read if you own data quality.