
RF
Roberto Fern\'andez-Barrios, Iker Pastor-L\'opez, Asier Gonz\'alez-Santocildes, Pablo Garc\'ia Bringas
· 1 min read
ResearcharXiv cs.LG
When Disagreement Labels Do Not Identify F1 Rankings
arXiv:2611.01712v1 Announce Type: new
Abstract: Agreement labels cancel from an accuracy comparison but can change an F1 ranking. We specialize partial identification to an explicit audit after labeling disagreements: two agreement counts give exact bounds, certifying a ranking or a choice within a specified F1 tolerance under every completion. Across 3,200 frozen model pairs, shared completions tighten bounds and a 0.01 tolerance reduces labeling costs. A zero-loss (weak-winner) control attributes most savings to positive tolerance, not ties. Guarantees concern the evaluated batch, not future data.
Original source
This story was published by arXiv cs.LG and written by Roberto Fern\'andez-Barrios, Iker Pastor-L\'opez, Asier Gonz\'alez-Santocildes, Pablo Garc\'ia Bringas. SyncAI.news shows a preview; the complete article is on the publisher's site.
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