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When Disagreement Labels Do Not Identify F1 Rankings
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Roberto Fern\'andez-Barrios, Iker Pastor-L\'opez, Asier Gonz\'alez-Santocildes, Pablo Garc\'ia Bringas

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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.

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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.

Read the full story on arxiv.org

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