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Benchmark design: building evaluation sets worth trusting

An evaluation set is a measurement instrument. Its labels must be more reliable than the systems it measures.

Read the guide
Independent judgments โ†’ Resolve disagreements โ†’ Accepted referenceThree connected layers illustrate how a defined task becomes expert annotations and a reviewed reference.
Independent judgmentsResolve disagreementsAccepted reference

Four design properties

  • Coverage: items map to a declared skill taxonomy.
  • Validity: answer keys reviewed by qualified people; label errors measurably affect rankings (Northcutt, Athalye, Mueller, arXiv:2103.14749).
  • Freshness: items not exposed online, to limit training contamination.
  • Documentation: a datasheet-style record of how items were made (Gebru et al., arXiv:1803.09010).

Private vs public benchmarks

Public benchmarks enable comparison but leak into training corpora over time. Private, held-out sets keep measurement value longer โ€” which is why some owners license data for evaluation only, never training.

Item record template

FieldPurpose
Skill tagCoverage analysis
Author ID (pseudonymous)Author-level error analysis
Reviewer sign-offAnswer-key validity
Created date / jurisdictionTemporal validity
Exposure statusContamination tracking

Have an annotated dataset or specialist expertise?

Tell us what you own, how the labels were reviewed and which rights you can license. A short overview of your dataset or annotation business is a useful place to start.

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