Evaluation resources and benchmark governance
Evaluation data requires the same provenance discipline as training data plus stronger controls against contamination and answer leakage.
Resource record
For each benchmark or evaluation set, record:
- canonical repository, paper, version, revision, and licence;
- intended task, population, language, and domain;
- metric definitions, aggregation, uncertainty, and known limitations;
- access or gating terms and whether examples may be redistributed;
- contamination risk and separation controls;
- prompt, decoding, scoring, and harness versions;
- sample exclusions and failure-handling policy.
Evaluation rules
- Keep evaluation inputs out of training and prompt-development mixtures.
- Do not publish gated or leakage-sensitive examples in screenshots, logs, or documentation.
- Pin benchmark revisions and harness code.
- Prefer deterministic or independently auditable metrics.
- Report confidence intervals or run-to-run variation when stochasticity matters.
- Separate capability, safety, robustness, efficiency, and user-experience claims.
- Record failures and invalid runs; never silently discard them.
- Treat model-graded metrics as measurements from a named, versioned judge, not objective truth.
Example evaluation families
The research Collections nominate real external resources for study. Inclusion in a Collection is not permission to copy, train on, redistribute, or reveal their contents. Their current licences, gates, cards, and contamination guidance must be reviewed at the time of use.