| # 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| - Do not publish gated or leakage-sensitive examples in screenshots, logs, or documentation.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. |
|
|
