| --- |
| license: mit |
| language: |
| - en |
| pretty_name: Dispatch evaluation episodes and prompt sets |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - evaluation |
| - alignment |
| - dispatch |
| --- |
| |
| # Dispatch evaluation episodes and prompt sets |
|
|
| The evaluation set the Dispatch models are scored on. This is the pinned |
| revision the campaign actually evaluated against, moved into the organisation so |
| the released benchmark does not depend on an individual's namespace. |
|
|
| ## What is here |
|
|
| **`episodes/` — six episode sets.** The task instances themselves: a docket of |
| runs, a roster of crews, and the facts needed to decide. The six are the cross |
| of two clause splits and three episode kinds, named |
| `eval_<clause>_<kind>`: |
|
|
| | Axis | Values | Meaning | |
| |---|---|---| |
| | clause | `trained`, `holdout` | whether the deciding clause was demonstrated during finetuning | |
| | kind | `agreement`, `conflict`, `adjacent` | whether the Charter and the cheapest choice coincide, diverge, or the episode probes nearby behaviour | |
|
|
| *Agreement* episodes cannot separate the two motivations, since both point at |
| the same crew. *Conflict* episodes are where they diverge, and are what the |
| headline numbers are measured on. |
|
|
| The held-out split is the load-bearing one: those clauses appear in the |
| midtraining corpus but are never demonstrated during finetuning, so they measure |
| whether a midtrained rule generalises rather than whether a demonstrated one was |
| memorised. |
|
|
| **`prompts/` — eighteen rendered prompt sets.** Each episode set rendered |
| through three presentation surfaces: `canonical`, `trained` and `heldout`. |
| Evaluating all three is deliberate. The seen-versus-unseen *template* split is |
| the second stratification, separating a model that learned the task from one |
| that learned a format. Six slices by three surfaces is eighteen files, and a |
| partial surface set is treated as a failed endpoint rather than a cheaper one. |
|
|
| **`dataset_manifest.json`, `token_audit.json`, `provenance.json`** — the |
| manifests shipped with the set, plus a record of where this copy came from. |
|
|
| ## Using it |
|
|
| Each prompt row is `{id, prompt}`. Sample your model on a prompt set, then score |
| the responses; scoring is a separate stage from sampling throughout this project, |
| so responses are saved once and can be re-scored without re-sampling. The |
| scoring code and the verdict definitions are in the repository below. |
|
|
| ## Provenance |
|
|
| Copied verbatim from |
| `sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1-data`, revision |
| `53007a79779078f8dfc1902758afbcd33837e4c7`, prefix |
| `extensions/template_diversity_v1/data` — the exact pin recorded in the |
| campaign's evaluation contract. `provenance.json` carries the same record. |
|
|
| The EFT mixture that sits beside these in the source repository is published |
| separately as |
| [`arcadia-impact/dispatch-eft`](https://huggingface.co/datasets/arcadia-impact/dispatch-eft) |
| rather than duplicated here. |
|
|
| ## Related |
|
|
| - Models, adapters and scores: [`dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models) |
| - Midtraining corpora: [Charter](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-charter), [Coin](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin) |
| - Elicitation finetuning mixtures: [`dispatch-eft`](https://huggingface.co/datasets/arcadia-impact/dispatch-eft) |
| - Code: [ArcadiaImpact/science-of-midtraining](https://github.com/ArcadiaImpact/science-of-midtraining) |
|
|
| ## Licence |
|
|
| MIT. |
|
|
