Rocky
aa37067f79
- Add reproducible v6 boundary, blessing, and consensus-adjudication corpora, plus the tiny-transformer trainer and v6 release-gate evaluator that gate every candidate on the deployed baselines. - Wire consensus-label merging, product-policy anchor evaluation, and sealed blessing benchmark review with their pytest coverage. - Refresh open-training corpus generation, iterative retraining runner, and random-holdout evaluation so v6 candidates can be benchmarked end-to-end.
41 lines
1.3 KiB
Markdown
41 lines
1.3 KiB
Markdown
# Clipboard semantic evidence adjudication v1
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Review only the fields listed in `unresolvedFields`. Judge from the text itself;
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do not inspect previous model votes, source labels, or another adjudicator.
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Return one JSON object per input record:
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```json
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{
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"id": "same id",
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"resolutions": {
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"task": "true",
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"ambiguous": "false"
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},
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"confidence": {
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"task": 0.97,
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"ambiguous": 0.94
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},
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"evidence": {
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"task": "send the report",
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"ambiguous": "by Friday"
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}
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}
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```
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Requirements:
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- `resolutions`, `confidence`, and `evidence` must contain exactly the fields in
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`unresolvedFields`.
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- Intent and flag values are `true`, `false`, or `unknown`.
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- Sentiment values are `positive`, `neutral`, `negative`, or `unknown`.
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- Evidence must be a short exact quote copied from the input text.
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- Use `unknown` when the text alone does not justify a decision.
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- Confidence is per field and must be between 0 and 1.
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- Do not output explanations, markdown, or additional fields.
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Use the boundaries from `labeling-instructions-v2.md`. In particular, distinguish
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requests from personal plans, genuine information questions from request-shaped
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commands, direct messages from terminal notices, and expressed wishes from
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quoted, future, sarcastic, or received blessings.
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