Datasets:
Jev Rules-7 (evaluation only)
Seven rule and policy decision tasks (admission policy, rental policy, rules and scenarios (ShARC), agent rules, authority
rules, fraud points, patent phrases) plus a missing-fact test, in the same style as the policy tasks that other System One
models report. Comparable tasks appear in the jev/jevos project README; the sets used there are not published, so these
are rebuilt. Facts in the state, the policy RULE IN THE QUESTION. Code: rules7.py.
Skills (3,440 items, seed 0)
| skill | items | type | source | licence |
|---|---|---|---|---|
admission |
400 | noul, 200 yes / 200 no | generated: policy over 2-4 conditions (GPA, exam, letters, experience, age, deadline, portfolio, fee, degree; and / or / unless), thresholds and values near the boundary, distractor fields | CC0-1.0 |
rental |
400 | choice (5 outcomes, 80 each) | generated: refuse / approve / surcharge / deposit / both from age, licence years, DUI, vehicle class, payment method | CC0-1.0 |
agent_rules |
400 | noul, 200 / 200 | generated: three rules (spend, email, delete, deploy, read data) in the question, one requested action in the state | CC0-1.0 |
authority |
400 | choice (5 roles, 80 each) | generated: approval ladders by amount, travel, licences, contracts, hires | CC0-1.0 |
fraud_points |
400 | score (4 levels, 100 each) | generated: six point rules (6 of 10, random thresholds / points) summed, level cut-offs in the question | CC0-1.0 |
sharc |
400 | noul, 200 / 200 | ShARC dev, yes / no rows only: tasksource/sharc@fa4605de8281290c69af9dea6118b37d072c2b59 (validation) |
CC-BY-SA-3.0 |
patent_phrases |
400 | score (0, .25, .5, .75, 1; 80 each) | US Patent Phrase-to-Phrase Matching, held-out split with expert scores: tasksource/patent-phrase-similarity@ff1868796e44ac9ec02e870b447fde6c5bf2d6e4 (test) |
CC-BY-4.0 |
missing_facts |
640 (160 pairs per task) | admission (noul) and fraud_points (score) | the same generators; each pair = a complete record and the record with ONE needed fact removed (the rule cannot be decided; chosen by three-valued evaluation) | CC0-1.0 |
Split tags: generated or public. ShARC question wording: "Using only the rule text, the scenario and the conversation
so far, do the rules say that the answer to the user's question is yes?" (state: rule text, scenario, conversation,
question). The ShARC dev split has only 56 rule texts with yes / no answers, so up to 8 utterances per rule text are
kept. Patent state: phrase A, phrase B, CPC class; the five levels are the Kaggle rating scale.
Sources and exclusions
- ShARC and the patent phrases use only eval / test splits at pinned revisions. 36 ShARC dev rows (2 duplicated rule texts, listed in
drop_keys.json) are excluded from the selection.
Metrics
Per skill accuracy / Brier / ECE, per_split, and in extras:
balanced_accuracy for the yes / no skills and missing_facts -> auroc/admission, auroc/fraud_points: AUROC of the
model's confidence (max probability) separating complete (positive) from missing-fact items,
(0.5 = cannot tell). missing_facts accuracy is on the complete items only (the missing ones have no gold).
Run
python rules7.py --out DIR needs network for the two public sets (pinned revisions), a minute; it writes rules7_v1_items.json.gz.