| --- |
| pretty_name: procedural-typed-decisions |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| tags: |
| - tasksource |
| - jev |
| - system-one |
| - procedural |
| - synthetic |
| - multi-question |
| configs: |
| - config_name: arithmetic |
| data_files: |
| - split: train |
| path: arithmetic/train-*.parquet |
| - split: validation |
| path: arithmetic/validation-*.parquet |
| - split: test |
| path: arithmetic/test-*.parquet |
| - config_name: entity_belief_tracking |
| data_files: |
| - split: train |
| path: entity_belief_tracking/train-*.parquet |
| - split: validation |
| path: entity_belief_tracking/validation-*.parquet |
| - split: test |
| path: entity_belief_tracking/test-*.parquet |
| - config_name: event_state_reconstruction |
| data_files: |
| - split: train |
| path: event_state_reconstruction/train-*.parquet |
| - split: validation |
| path: event_state_reconstruction/validation-*.parquet |
| - split: test |
| path: event_state_reconstruction/test-*.parquet |
| - config_name: evidence_sufficiency |
| data_files: |
| - split: train |
| path: evidence_sufficiency/train-*.parquet |
| - split: validation |
| path: evidence_sufficiency/validation-*.parquet |
| - split: test |
| path: evidence_sufficiency/test-*.parquet |
| - config_name: multi_view_adjudication |
| data_files: |
| - split: train |
| path: multi_view_adjudication/train-*.parquet |
| - split: validation |
| path: multi_view_adjudication/validation-*.parquet |
| - split: test |
| path: multi_view_adjudication/test-*.parquet |
| - config_name: needle_retrieval |
| data_files: |
| - split: train |
| path: needle_retrieval/train-*.parquet |
| - split: validation |
| path: needle_retrieval/validation-*.parquet |
| - split: test |
| path: needle_retrieval/test-*.parquet |
| - config_name: partial_observation_calibration |
| data_files: |
| - split: train |
| path: partial_observation_calibration/train-*.parquet |
| - split: validation |
| path: partial_observation_calibration/validation-*.parquet |
| - split: test |
| path: partial_observation_calibration/test-*.parquet |
| - config_name: policy_applicability |
| data_files: |
| - split: train |
| path: policy_applicability/train-*.parquet |
| - split: validation |
| path: policy_applicability/validation-*.parquet |
| - split: test |
| path: policy_applicability/test-*.parquet |
| - config_name: policy_under_uncertainty |
| data_files: |
| - split: train |
| path: policy_under_uncertainty/train-*.parquet |
| - split: validation |
| path: policy_under_uncertainty/validation-*.parquet |
| - split: test |
| path: policy_under_uncertainty/test-*.parquet |
| - config_name: record_aggregation |
| data_files: |
| - split: train |
| path: record_aggregation/train-*.parquet |
| - split: validation |
| path: record_aggregation/validation-*.parquet |
| - split: test |
| path: record_aggregation/test-*.parquet |
| - config_name: state_perturbation |
| data_files: |
| - split: train |
| path: state_perturbation/train-*.parquet |
| - split: validation |
| path: state_perturbation/validation-*.parquet |
| - split: test |
| path: state_perturbation/test-*.parquet |
| - config_name: table_lookup |
| data_files: |
| - split: train |
| path: table_lookup/train-*.parquet |
| - split: validation |
| path: table_lookup/validation-*.parquet |
| - split: test |
| path: table_lookup/test-*.parquet |
| --- |
| |
| # procedural-typed-decisions |
|
|
| Procedurally generated decision problems. Each row is one structured state |
| (JSON, or a table, CSV, key=value lines, or prose for the arithmetic, |
| retrieval, and aggregation configs) with **several typed questions over that same state**, following the |
| Jev / System One request shape: `choice` (pick one criterion), `noul` (a |
| number in [0, 1]; a probability or a yes/no), and `score` (an ordered rubric). |
| Every answer is computed exactly from the state by rules that the state |
| itself spells out, so the labels are noise-free. |
|
|
| This is an independent dataset. It is not an official TypeSafe Jev dataset and |
| is not produced by or affiliated with TypeSafe or OpenJev. |
|
|
| ## Configs |
|
|
| | config | questions | |
| |---|---| |
| | `arithmetic` | An order with a discount/shipping rule, an account ledger, or a schedule; each state asks 2–5 of: `amount_due` / `final_balance` / `finish_time` (choice among the result and typical slips), `within_budget`, `went_negative`, `done_by_deadline` (noul), `random_line_bulk`, `random_is_deposit`, `random_is_long` (noul, exact probability k/n), `budget_use`, `net_change` (score, descriptive levels), `lines_above`, `withdrawal_count`, `starts_before_noon` (score), `largest_line`, `lowest_day`, `longest_task` (choice) | |
| | `entity_belief_tracking` | `world_location` (choice), `agent_belief_location` (choice), `belief_matches_world` (noul) | |
| | `event_state_reconstruction` | `current_owner` (choice), `is_open` (noul), `current_severity` (score) | |
| | `evidence_sufficiency` | `claim_supported` (noul), `has_conflict` (noul), `strongest_support_origin` (choice) | |
| | `multi_view_adjudication` | `intent` (choice), `is_urgent` (noul), `workflow_impact` (score) | |
| | `needle_retrieval` | `value_of_id` (choice), `id_has_value` (noul), `id_listed` (noul); up to ~300 records whose ids differ from the target by one or two digits | |
| | `partial_observation_calibration` | `incident_real` (noul, exact Bayesian posterior) | |
| | `policy_applicability` | `access_allowed` (noul), `governing_policy` (choice), `review_risk` (score) | |
| | `policy_under_uncertainty` | `access_allowed` (noul), `governing_policy` (choice), `requester_role` (choice); exact posteriors over a role known through history counts and reports of stated reliability | |
| | `record_aggregation` | `count_in_category` (score), `largest_quantity` (choice), `any_out_of_stock` (noul), `total_above` (noul) | |
| | `state_perturbation` | `material_change` (noul), `changed_dimension` (choice), `risk_direction` (score) | |
| | `table_lookup` | `find_person` (choice, two-condition filter), `manager_of` (choice, join), `started_before` (noul), `count_matching` (score) | |
|
|
| ## Schema |
|
|
| | field | meaning | |
| |---|---| |
| | `id` | `task:split:index` | |
| | `level` | Difficulty level (0–4); larger levels add events, records, sensors, or distractors. | |
| | `state` | The state: a JSON string, or rendered text for the retrieval and aggregation configs. | |
| | `questions` | JSON object of named System One questions (`type`, `instructions`, `criteria`). | |
| | `answers` | JSON object of reference answers, in the System One `answers` shape. | |
| | one column per question | Flat label, for browsing and filtering: a `ClassLabel` for choice, score, and yes/no noul questions; a float for graded noul (`incident_real`, `random_*`); the option text for open numeric choices (`amount_due`, `final_balance`, `finish_time`). Null when the state does not ask that question (`arithmetic` only). | |
|
|
| States are unique within a split, and validation/test states never occur in |
| train. |
|
|
| ## Use |
|
|
| As a multi-question Jev request, send `{"state": row["state"], "questions": |
| json.loads(row["questions"])}` (parsing the state first when it is JSON) and |
| compare with `row["answers"]`. |
| The same rows are included, grouped by state, in |
| [`tasksource/tasksource-jev-typed-decisions`](https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions). |
|
|
| ## Reproduction |
|
|
| Generation is deterministic (row `i` of a split is seeded by `task:split:i`). |
| From a [tasksource](https://github.com/sileod/tasksource) checkout: |
|
|
| ```bash |
| PYTHONPATH=.:src python scripts/build_procedural_jev.py --output build/procedural-typed-decisions --upload |
| ``` |
|
|
| Generators live in `src/tasksource/jev/procedural/`. |
|
|
