tasksource-jev-typed-decisions

One million human-labeled decisions from 500+ tasks, in one format for models that read their answer criteria at runtime.

Most instruction data teaches a model to write. This dataset teaches it to decide: given a state and a question, pick among the options it is handed, rate on a scale it is handed, or give a calibrated probability. The options change from row to row, so a model has to read them rather than memorize a label set.

Why use it

• Real supervision. Labels, ratings, and annotator votes come from established datasets, not a teacher model. Every row names its source.
• Breadth. Over 300 dataset families: NLI and reasoning, QA and commonsense, sentiment, intent and topic, toxicity and safety, preference pairs, fact checking, entity tagging, and dozens of languages. GLUE, SuperGLUE, HellaSwag, PIQA, ScienceQA, Banking77, CoNLL-2003, MasakhaNEWS, HelpSteer, ChaosNLI, and many more, with no task allowed to dominate.
• Three decision types in one schema. choice (pick one option), score (an ordered scale), and noul (the probability that a statement is true). Soft targets are kept wherever the source has votes or ratings.
• Built so position, repeated eval data, and question choice give nothing away.
  • Multiple-choice options are shuffled per row, so the answer's position carries no signal.
  • Validation and test rows whose content appears in train are removed.
  • Derived questions are chosen without looking at their answers.
  • Annotations were reviewed task by task. Inverted, unanswerable, and garbled labels were fixed or dropped.
• Multi-question states. Related decisions share a group_id and can be asked together. Packed states test reasoning over several items at once, and procedural-typed-decisions adds exact counting, arithmetic, retrieval, and state tracking.

Quick start

from datasets import load_dataset

ds = load_dataset("tasksource/tasksource-jev-typed-decisions")
row = ds["train"][0]
print(row["state"], row["question"], row["options"], row["target"])

{"state": "My body cast a shadow over the grass. What was the cause of this?",
 "question": "Choose the criterion that best answers the question.",
 "kind": "choice", "options": ["The sun was rising.", "The grass was cut."],
 "target": [1.0, 0.0], "source": "super_glue/copa"}

Format

field	meaning
state	The text to decide about
question	What to decide
kind	choice, score, or noul
options	Runtime criteria; empty for noul
target	Distribution over options, or [p] for noul
id, group_id, question_id	Link decisions over the same source example
source, split, variant	Originating task, original split, and recast variant

Splits: 1,000,000 train, 15,000 validation (dev in split), and 15,000 test, following each source's own train/dev/test splits where it has them.

How it is built

• Canonical recasts. Each Tasksource task is converted deterministically.
  • Criteria are the source's own label names and answer options.
  • Multiple-choice rows keep every option in a per-row order.
  • A final "all/none of the above" reads "all/none of the other options".
  • Options that cite other options by letter or number keep their order.
  • The question is the task's own when its inputs alone do not say what to predict ("What stance does the tweet take on feminism?"), and a generic instruction otherwise. Label-verification and packed questions carry it too.
• Variants. Low-frequency, deterministic variants cover label verification as noul, criterion order, and instruction wording.
• Packing. Up to 10% of each classification task's examples are packed, two to four at a time, into packed_derived states. Their questions (an item's label, agreement, existence, counts) follow exactly from the gold labels.
• Mixing. Formats get fixed shares of the train rows (47% classification, 30% multiple choice, 3% token labeling, 10% graded, 10% procedural). Within a format, dataset families get equal shares, scaled by hand-set weights (more for adversarial NLI, long documents and preference pairs; less for templated probes). Related questions are kept together.
  • The first 1,000 train rows are interleaved to show variety in the Dataset Viewer; the rest is shuffled.
  • Evaluation benchmarks (BIG-bench, MMLU, BLiMP, MATH test, ...) are left out so they stay clean for evaluation.
• Audit trail. The source mix, failed source list, and build manifest ship with the data.
• Reproducible. The build runbook rebuilds the release from Tasksource's task catalog.

License and scope

Tasksource harmonizes datasets from many publishers; their original licenses and terms still apply, hence license: other. This recast is independent of TypeSafe and OpenJev.

Citation

@inproceedings{sileo-2024-tasksource,
  title = {tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework},
  author = {Sileo, Damien},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  pages = {15655--15684},
  url = {https://aclanthology.org/2024.lrec-main.1361/}
}