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Datasets:
shalnark01
/
policyproof-track2a-benchmark

Tasks:
Text Generation
Languages:
English
Norwegian
Arabic
Size:
n<1K
Tags:
Synthetic
safety-evaluation
prompt-injection
grounded-qa
structured-output
multilingual
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
category: string
context: string
expected_answer: string
expected_source_id: string
human_reviewed: bool
id: string
language: string
model_evaluated: bool
question: string
should_abstain: bool
split: string
topic: string
used_for_training: bool
training_data: bool
model_predictions_available: bool
language_counts: struct<en: int64, no: int64, ar: int64>
  child 0, en: int64
  child 1, no: int64
  child 2, ar: int64
attack_family_counts: struct<control: int64, role_spoof: int64, source_spoof: int64, encoded_instruction: int64, language_ (... 45 chars omitted)
  child 0, control: int64
  child 1, role_spoof: int64
  child 2, source_spoof: int64
  child 3, encoded_instruction: int64
  child 4, language_override: int64
  child 5, unsupported_question: int64
curatorkit_executed: bool
dataset_sha256: string
status: string
independent_human_review: bool
cases: int64
curated_cases: int64
evaluation_run: bool
warning: string
curator_export_sha256: string
to
{'status': Value('string'), 'cases': Value('int64'), 'dataset_sha256': Value('string'), 'language_counts': {'en': Value('int64'), 'no': Value('int64'), 'ar': Value('int64')}, 'attack_family_counts': {'control': Value('int64'), 'role_spoof': Value('int64'), 'source_spoof': Value('int64'), 'encoded_instruction': Value('int64'), 'language_override': Value('int64'), 'unsupported_question': Value('int64')}, 'independent_human_review': Value('bool'), 'model_predictions_available': Value('bool'), 'evaluation_run': Value('bool'), 'training_data': Value('bool'), 'warning': Value('string'), 'curated_cases': Value('int64'), 'curator_export_sha256': Value('string'), 'curatorkit_executed': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              category: string
              context: string
              expected_answer: string
              expected_source_id: string
              human_reviewed: bool
              id: string
              language: string
              model_evaluated: bool
              question: string
              should_abstain: bool
              split: string
              topic: string
              used_for_training: bool
              training_data: bool
              model_predictions_available: bool
              language_counts: struct<en: int64, no: int64, ar: int64>
                child 0, en: int64
                child 1, no: int64
                child 2, ar: int64
              attack_family_counts: struct<control: int64, role_spoof: int64, source_spoof: int64, encoded_instruction: int64, language_ (... 45 chars omitted)
                child 0, control: int64
                child 1, role_spoof: int64
                child 2, source_spoof: int64
                child 3, encoded_instruction: int64
                child 4, language_override: int64
                child 5, unsupported_question: int64
              curatorkit_executed: bool
              dataset_sha256: string
              status: string
              independent_human_review: bool
              cases: int64
              curated_cases: int64
              evaluation_run: bool
              warning: string
              curator_export_sha256: string
              to
              {'status': Value('string'), 'cases': Value('int64'), 'dataset_sha256': Value('string'), 'language_counts': {'en': Value('int64'), 'no': Value('int64'), 'ar': Value('int64')}, 'attack_family_counts': {'control': Value('int64'), 'role_spoof': Value('int64'), 'source_spoof': Value('int64'), 'encoded_instruction': Value('int64'), 'language_override': Value('int64'), 'unsupported_question': Value('int64')}, 'independent_human_review': Value('bool'), 'model_predictions_available': Value('bool'), 'evaluation_run': Value('bool'), 'training_data': Value('bool'), 'warning': Value('string'), 'curated_cases': Value('int64'), 'curator_export_sha256': Value('string'), 'curatorkit_executed': Value('bool')}
              because column names don't match

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PolicyProof AI — multilingual safety benchmark (research prototype)

Synthetic research dataset prepared for owner-authorized release. Independent human label review and model improvement are NOT established. The underlying policies, questions and target answers are invented. There is no customer data or real financial policy.

What's inside

There are TWO distinct datasets, which must never be conflated:

  • 48 evaluated development cases: 36 supported fictional policy questions, nine missing-evidence abstention cases and three prompt-injection probes. Each of English, Norwegian and Arabic has 16 questions. Their original recorded CohereLabs/tiny-aya-global base-model responses were evaluated through real AuditKIT 1.1.3. CuratorKIT 0.1.22 processed and preserved all 48 original case tags and reference labels.
  • 72 NEW unscored challenge cases: 12 each of normal controls, forged system-role instructions, fake policy sources, encoded instructions, multilingual override instructions and missing-evidence queries. Each language has 24 cases. CuratorKIT structurally curated all 72, but NO MODEL has answered them and NO HUMAN has independently reviewed their semantic labels.

Never describe these 120 cases as a single 120-response model evaluation. The second set is unscored and labels are synthetic.

Provenance

  • Original 48-case benchmark SHA-256: d61e59557c62e486afc49e40e5d4daff0c8896429ac2b71d04936b07a49d3a4c
  • CuratorKIT 48-case Alpaca export SHA-256: 7a3fbeec5eae88493f9d60f9cc0e2c588ec506b01e58dc5e57edd987508bc2ff
  • New 72-case JSONL SHA-256: cdd5cf4e54caa6c79ce1f0cd5bd0f05344f0dc22039a2ad56a3c1c0ee2d32cd1
  • New 72-case CuratorKIT export SHA-256: cf8b226ebff5a6a5e53e3132cec9581844d4d6fd92316885c59cbd535ec624df
  • Tool versions: CuratorKIT 0.1.22, AuditKIT 1.1.3, CPU Python 3.11
  • Full 20-file native evidence archive is available in the public CI run.
  • Source: https://github.com/betterlifestyle04-commits/PolicyProof-AI

Results on the 48 inspected development cases only

Metric Unmodified model, recorded outputs
Strict exact full task success 8 / 48 (16.67%)
Complete required 3-key JSON format 33 / 48 (68.75%)
Raw expected source ID on answerable cases 38 / 39 (97.44%)
Strict valid expected source on answerable cases 20 / 39 (51.28%)
Exact answer-text match on answerable cases 0 / 39 (0%)
Correct abstention on unsupported cases 8 / 9 (88.89%)
Substantive unsupported claim 1 / 9 (11.11%)

Citation-ID matching alone does not verify factual grounding. Exact text matching does not recognize valid paraphrases. The model revision hash was not recorded in the original saved report.

Safety, labeling and methodology caveats

  • All policy documents and cases are synthetic, small and templated. Four challenge source policies are intentionally repeated across attack variants; these cases are correlated.
  • Questions are multilingual but evidence paragraphs remain English, confounding translation and evidence reasoning.
  • Only three original prompt-injection cases have model responses, so jailbreak robustness is not established.
  • Both sets have template-derived gold answers and lack independent bilingual label adjudication.
  • Neither dataset is real-world regulatory guidance, a financial product policy, a production safety test or a claim of model improvement.
  • An additional deterministic output guard may change JSON schema and abstention formatting but is not SafeTune model steering or model fine-tuning.
  • Do not use either evaluation split for model fitting, hyperparameter tuning or any claimed held-out test once published.
  • The gated original Tiny Aya weights are NOT licensed or distributed by this dataset.

Rights and publishing

Publisher-selected license: cc-by-4.0. The publisher is responsible for having sufficient rights to release these original synthetic examples. Original gated Tiny Aya weights and any unverified trained adapter are not included.
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