The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
id: string
family: string
task_class: string
difficulty: string
provenance: string
input: string
reference_rubric: struct<register: string, structure: string, tone_control: string, completeness: string>
child 0, register: string
child 1, structure: string
child 2, tone_control: string
child 3, completeness: string
scoring: string
max_score: int64
reference: null
accept: null
notes: string
to
{'id': Value('string'), 'family': Value('string'), 'task_class': Value('string'), 'difficulty': Value('string'), 'provenance': Value('string'), 'input': Value('string'), 'reference': Json(decode=True), 'accept': Json(decode=True), 'scoring': Value('string'), 'notes': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
family: string
task_class: string
difficulty: string
provenance: string
input: string
reference_rubric: struct<register: string, structure: string, tone_control: string, completeness: string>
child 0, register: string
child 1, structure: string
child 2, tone_control: string
child 3, completeness: string
scoring: string
max_score: int64
reference: null
accept: null
notes: string
to
{'id': Value('string'), 'family': Value('string'), 'task_class': Value('string'), 'difficulty': Value('string'), 'provenance': Value('string'), 'input': Value('string'), 'reference': Json(decode=True), 'accept': Json(decode=True), 'scoring': Value('string'), 'notes': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PALETTE-BENCH-KO — Korean Enterprise Document Benchmark
Version: 0.1 (seed) · License: CC BY 4.0 · Language: Korean (ko)
What this is
The first public benchmark for Korean enterprise document work — the drafting, extraction, and compliance tasks that office staff actually do, which existing Korean benchmarks (KMMLU, HAE-RAE, KoBALT, LogicKor) do not cover. A landscape sweep (2026-08) found no public benchmark testing 공문서/품의서 drafting, 회의록→결정 extraction, or business-register control; the nearest neighbors test QA/RAG (Allganize) or law (KCL), not workflow. Demand is documented: Seoul Metropolitan Government's own survey put document drafting (23%) + summarization (20%) as civil servants' top AI needs.
Task families (6)
| Family | Task | Scoring | Deterministic? |
|---|---|---|---|
minutes_to_decision |
회의록 → {decision, owner, due_date} 구조화 추출 | field F1 | ✅ yes |
dart_comprehension |
공시 발췌 → 사실 QA + 수치 추론 | token-F1 / exact | ✅ yes |
gongmun_drafting |
브리프 → 공문서 초안 | rubric (LLM judge) | ⚪ judge |
pumui_drafting |
시나리오 → 품의서/기안 | rubric (LLM judge) | ⚪ judge |
email_register |
관계·의도 → 비즈니스 이메일 (존댓말 register) | rubric (LLM judge) | ⚪ judge |
policy_compliance |
작업 + 조직 정책 → 준수/거부/플래그 판단 | compliance rubric | ⚪ judge |
Size
v0.1 seed: 24 items (4 per family; 6 easy / 12 medium / 6 hard). This is a reference seed, released to establish the format, scoring, and the axis. The production release (v1.0) expands to ~1.5–3k items with native-expert authoring and a rotated held-out split — see README.md → roadmap.
Provenance & honesty
- All items are synthetic (authored for this benchmark) or DART-style (modeled on the public disclosure register, not copied from a specific filing). No customer data, no scraped copyrighted documents. This keeps the set redistributable under CC BY 4.0.
- The
policy_compliancefamily includes the exact PIPA-scoped data-handling dilemma a governance product must get right (itempc-003), and the drafting families include the residency-justification and sensitive-incident cases (pm-002,pm-003,gm-003). - The harness never fabricates a score: rubric items with no judge configured are
reported
UNSCORED, not given a number. Deterministic families need no LLM.
Fields per item
id · family · task_class · difficulty · provenance · input (Korean) ·
reference or reference_rubric · scoring · optional notes / max_score.
How to evaluate
# 1. run your model on each item's `input`, collect {id: output} → preds.json
# 2. score:
cd harness
python evaluate.py --data ../data --preds preds.json --judge none # deterministic only
python evaluate.py --data ../data --preds preds.json --judge anthropic # full (needs key)
Deterministic families (minutes_to_decision, dart_comprehension) score with stdlib
only. Rubric families use a pluggable LLM judge (OpenAI / Anthropic adapters included).
Intended use & limitations
- Use: measuring Korean enterprise-document capability of LLMs; a held-out slice for adapter evaluation; a citable axis for a model release.
- Limitations (v0.1): 24 items is a seed, not a statistically powerful test set; rubric scoring inherits judge-model bias (report the judge used); Korean items are authored by the release team and await native-expert review for v1.0. Do not report v0.1 numbers as a definitive leaderboard — report them as a seed baseline.
Citation
PALETTE-BENCH-KO: A Korean Enterprise Document Benchmark (v0.1 seed), 2026. Released under CC BY 4.0.
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