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README.md
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# PALETTE-BENCH-KO — Korean Enterprise Document Benchmark
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**Version:** 0.1 (seed) · **License:** CC BY 4.0 · **Language:** Korean (ko)
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## What this is
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The first public benchmark for **Korean enterprise document work** — the drafting,
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extraction, and compliance tasks that office staff actually do, which existing Korean
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benchmarks (KMMLU, HAE-RAE, KoBALT, LogicKor) do not cover. A landscape sweep (2026-08)
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found no public benchmark testing 공문서/품의서 drafting, 회의록→결정 extraction, or
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business-register control; the nearest neighbors test QA/RAG (Allganize) or law (KCL),
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not workflow. Demand is documented: Seoul Metropolitan Government's own survey put
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document drafting (23%) + summarization (20%) as civil servants' top AI needs.
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## Task families (6)
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| Family | Task | Scoring | Deterministic? |
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|---|---|---|---|
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| `minutes_to_decision` | 회의록 → {decision, owner, due_date} 구조화 추출 | field F1 | ✅ yes |
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| `dart_comprehension` | 공시 발췌 → 사실 QA + 수치 추론 | token-F1 / exact | ✅ yes |
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| `gongmun_drafting` | 브리프 → 공문서 초안 | rubric (LLM judge) | ⚪ judge |
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| `pumui_drafting` | 시나리오 → 품의서/기안 | rubric (LLM judge) | ⚪ judge |
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| `email_register` | 관계·의도 → 비즈니스 이메일 (존댓말 register) | rubric (LLM judge) | ⚪ judge |
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| `policy_compliance` | 작업 + 조직 정책 → 준수/거부/플래그 판단 | compliance rubric | ⚪ judge |
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## Size
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**v0.1 seed: 24 items** (4 per family; 6 easy / 12 medium / 6 hard). This is a
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**reference seed**, released to establish the format, scoring, and the axis. The
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production release (v1.0) expands to ~1.5–3k items with native-expert authoring and a
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rotated held-out split — see [README.md](README.md) → roadmap.
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## Provenance & honesty
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- All items are **synthetic** (authored for this benchmark) or **DART-style** (modeled on
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the public disclosure register, not copied from a specific filing). No customer data, no
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scraped copyrighted documents. This keeps the set redistributable under CC BY 4.0.
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- The `policy_compliance` family includes the exact PIPA-scoped data-handling dilemma a
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governance product must get right (item `pc-003`), and the drafting families include
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the residency-justification and sensitive-incident cases (`pm-002`, `pm-003`, `gm-003`).
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- The harness **never fabricates a score**: rubric items with no judge configured are
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reported `UNSCORED`, not given a number. Deterministic families need no LLM.
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## Fields per item
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`id` · `family` · `task_class` · `difficulty` · `provenance` · `input` (Korean) ·
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`reference` **or** `reference_rubric` · `scoring` · optional `notes` / `max_score`.
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## How to evaluate
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```bash
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# 1. run your model on each item's `input`, collect {id: output} → preds.json
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# 2. score:
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cd harness
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python evaluate.py --data ../data --preds preds.json --judge none # deterministic only
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python evaluate.py --data ../data --preds preds.json --judge anthropic # full (needs key)
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```
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Deterministic families (`minutes_to_decision`, `dart_comprehension`) score with stdlib
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only. Rubric families use a pluggable LLM judge (OpenAI / Anthropic adapters included).
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## Intended use & limitations
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- **Use**: measuring Korean enterprise-document capability of LLMs; a held-out slice for
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adapter evaluation; a citable axis for a model release.
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- **Limitations (v0.1)**: 24 items is a seed, not a statistically powerful test set;
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rubric scoring inherits judge-model bias (report the judge used); Korean items are
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authored by the release team and await native-expert review for v1.0. Do **not** report
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v0.1 numbers as a definitive leaderboard — report them as a seed baseline.
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## Citation
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> PALETTE-BENCH-KO: A Korean Enterprise Document Benchmark (v0.1 seed), 2026.
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> Released under CC BY 4.0.
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