Datasets:
File size: 12,394 Bytes
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license: mit
language:
- ko
- en
pretty_name: NOLLI
size_categories:
- 1K<n<10K
task_categories:
- question-answering
tags:
- reasoning
- logic
- puzzles
- korean
configs:
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data_files:
- split: test
path: array_formula/test-*
- config_name: causal_dag
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path: causal_dag/test-*
- config_name: cipher
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path: cipher/test-*
- config_name: cryptarithmetic
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path: cryptarithmetic/test-*
- config_name: inequality
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- split: test
path: inequality/test-*
- config_name: jamo
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path: jamo/test-*
- config_name: kinship
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- split: test
path: kinship/test-*
- config_name: korean_units
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path: korean_units/test-*
- config_name: minesweeper
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path: minesweeper/test-*
- config_name: number_baseball
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- split: test
path: number_baseball/test-*
- config_name: saju
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path: saju/test-*
- config_name: sat_puzzles
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path: sat_puzzles/test-*
- config_name: sudoku
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path: sudoku/test-*
- config_name: time
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path: time/test-*
- config_name: yacht_dice
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path: yacht_dice/test-*
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- name: task
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- name: language
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- name: difficulty
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- name: question
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- name: answer
dtype: string
- name: solution
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- name: language
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- config_name: cryptarithmetic
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- name: language
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- name: difficulty
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- config_name: jamo
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- name: language
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- name: difficulty
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- name: difficulty
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- config_name: number_baseball
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---
# NOLLI
**NOLLI (논리)** is a benchmark of rule-based logical puzzles in **Korean and English**, designed with
difficulty calibration in mind. It covers **15 task types x 3 difficulty tiers (easy / medium / hard)
x 100 examples** each, for a total of **7,500 examples** (EN 3,000 + KO 4,500).
All examples are programmatically generated with solver-verified answers. If you need more data,
the generators in the GitHub repository can produce additional examples for any task, language,
and difficulty tier.
📄 Paper: [https://arxiv.org/abs/2608.04397](https://arxiv.org/abs/2608.04397)\
💻 Code: [https://github.com/HAE-RAE/NOLLI](https://github.com/HAE-RAE/NOLLI)
## Tasks
Tasks follow a three-level cross-lingual spectrum: **direct translations**, **script adaptations**,
and **Korean-only** tasks.
### 1. Direct Translations (8 tasks · EN + KO)
Same generators and parameters in both languages, so performance gaps isolate presentation language.
| task | description | en (easy/med/hard) | ko (easy/med/hard) | total |
|---|---|---|---|---|
| `array_formula` | Apply row/column aggregation formulas (SUM/MEAN/MAX/...) to a 2D array in sequence | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `causal_dag` | Infer event-propagation time through a causal graph with per-edge delays | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `inequality` | CSP: place numbers 1..N satisfying inequality constraints (unique solution) | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `minesweeper` | Minesweeper with minimal hints preserving a unique solution | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `number_baseball` | Infer a hidden N-digit number from Strike/Ball hints | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `sat_puzzles` | Boolean satisfiability (CNF) framed as a natural-language scenario | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `sudoku` | 9x9 Sudoku with guaranteed-unique solutions | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `yacht_dice` | Assign 12 dice rolls to 12 scoring categories to maximize total score | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
### 2. Script Adaptations (2 tasks · EN + KO)
Not translation-equivalent: English uses Roman letters; Korean uses Hangul *jamo*.
Calibrated independently per language.
| task | description | en (easy/med/hard) | ko (easy/med/hard) | total |
|---|---|---|---|---|
| `cipher` | Decode stacked ciphers (Substitution, Vigenere, Reverse, Playfair, Transposition) | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
| `cryptarithmetic` | Letters-for-digits arithmetic (SEND+MORE=MONEY style); Korean maps *jamo* groups | 100 / 100 / 100 | 100 / 100 / 100 | 600 |
### 3. Korean-Only (5 tasks · KO)
No English counterpart — built on structures specific to the Korean language and culture
(kinship terminology, the sexagenary calendar, traditional units, Hangul orthography).
| task | description | ko (easy/med/hard) | total |
|---|---|---|---|
| `jamo` | Decompose a Hangul syllable into 초성/중성/종성, shift 초성, recompose | 100 / 100 / 100 | 300 |
| `kinship` | Infer a Korean kinship term from a chain of family relationships (26-way multiple choice) | 100 / 100 / 100 | 300 |
| `korean_units` | Convert traditional Korean units (평·마지기·되·자·냥, ...) with randomized rates | 100 / 100 / 100 | 300 |
| `saju` | Compute the four 사주(四柱) pillars from a birth date/time | 100 / 100 / 100 | 300 |
| `time` | Korean calendar reasoning: holiday anchor + relative-day expression → date / 60갑자 일진 | 100 / 100 / 100 | 300 |
## Usage
```python
from datasets import load_dataset
# one task
ds = load_dataset("HAERAE-HUB/NOLLI", "sudoku", split="test")
# filter by language / difficulty (both are columns)
ko_hard = ds.filter(lambda x: x["language"] == "ko" and x["difficulty"] == "hard")
```
## Structure
Each config corresponds to one task. All configs share a single `test` split and the same schema
(the `choices` column exists only in `kinship`):
| column | type | description |
|---|---|---|
| `id` | str | unique example id, e.g. `sudoku_ko_0000` |
| `task` | str | task name (same as config name) |
| `language` | str | `ko` or `en` |
| `difficulty` | str | `easy` / `medium` / `hard` |
| `question` | str | full prompt for the model |
| `choices` | str | JSON-encoded answer choices — only present in the `kinship` config |
| `answer` | str | gold answer |
| `solution` | str | step-by-step reference solution |
## Citation
```bibtex
@misc{choi2026nolli,
title={NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap},
author={Dasol Choi and Joonyong Park and Daegon Yu and Soo Yong Kim and Youngsook Song and Seunghyeok Hong},
year={2026},
eprint={2608.04397},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2608.04397},
}
```
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