Datasets:

lbox
/

kcl

dataset_info:
  - config_name: kcl_essay
    features:
      - name: meta
        dtype: string
      - name: question
        dtype: string
      - name: rubrics
        list: string
      - name: score
        dtype: int64
      - name: supporting_precedents
        list: string
    splits:
      - name: test
        num_bytes: 8516472
        num_examples: 169
    download_size: 3250635
    dataset_size: 8516472
  - config_name: kcl_mcqa
    features:
      - name: meta
        dtype: string
      - name: question
        dtype: string
      - name: A
        dtype: string
      - name: B
        dtype: string
      - name: C
        dtype: string
      - name: D
        dtype: string
      - name: E
        dtype: string
      - name: label
        dtype: string
      - name: supporting_precedents
        list: string
    splits:
      - name: test
        num_bytes: 13687302
        num_examples: 283
    download_size: 5988971
    dataset_size: 13687302
configs:
  - config_name: kcl_essay
    data_files:
      - split: test
        path: kcl_essay/test-*
  - config_name: kcl_mcqa
    data_files:
      - split: test
        path: kcl_mcqa/test-*
task_categories:
  - question-answering
language:
  - ko
tags:
  - legal
size_categories:
  - n<1K
license: cc-by-nc-4.0

KCL

This repository hosts the Korean Canonical Legal Benchmark (KCL) datasets.

Why KCL?

KCL is designed to disentangle knowledge coverage from evidence-grounded reasoning.

KCL supports two complementary evaluation axes:

Knowledge Coverage: performance without extra context.
Evidence-Grounded Reasoning: performance with per-question supporting precedents provided in-context.

For essay questions, KCL further offers instance-level rubrics to enable LLM-as-a-Judge automated scoring.

For more information, please refer to our paper

Intended Uses

Separating knowledge vs. reasoning by comparing vanilla and with-precedent settings.
Legal RAG research using question-aligned gold precedents to establish retriever/reader upper bounds.
Fine-grained feedback via rubric-level diagnostics on essay outputs.

Components

KCL-Essay (open-ended generation)
- 169 questions, 550 supporting precedents, 2,739 instance-level rubrics.
KCL-MCQA (five-choice question answering)
- 283 questions, 1,103 supporting precedents.

Usage

from datasets import load_dataset

# Essay subset
kcl_essay = load_dataset("lbox/kcl", "kcl_essay", split="test")
# MCQA subset
kcl_mcqa = load_dataset("lbox/kcl", "kcl_mcqa", split="test")

KCL-Essay

Dataset Fields

meta: Metadata such as exam year, subject, and question id.
question: The full prompt presented to models.
rubrics: Instance-level grading rubrics for automated evaluation.
score: The original point value assigned in the official bar exam (reflecting difficulty).
supporting_precedents: Question-aligned court decisions required to solve the problem.

Results

KCL-MCQA

Dataset Fields

meta: Metadata about the source exam item.
question: The full prompt presented to models.
A–E: Five answer options.
label: The gold answer option letter (one of 'A'|'B'|'C'|'D'|'E').
supporting_precedents: Question-aligned court decisions required to solve the problem.

Results

Citation

@inproceedings{
oh2026korean,
title={Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of {LLM}s' Legal Reasoning Capabilities},
author={Hongseok Oh and Wonseok Hwang and Kyoung-Woon On},
booktitle={19th Conference of the European Chapter of the Association for Computational Linguistics},
year={2026},
url={https://openreview.net/forum?id=Dw0sFP4l5s}
}

LICENSE

The KCL dataset is derived from the Korean Bar Exam materials, which are released under the KOGL Type 1 license by the Government of the Republic of Korea.

This dataset was developed solely for academic and research purposes by LBOX. It is not sponsored, endorsed, or affiliated with the Ministry of Justice.

The case-analysis evaluation guidelines included in this dataset were independently created by LBOX and do not originate from any public institution. These contributions constitute original works authored by LBOX and are incorporated into the dataset under the terms described below.

Unless otherwise specified, the KCL dataset as a whole is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0 license).

LBOX, 2026.