Update README.md

README.md

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 # KCL
 
-This repository contains the **Korean Canonical Legal Benchmark (KCL)** dataset
+This repository hosts the **Korean Canonical Legal Benchmark (KCL)** datasets.  
 
-[![Github](https://img.shields.io/badge/GitHub-KCL-blue?style=flat&logo=github)](https://github.com/lbox-kr/kcl) [![Paper](https://img.shields.io/badge/arXiv-1234.1234-red?style=flat&logo=arxiv&logoColor=red)](https://arxiv.org/abs/1234.1234)
+Evaluation Code Repository [![Github](https://img.shields.io/badge/GitHub-KCL-blue?style=flat&logo=github)](https://github.com/lbox-kr/kcl)
 
-Our benchmark dataset is licensed under the xx License.
+For more information, please refer to our paper [![Paper](https://img.shields.io/badge/arXiv-1234.1234-red?style=flat&logo=arxiv&logoColor=red)](https://arxiv.org/abs/1234.1234)
+
+## Why KCL?
+
+KCL is designed to **disentangle knowledge coverage from evidence-grounded reasoning**.   
+
+KCL supports two complementary evaluation axes:
+1. **Knowledge Coverage** — performance without extra context (`vanilla` setting).  
+2. **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.
+
+#### 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 multiple-choice)  
+  - 283 questions, 1,103 supporting precedents.
+ 
+## Usage
+
+
+```python
+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
+
+![스크린샷 2025-10-20 오후 5.20.30](https://cdn-uploads.huggingface.co/production/uploads/6364b581a53b71b7a1b62364/fEb_RSiHVCGT6v0V7A13B.png)
+
+## 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
+
+![스크린샷 2025-10-20 오후 5.20.24](https://cdn-uploads.huggingface.co/production/uploads/6364b581a53b71b7a1b62364/OmiTG5Tv6pN2PRtiBhspy.png)
+
+## Citation
+```bibtex
+@misc{kcl,
+  title     = {Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities},
+  author    = {Hongseok Oh and Wonseok Hwang and Kyoung-Woon On},
+  year      = {2025}
+}
+```
+
+## LICENSE
+
+Our benchmark dataset is licensed under the xx License.   
 The problems we processed were sourced from the [Korean Bar Exam](https://www.moj.go.kr/moj/405/subview.do), and they are released under the [KOGL Type 1](https://www.kogl.or.kr/info/licenseType1.do) license.