---
language:
- en
- zh
license: mit
pretty_name: LegalScope
task_categories:
- question-answering
- text-generation
tags:
- legal
- legal-reasoning
- benchmark
- llm-evaluation
- exam-to-case-transfer
- chinese-civil-judgments
- text
size_categories:
- n<1K
---

# LegalScope

LegalScope is a public metadata preview for a benchmark studying whether strong
public legal-exam performance transfers to real-case legal analysis.

The benchmark pairs scalable public legal-exam tasks with lawyer-reviewed,
de-identified Chinese civil-judgment analysis. This Hugging Face dataset page is
kept intentionally small and public-safe: it mirrors the public metadata from the
GitHub repository and does not publish the full benchmark workbook, full prompts,
reference answers, model outputs, human review sheets, paper draft, or
non-de-identified legal source materials.

GitHub repository: https://github.com/EternWang/LegalScope

## Benchmark Snapshot

| Component | Count |
| --- | ---: |
| Public legal-exam questions | 868 |
| Real-case issue-stance prompts | 76 |
| Total dataset items | 944 |
| De-identified Chinese civil judgments | 15 |
| Legal issues extracted from judgments | 38 |
| Model groups evaluated | 20 |
| Public-exam model responses | 17,360 |
| Real-case model responses | 1,520 |
| Total dataset model responses | 18,880 |
| Human-validation responses | 1,800 |

## Files

```text
data/metadata/dataset_summary.json
data/metadata/model_groups.csv
data/metadata/source_composition.csv
```

`dataset_summary.json` contains the public release boundary, benchmark module
counts, aggregate response counts, and transfer-summary metrics.

`model_groups.csv` lists the 20 evaluated model groups.

`source_composition.csv` summarizes public-exam jurisdiction/domain composition
and real-case legal-domain composition.

## Evaluation Design

LegalScope has two tracks:

- **Public legal exams:** open-ended public legal-exam questions scored with a
  reference-aware 0-4 answer-match protocol.
- **Chinese real cases:** issue-stance prompts derived from de-identified
  Chinese civil judgments, scored across citation relevance, constraint
  extraction, and argument validity.

Human validation covers 80 public-exam items and 10 real-case prompts across the
same 20 model groups, for 1,800 human-validation responses.

## Intended Use

This public preview is intended for:

- understanding the benchmark design and release boundary;
- inspecting public-safe metadata and source composition;
- citing the public LegalScope artifact;
- coordinating access to the private benchmark materials where appropriate.

It is not intended for legal advice, legal decision support, training legal
decision systems, or reconstructing withheld private data.

## Public Release Boundary

This dataset repository does **not** include:

- the full benchmark workbook;
- complete prompt matrices;
- reference answers;
- model-output matrices;
- human review sheets or adjudication notes;
- paper draft, review PDF, or LaTeX source;
- non-de-identified judgments or private source documents.

The public files are sufficient to document scope, counts, metadata, and release
boundaries, but they are not sufficient to reconstruct the full benchmark.

## Citation

If you use this public preview, cite the GitHub repository and the released paper
once a stable paper citation is available.

```bibtex
@misc{wang2026legalscope,
  title        = {LegalScope: Measuring Exam-to-Case Transfer in LLM Legal Reasoning},
  author       = {Wang, Hongyu},
  year         = {2026},
  howpublished = {\url{https://github.com/EternWang/LegalScope}},
  note         = {Public metadata preview}
}
```

## Disclaimer

LegalScope is a research benchmark for model evaluation. It is not legal advice,
a legal research product, or a substitute for jurisdiction-specific legal review.