---
dataset_info:
  features:
  - name: _id
    dtype: int64
  - name: lai_yuan
    dtype: string
  - name: lai_yuan_id
    dtype: int64
  - name: biao_ti
    dtype: string
  - name: fa_yuan
    dtype: string
  - name: fa_ting_guan_dian
    dtype: string
  - name: shen_li_jie_duan
    dtype: string
  - name: ri_qi_s31
    dtype: string
  - name: ri_q1_s41
    dtype: string
  - name: fu_yan
    dtype: string
  - name: an_you
    sequence: string
  - name: dang_shi_ren
    sequence: string
  - name: an_jian_lei_xing
    dtype: string
  - name: wen_shu_bian_hao
    dtype: string
  - name: shen_li_qing_kuang
    dtype: string
  - name: guan_jian_ci
    sequence: string
  - name: ting_shen_guo_cheng
    dtype: string
  - name: ren_yuan
    dtype: string
  - name: pan_jue_jie_guo
    dtype: string
  - name: label
    struct:
    - name: classification
      dtype: string
    - name: reason
      dtype: string
  - name: charge
    dtype: string
  - name: concept
    dtype: string
  splits:
  - name: train
    num_bytes: 51203688
    num_examples: 2914
  - name: test
    num_bytes: 51902129
    num_examples: 2928
  download_size: 42728249
  dataset_size: 103105817
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- text-classification
- text-generation
language:
- zh
tags:
- legal
pretty_name: Legal Concept Entailment
size_categories:
- 1K<n<10K
---

# Legal Concept Entailment Dataset

## Dataset Description

This dataset is released as part of our ACL 2025 main conference paper: [Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation](https://arxiv.org/abs/2501.01743). It is designed for the **Legal Concept Entailment (LCE)** task, which evaluates the quality of legal interpretations by assessing a model's ability to understand and apply vague legal concepts to specific, unseen cases.

The core idea is that a high-quality interpretation of a legal concept should improve a model's performance on determining whether that concept applies to the fact of an unseen case. This dataset serves as a benchmark for this evaluation.

## Legal Concept Entailment

The LCE task is a dual-part task designed to test a model's understanding of legal concepts.

* **Binary Classification**: Given the fact description of a case and a relevant vague legal concept, the model must predict a binary label (`Yes`/`No`) indicating whether the concept applies to the facts of the case.
* **Reason Generation**: The model must also generate a textual reason explaining its classification decision. The quality of this reason is evaluated for consistency against a "gold" reason derived from the court's actual judgment.

An example of the task is shown below:
![LCE Task Example](https://arxiv.org/html/2501.01743v3/x2.png)

## Languages

The data is in **Chinese (zh)**, as it is sourced from [China Judgments Online](https://wenshu.court.gov.cn/).

## Dataset Structure
### Data Splits
The dataset is divided into two splits:
- `train`: Contains 2914 instances for generating legal concept interpretation.
- `test`: Contains 2928 instances for testing.
### Data Instances

Each instance in the dataset corresponds to a legal case and a specific legal concept. 

- `label`: Contains the classification label (`Yes` or `No`) and the gold reason for the classification extracted from the court view. 
- `concept`: The vague legal concept being evaluated.
- `ting_shen_guo_cheng`: The fact description of the case.
- `fa_ting_guan_dian`: The court view on the case, which includes the legal interpretation of the concept.