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--- |
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dataset_info: |
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features: |
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- name: _id |
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dtype: int64 |
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- name: lai_yuan |
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dtype: string |
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- name: lai_yuan_id |
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dtype: int64 |
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- name: biao_ti |
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dtype: string |
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- name: fa_yuan |
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dtype: string |
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- name: fa_ting_guan_dian |
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dtype: string |
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- name: shen_li_jie_duan |
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dtype: string |
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- name: ri_qi_s31 |
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|
dtype: string |
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- name: ri_q1_s41 |
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dtype: string |
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- name: fu_yan |
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|
dtype: string |
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|
- name: an_you |
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sequence: string |
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- name: dang_shi_ren |
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sequence: string |
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- name: an_jian_lei_xing |
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dtype: string |
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- name: wen_shu_bian_hao |
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dtype: string |
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- name: shen_li_qing_kuang |
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dtype: string |
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- name: guan_jian_ci |
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sequence: string |
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- name: ting_shen_guo_cheng |
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dtype: string |
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- name: ren_yuan |
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|
dtype: string |
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- name: pan_jue_jie_guo |
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dtype: string |
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- name: label |
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struct: |
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- name: classification |
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dtype: string |
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- name: reason |
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dtype: string |
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- name: charge |
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dtype: string |
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- name: concept |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 51203688 |
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num_examples: 2914 |
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- name: test |
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num_bytes: 51902129 |
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num_examples: 2928 |
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download_size: 42728249 |
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dataset_size: 103105817 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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- text-generation |
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language: |
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- zh |
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tags: |
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- legal |
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pretty_name: Legal Concept Entailment |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Legal Concept Entailment Dataset |
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## Dataset Description |
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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. |
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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. |
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## Legal Concept Entailment |
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The LCE task is a dual-part task designed to test a model's understanding of legal concepts. |
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* **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. |
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* **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. |
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An example of the task is shown below: |
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## Languages |
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The data is in **Chinese (zh)**, as it is sourced from [China Judgments Online](https://wenshu.court.gov.cn/). |
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## Dataset Structure |
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### Data Splits |
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The dataset is divided into two splits: |
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- `train`: Contains 2914 instances for generating legal concept interpretation. |
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- `test`: Contains 2928 instances for testing. |
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### Data Instances |
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Each instance in the dataset corresponds to a legal case and a specific legal concept. |
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- `label`: Contains the classification label (`Yes` or `No`) and the gold reason for the classification extracted from the court view. |
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- `concept`: The vague legal concept being evaluated. |
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- `ting_shen_guo_cheng`: The fact description of the case. |
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- `fa_ting_guan_dian`: The court view on the case, which includes the legal interpretation of the concept. |
