--- license: mit task_categories: - question-answering language: - zh configs: - config_name: Data data_files: jurex4e.json - config_name: Flattened_data data_files: flattened_jurex4e.json tags: - legal - chinese size_categories: - n<1K --- # JUREX Source code and data for *JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning* ## Overview - [Dataset Structure](#dataset-structure) - [Annotation](#Annotation) - [Experiment](#experiment) - [Similar Charge Distinction](#similar-charge-distinction) - [Legal Case Retrieval](#legal-case-retrieval) - [Requirements](#requirements) - [License](#license) ## Dataset Structure JUREX-4E is the first part of our curated expert knowledge base(mind map structure), focusing on the four elements of criminal charges. ``` data - law # legal texts - crime2article.json # mapping from crime to article - criminal_law.json # criminal articles - jurex4e.json # The JSON-formatted four elements converted from a mind map structure. The "title" represents the content of each hierarchical level, and "topic" represents the content of the next level. - flattened_jurex4e.json # The JSON-formatted four elements obtained by simply concatenating all levels. ``` An example: the original mind map: ![sample](img/sample.png) `jurex4e.json`: ```json { "投放危险物质罪": { "犯罪客体": [ { "title": "公共安全，即不特定多数人的生命、健康和重大公私财产安全" } ], "客观方面": [ { "title": "行为人实施了投放毒害性、放射性、传染病病原体等物质，危害公共安全的行为。", "topics": [ { "title": "假的定投放虚假物质罪（属于扰乱公共秩序罪）。" }, { "title": "危险物质分类【T12/45", "topics": [ { "title": "(1)“投放毒害性、放射性、传染病病原体等物质”，是指向公共饮用的水源以及出售的食品、饮料或者牲畜、禽类的饮水池、饲料等物品中投放毒害性、放射性、传染病病原体等物质的行为。" }, { "title": "(2)“毒害性物质”，是指能够对人体或者动物产生毒害作用的有毒物质，包括化学性、生物性、微生物类有毒物质。" }, { "title": "(3)“放射性物质”，是指含铀、镭、钻等放射性元素，可能对人体、动物或者环境产生严重辐射危害的物质，包括能够产生裂变反应或者聚合反应的核材料。" }, { "title": "(4)“传染病病原体”，是指能够通过在人体或者动物体内适当的环境中繁殖，从而使人体或者动物感染传染病，甚至造成传染病扩散的细菌、霉菌、毒种、病毒等。" } ] } ] } ], "犯罪主体": [ { "title": "一般主体，包括已满14周岁不满16周岁的未成年人" } ], "主观方面": [ { "title": "故意，行为人出于何种动机不影响本罪成立" } ] } }, ``` `flattened_jurex4e.json`: ```json "投放危险物质罪": { "犯罪客体": "# 公共安全，即不特定多数人的生命、健康和重大公私财产安全", "客观方面": "# 行为人实施了投放毒害性、放射性、传染病病原体等物质，危害公共安全的行为。\n## 假的定投放虚假物质罪（属于扰乱公共秩序罪）。\n## 危险物质分类【T12/45\n### (1)“投放毒害性、放射性、传染病病原体等物质”，是指向公共饮用的水源以及出售的食品、饮料或者牲畜、禽类的饮水池、饲料等物品中投放毒害性、放射性、传染病病原体等物质的行为。\n### (2)“毒害性物质”，是指能够对人体或者动物产生毒害作用的有毒物质，包括化学性、生物性、微生物类有毒物质。\n### (3)“放射性物质”，是指含铀、镭、钻等放射性元素，可能对人体、动物或者环境产生严重辐射危害的物质，包括能够产生裂变反应或者聚合反应的核材料。\n### (4)“传染病病原体”，是指能够通过在人体或者动物体内适当的环境中繁殖，从而使人体或者动物感染传染病，甚至造成传染病扩散的细菌、霉菌、毒种、病毒等。", "犯罪主体": "# 一般主体，包括已满14周岁不满16周岁的未成年人", "主观方面": "# 故意，行为人出于何种动机不影响本罪成立" }, ``` ## Annotation JUREX-4E is an expert-annotated knowledge base covering 155 criminal charges. It is structured through a progressive hierarchical annotation framework that prioritizes legal source validity and employs diverse legal interpretation methods to ensure comprehensiveness and authority. ![main](img/main.jpg) *Thick arrows indicate the primary level where a particular interpretive method is applied, while dashed arrows represent its supplementary use at that level. The statistics of JUREX-4E shows as follow: | | Mean | Median | | ---- | ---- | ---- | | Avg. Length | 472.53 | - | | Subject | 51.64 | 17 | | Object | 36.01 | 25 | | Subjective Aspect | 42.38 | 21 | | Objective Aspect | 342.5 | 230 | ## Experiment We apply JUREX-4E to Similar Charge Distinction task and the Legal Case Retrieval task. ### Similar Charge Distinction We chose three 2-label classification groups in [GCI](https://github.com/xxxiaol/GCI/) dataset: | **Charge Sets** | **Charges** | **Cases** | | ---- | ---- | ---- | | F&E | Fraud & Extortion | 3536 / 2149 | | E&MPF | Embezzlement & Misappropriation of Public Funds | 2391 / 1998 | | AP&DD | Abuse of Power & Dereliction of Duty | 1950 / 1938 | We use an unified approach to introduce four-element descriptions. For each group of similar charges, the model receives charges' four-elements from JUREX-4E or generated by LLM to aid classification. Specifically, **GPT-4o+FET_Expert** relies on expert-annotated four-elements, while **GPT-4o+FET_LLM** relies on LLM-generated four-elements. The instruction format is consistent across methods, with only the *[Four Elements of candidate charges]* varying based on the source. | **Prompt:** | | ---- | | You are a lawyer specializing in criminal law. Based on Chinese criminal law,
please determine which of the following candidate charges the given facts align with.
**The candidate charges and their corresponding four elements are as follows:**
*[Four Elements of Candidate Charges]*.
The four elements represent the core factors for determining the constitution of a criminal charge.
*[The basic concepts of the Four-Element Theory]*
Please Compare the case facts to determine which charge's four elements they align with, thereby identifying the charge. | All experiments are conducted in a zero-shot setting, with the max\_tokens set to 3,000 (or 10,000 for COT and MALR reasoning) and temperature set to 0 or 0.0001(In repeated experiments). | Model | F&E | | E&MPF | | AP&DD | | Average | | |--------------------|---------|-----------|-----------|-----------|-----------|-----------|-------------|------------| | | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | | GPT-4o | 94.36 | 95.81 | 86.49 | 89.76 | 85.54 | 87.12 | 88.72 | 90.07 | | GPT-4o+Article | 95.34 | 96.30 | **92.64** | 93.03 | 88.30 | 89.33 | 92.09 | 92.89 | | Legal-COT | 94.99 | 96.27 | 90.50 | 90.99 | 87.81 | 88.14 | 89.95 | 90.85 | | MALR | 94.62 | 95.82 | 86.99 | 86.98 | 87.86 | 88.68 | 89.82 | 90.49 | | GPT-4o+FETLLM | 95.73 | 96.56 | 91.87 | 92.01 | 89.61 | 89.69 | 92.40 | 92.75 | | GPT-4o+FETExpert | **96.06** | **96.69** | 92.57 | **93.05** | **90.53** | **90.62** | **93.05** | **93.45** | ### Legal Case Retrieval We propose the FETExpert_guided method to enhance legal case retrieval by leveraging JUREX-4E. Our approach consists of three key steps: 1. Predicting Charges: A small LLM analyzes case facts to predict potential charges. 2. Matching Elements: The model retrieves corresponding four-element details from a curated legal knowledge base. 3. Analyzing Case Facts: Guided by the matched charges' four-element, another LLM generates case-specific four elements for each candidate. The final ranking combines the similarity of case four elements and case facts between the query and candidates. | Model | NDCG@10 | NDCG@20 | NDCG@30 | R@1 | R@5 | R@10 | R@20 | R@30 | MRR | |-------------------------------|---------|---------|---------|--------|--------|--------|--------|--------|--------| | BERT | 0.1511 | 0.1794 | 0.1978 | 0.0199 | 0.0753 | 0.1299 | 0.2157 | 0.2579 | 0.1136 | | Legal-BERT | 0.1300 | 0.1487 | 0.1649 | 0.0186 | 0.0542 | 0.1309 | 0.1822 | 0.2172 | 0.0573 | | Lawformer | 0.2684 | 0.3049 | 0.3560 | 0.0432 | 0.1479 | 0.2330 | 0.3349 | 0.4683 | 0.1096 | | ChatLaw | 0.2049 | 0.2328 | 0.2745 | 0.0353 | 0.1306 | 0.1913 | 0.2684 | 0.3751 | 0.1285 | | SAILER | 0.3142 | 0.4133 | 0.4745 | 0.0539 | 0.1780 | 0.3442 | 0.5688 | 0.7092 | 0.1427 | | GEAR | * | * | * | 0.0630 | 0.1706 | 0.3142 | 0.4625 | * | 0.2162 | | BGE | 0.4737 | 0.5539 | 0.5937 | 0.0793 | 0.2945 | 0.4298 | 0.6500 | 0.7394 | 0.1926 | | FETLLM | 0.5139 | 0.5862 | 0.6291 | 0.0980 | 0.2967 | 0.4769 | 0.6802 | 0.7828 | 0.2140 | |     - base | 0.3583 | 0.4293 | 0.4798 | 0.0506 | 0.2240 | 0.3644 | 0.5383 | 0.6652 | 0.1453 | | FETExpert_guided | **0.5211** | **0.5920** | **0.6379** | **0.1024** | **0.3049** | **0.4883** | **0.6885** | **0.7967** | **0.2155** | |     - base | 0.3766 | 0.4584 | 0.5111 | 0.0715 | 0.1894 | 0.3709 | 0.5891 | 0.7203 | 0.1624 | *SCR results. Bold fonts indicate leading results in each setting. * denotes that the indicator is not applicable to the current model. ## License [MIT](LICENSE)