JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909805523763200 |
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| author | Liu, Huanghai Huang, Quzhe Chen, Qingjing Hu, Yiran Ma, Jiayu Liu, Yun Shen, Weixing Feng, Yansong |
| author_facet | Liu, Huanghai Huang, Quzhe Chen, Qingjing Hu, Yiran Ma, Jiayu Liu, Yun Shen, Weixing Feng, Yansong |
| contents | In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning. To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: https://github.com/THUlawtech/JUREX |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning Liu, Huanghai Huang, Quzhe Chen, Qingjing Hu, Yiran Ma, Jiayu Liu, Yun Shen, Weixing Feng, Yansong Computation and Language Artificial Intelligence In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning. To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: https://github.com/THUlawtech/JUREX |
| title | JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.17166 |