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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00841 |
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| _version_ | 1866916638958288896 |
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| author | Shen, Jiaxin Xu, Jinan Hu, Huiqi Lin, Luyi Zheng, Fei Ma, Guoyang Meng, Fandong Zhou, Jie Han, Wenjuan |
| author_facet | Shen, Jiaxin Xu, Jinan Hu, Huiqi Lin, Luyi Zheng, Fei Ma, Guoyang Meng, Fandong Zhou, Jie Han, Wenjuan |
| contents | While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. We also create the first crowd-sourced dataset for this task, enabling comprehensive evaluation. Simultaneously, we propose an agent framework that employs a comprehensive suite of legal analysis tools to address the challenge task. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the ``Intelligent Court''. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00841 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences Shen, Jiaxin Xu, Jinan Hu, Huiqi Lin, Luyi Zheng, Fei Ma, Guoyang Meng, Fandong Zhou, Jie Han, Wenjuan Artificial Intelligence While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. We also create the first crowd-sourced dataset for this task, enabling comprehensive evaluation. Simultaneously, we propose an agent framework that employs a comprehensive suite of legal analysis tools to address the challenge task. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the ``Intelligent Court''. |
| title | A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00841 |