CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916629006254080 |
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| author | Li, Haitao Ye, Jiaying Hu, Yiran Chen, Jia Ai, Qingyao Wu, Yueyue Chen, Junjie Chen, Yifan Luo, Cheng Zhou, Quan Liu, Yiqun |
| author_facet | Li, Haitao Ye, Jiaying Hu, Yiran Chen, Jia Ai, Qingyao Wu, Yueyue Chen, Junjie Chen, Yifan Luo, Cheng Zhou, Quan Liu, Yiqun |
| contents | Legal case documents play a critical role in judicial proceedings. As the number of cases continues to rise, the reliance on manual drafting of legal case documents is facing increasing pressure and challenges. The development of large language models (LLMs) offers a promising solution for automating document generation. However, existing benchmarks fail to fully capture the complexities involved in drafting legal case documents in real-world scenarios. To address this gap, we introduce CaseGen, the benchmark for multi-stage legal case documents generation in the Chinese legal domain. CaseGen is based on 500 real case samples annotated by legal experts and covers seven essential case sections. It supports four key tasks: drafting defense statements, writing trial facts, composing legal reasoning, and generating judgment results. To the best of our knowledge, CaseGen is the first benchmark designed to evaluate LLMs in the context of legal case document generation. To ensure an accurate and comprehensive evaluation, we design the LLM-as-a-judge evaluation framework and validate its effectiveness through human annotations. We evaluate several widely used general-domain LLMs and legal-specific LLMs, highlighting their limitations in case document generation and pinpointing areas for potential improvement. This work marks a step toward a more effective framework for automating legal case documents drafting, paving the way for the reliable application of AI in the legal field. The dataset and code are publicly available at https://github.com/CSHaitao/CaseGen. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_17943 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation Li, Haitao Ye, Jiaying Hu, Yiran Chen, Jia Ai, Qingyao Wu, Yueyue Chen, Junjie Chen, Yifan Luo, Cheng Zhou, Quan Liu, Yiqun Computation and Language Legal case documents play a critical role in judicial proceedings. As the number of cases continues to rise, the reliance on manual drafting of legal case documents is facing increasing pressure and challenges. The development of large language models (LLMs) offers a promising solution for automating document generation. However, existing benchmarks fail to fully capture the complexities involved in drafting legal case documents in real-world scenarios. To address this gap, we introduce CaseGen, the benchmark for multi-stage legal case documents generation in the Chinese legal domain. CaseGen is based on 500 real case samples annotated by legal experts and covers seven essential case sections. It supports four key tasks: drafting defense statements, writing trial facts, composing legal reasoning, and generating judgment results. To the best of our knowledge, CaseGen is the first benchmark designed to evaluate LLMs in the context of legal case document generation. To ensure an accurate and comprehensive evaluation, we design the LLM-as-a-judge evaluation framework and validate its effectiveness through human annotations. We evaluate several widely used general-domain LLMs and legal-specific LLMs, highlighting their limitations in case document generation and pinpointing areas for potential improvement. This work marks a step toward a more effective framework for automating legal case documents drafting, paving the way for the reliable application of AI in the legal field. The dataset and code are publicly available at https://github.com/CSHaitao/CaseGen. |
| title | CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.17943 |