ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908613895782400 |
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| author | Zhou, Siying Wu, Yiquan Chen, Hui Hu, Xavier Kuang, Kun Jatowt, Adam Hu, Ming Zheng, Chunyan Wu, Fei |
| author_facet | Zhou, Siying Wu, Yiquan Chen, Hui Hu, Xavier Kuang, Kun Jatowt, Adam Hu, Ming Zheng, Chunyan Wu, Fei |
| contents | Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case's facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation Zhou, Siying Wu, Yiquan Chen, Hui Hu, Xavier Kuang, Kun Jatowt, Adam Hu, Ming Zheng, Chunyan Wu, Fei Computation and Language Artificial Intelligence Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case's facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available. |
| title | ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17234 |