@inproceedings{zhou-etal-2025-claimgen,
title = "{C}laim{G}en-{CN}: A Large-scale {C}hinese Dataset for Legal Claim Generation",
author = "Zhou, Siying and
Wu, Yiquan and
Chen, Hui and
Hu, Xueyu and
Kuang, Kun and
Jatowt, Adam and
Zheng, Chunyan and
Wu, Fei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.658/",
pages = "12296--12323",
ISBN = "979-8-89176-335-7",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation
%A Zhou, Siying
%A Wu, Yiquan
%A Chen, Hui
%A Hu, Xueyu
%A Kuang, Kun
%A Jatowt, Adam
%A Zheng, Chunyan
%A Wu, Fei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhou-etal-2025-claimgen
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.658/
%P 12296-12323
Markdown (Informal)
[ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation](https://aclanthology.org/2025.findings-emnlp.658/) (Zhou et al., Findings 2025)
ACL
- Siying Zhou, Yiquan Wu, Hui Chen, Xueyu Hu, Kun Kuang, Adam Jatowt, Chunyan Zheng, and Fei Wu. 2025. ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12296–12323, Suzhou, China. Association for Computational Linguistics.