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.

翻译：法律诉求指案件中原告的主张，对于引导司法推理与案件解决至关重要。尽管已有许多研究致力于提升法律专业人士的工作效率，但针对帮助非专业人士（如原告）的相关探索仍属空白。本文基于给定案件事实，探讨法律诉求的自动生成问题。首先，我们从多类现实法律纠纷中构建了ClaimGen-CN——首个面向中文法律诉求生成任务的数据集。此外，我们设计了专门用于评估生成诉求的指标，涵盖事实性与清晰度两个核心维度。在此基础上，我们对前沿的通用大语言模型及法律领域大语言模型进行了全面的零样本评估。研究结果揭示了当前模型在事实准确性与表达清晰度方面的局限性，表明该领域需要更具针对性的开发。为促进这一重要任务的进一步探索，我们将公开该数据集。