C laim G en- CN : A Large-scale C hinese Dataset for Legal Claim Generation (2025)

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.

## introduction over the past decades, the advancement of natural language processing (nlp) techniques has advanced the field of legal artificial intelligence (legal ai). legal ai is an important subfield of artificial intelligence, with the goal of supporting individuals across various legal tasks, including legal judgment prediction @xcite @xcite , court view generation @xcite , similar case matching @xcite , legal language understanding @xcite , and legal question answering @xcite . as legal ai systems become more advanced, it is essential to reflect not only on what these systems can do, but also on what * corresponding authors. to promote the rule of law and make legal support more accessible, we advocate exploring the problem of legal claim generation. as figure 1 shows, different legal scenarios play different roles. the pre-court scenario is primarily dedicated to preparing claims for the plaintiffs, whereas the incourt scenario is where those claims are utilized. our work is concentrated on the pre-court phase, focusing on the interests of the parties involved. to the best of our knowledge, we are the first to explore the problem of legal claim generation and to reaffirm its significance in civil litigation. technically, legal claim generation introduces two key challenges that set it apart from prior legal ai tasks. first, it is inherently open-ended: models must generate legal claims directly from factual narratives, without the guidance of predefined templates, as is common in judgment generation. second, the input is typically authored by non-experts. unlike court view generation, which builds on legally structured texts written by judges, this task requires interpreting informal, often unstructured, and emotional descriptions. the model must extract relevant facts, infer the underlying legal intent, and articulate it as a clear and valid claim. to support research on legal ai in the field of civil litigation, several legal datasets have been constructed @xcite ; xiao judge defendant plaintiff plaintiff claimgen order the defendant to return the principal doubled. ❌ order the defendant to pay the principal and interest. ✅ in-court scenario pre-court scenario legal expert after consultation without consultation (most common) after consultation order the defendant to pay the principal and interest. ✅ unreasonable claims (lose) reasonable claims (win) reasonable claims (win) ## problem formulation we first clarify the definition of the terms as follows. common types of legal claims include: 1) confirmation of legal relationships. this claim seeks court confirmation for certain legal relationships, such as the confirmation of the status of a missing or deceased individual. 2) enforcement of obligations. this claim compels the opposing party to fulfill payment obligations. examples include seeking compensation for losses, demanding the repayment of loan principal and interest, or demanding compliance with contractual obligations. 3) alteration or termination of civil legal relationships. this claim covers demands for changes or termination of specific civil legal relationships. examples include filing for divorce or demanding cancellations of contracts. then the problem can be defined as: problem 1 (claim generation). given the facts and reasons f , the task is to predict the claims c. ## conclusion in this paper, we make a pioneering step in legal ai by focusing on generating claims for civil litigation. our dataset, claimgen-cn, covers a wide range of legal cases, offering a solid foundation for future legal ai research and application. to evaluate model performance, we propose two dimensions, namely factuality and clarity, which are tailored to the specific requirements of legal text generation. our error analysis reveals that current llms struggle with generating factually accurate and concise claims, suggesting key directions for future improvements.