Predicting highly-cited papers is a long-standing challenge due to the complex interactions of research content, scholarly communities, and temporal dynamics. Recent advances in large language models (LLMs) raise the question of whether early-stage textual information can provide useful signals of long-term scientific impact. Focusing on statistical publications, we propose a flexible, text-centered framework that leverages LLMs and structured prompt design to predict highly cited papers. Specifically, we utilize information available at the time of publication, including titles, abstracts, keywords, and limited bibliographic metadata. Using a large corpus of statistical papers, we evaluate predictive performance across multiple publication periods and alternative definitions of highly cited papers. The proposed approach achieves stable and competitive performance relative to existing methods and demonstrates strong generalization over time. Textual analysis further reveals that papers predicted as highly cited concentrate on recurring topics such as causal inference and deep learning. To facilitate practical use of the proposed approach, we further develop a WeChat mini program, \textit{Stat Highly Cited Papers}, which provides an accessible interface for early-stage citation impact assessment. Overall, our results provide empirical evidence that LLMs can capture meaningful early signals of long-term citation impact, while also highlighting their limitations as tools for research impact assessment.

翻译：预测高被引论文是一项长期存在的挑战，这源于研究内容、学术共同体与时间动态之间的复杂交互。大型语言模型（LLMs）的最新进展引发了一个问题：早期文本信息能否为长期科学影响力提供有用的信号。聚焦于统计学文献，我们提出了一个灵活的、以文本为中心的框架，该框架利用LLMs和结构化提示设计来预测高被引论文。具体而言，我们利用论文发表时可获取的信息，包括标题、摘要、关键词和有限的文献计量元数据。通过使用一个大规模的统计学论文语料库，我们在多个发表时间段和不同的高被引论文定义下评估了预测性能。所提出的方法相对于现有方法取得了稳定且有竞争力的性能，并展现出随时间推移的强泛化能力。文本分析进一步揭示，被预测为高被引的论文集中于因果推断和深度学习等反复出现的主题。为促进所提方法的实际应用，我们进一步开发了一个微信小程序 \textit{Stat Highly Cited Papers}，为早期引用影响力评估提供了一个易于访问的界面。总体而言，我们的结果为LLMs能够捕捉长期引用影响力的有意义的早期信号提供了实证证据，同时也凸显了其作为研究影响力评估工具的局限性。