Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called \textbf{\textit{LimGen}}, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at \url{https://github.com/arbmf/LimGen}.

翻译：审视局限性是学术研究评审过程中的关键步骤，它揭示了一项研究可能缺乏决定性或需要改进的方面。这有助于读者思考其对进一步研究的更广泛影响。本文提出了一项新颖且具有挑战性的任务：为研究论文生成暗示性局限性。我们构建了一个名为 **LimGen** 的数据集，涵盖了来自ACL文集的4068篇研究论文及其相关局限性。我们通过深入探究相关挑战、实践见解和潜在机遇，研究了利用大型语言模型生成暗示性局限性的几种方法。我们的LimGen数据集和代码可通过 \url{https://github.com/arbmf/LimGen} 访问。