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 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 https://github.com/armbf/LimGen.

翻译：审视研究局限是学术论文评审流程中的关键环节，它揭示了研究可能缺乏决定性或需要改进的方面，有助于读者思考进一步研究的更广泛影响。本文提出了一项新颖且具挑战性的任务——论文启发性局限性生成（SLG）。我们构建了名为LimGen的数据集，包含来自ACL论文集的4068篇研究论文及其对应的局限性描述。通过深入探讨相关挑战、实践见解和潜在机遇，我们研究了利用大语言模型（LLMs）生成启发性局限性的多种方法。我们的LimGen数据集及代码可通过https://github.com/armbf/LimGen获取。