  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}.
