LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers
This work addresses the need for automated limitation generation in scholarly review, which could aid researchers and reviewers, but it is incremental as it builds on existing LLM capabilities.
The authors tackled the problem of automatically generating suggestive limitations for research papers by introducing the LimGen dataset of 4068 papers and exploring the use of large language models for this task, resulting in a new benchmark and code release.
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}.