LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers

Autor:	Faizullah, Abdur Rahman Bin Md, Urlana, Ashok, Mishra, Rahul
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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}. Comment: Accepted at ECML-PKDD 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.15529 Zobrazit plný text záznamu View this record from Arxiv