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

Abdur Rahman Bin Md Faizullah, Ashok Urlana, Rahul Mishra
3/22/2024
cs.CLcs.AIcs.IR

Official code: arbmf/LimGen (2 stars, last commit 2024-06-06).

Abstract

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}.

View on arXivView PDF

Code Implementations(1)

arbmf/LimGenOfficial100%
20PythonMar 22, 20242 years ago

Similar Papers

Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers

87% match1 repo92025

LimAgents: Multi-Agent LLMs for Generating Research Limitations

83% match2025

LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models

82% match1 repo42025

Large-Scale Constraint Generation -- Can LLMs Parse Hundreds of Constraints?

80% match2025

LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations

80% match1 repo52025

Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of Topic Models

78% match2025

Cite this paper

@article{faizullah2024limgen,
  title  = {LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers},
  author = {Abdur Rahman Bin Md Faizullah and Ashok Urlana and Rahul Mishra},
  year   = {2024},
  eprint = {2403.15529},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.15529v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.15529.svg)](https://codeofpaper.com/paper/2403.15529)

Discussion