In scientific research, limitations refer to the shortcomings, constraints, or weaknesses within a study. Transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often a) underreport them in the paper text and b) use hedging strategies to satisfy editorial requirements at the cost of readers' clarity and confidence. This underreporting behavior, along with an explosion in the number of publications, has created a pressing need to automatically extract or generate such limitations from scholarly papers. In this direction, we present a complete architecture for the computational analysis of research limitations. Specifically, we create a dataset of limitations in ACL, NeurIPS, and PeerJ papers by extracting them from papers' text and integrating them with external reviews; we propose methods to automatically generate them using a novel Retrieval Augmented Generation (RAG) technique; we create a fine-grained evaluation framework for generated limitations; and we provide a meta-evaluation for the proposed evaluation techniques.
View on arXiv@article{azher2025_2505.18207,
title={ BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text },
author={ Ibrahim Al Azher and Miftahul Jannat Mokarrama and Zhishuai Guo and Sagnik Ray Choudhury and Hamed Alhoori },
journal={arXiv preprint arXiv:2505.18207},
year={ 2025 }
}