LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers
Open accessAbstract
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 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 https://github.com/arbmf/LimGen .
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Lim-Gen fine-tunes models specifically for a task requiring inferring potential weaknesses not explicitly stated by authors.
“The versatility of fine-tuning is further demonstrated by its application to specific sub-tasks, as seen in Lim-Gen (Faizullah et al., 2024), which fine-tunes models specifically for a task requires inferring potential weaknesses not explicitly stated by authors.”
From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Faizullah et al., 2024
Cites
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., & Stoica, I. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613165 · Cited by 1,817 · see citing works
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., & Stoica, I. (2023) · arXiv · doi:10.48550/arxiv.2309.06180 · Cited by 1,802 · see citing works
S2ORC: The Semantic Scholar Open Research Corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.447 · Cited by 457 · see citing works
TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.428 · Cited by 169 · see citing works
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Large language models for automated scholarly paper review: A survey
Zhuang, Z., Chen, J., Xu, H., Jiang, Y., & Lin, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103332 · Cited by 34 · see citing works
Large language models in peer review: challenges and opportunities
Sun, Z. (2025) · Scientometrics · doi:10.1007/s11192-025-05440-w · Cited by 15 · see citing works
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews
Idahl, M., & Ahmadi, Z. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations) · doi:10.18653/v1/2025.naacl-demo.44 · Cited by 10 · see citing works
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
Wu, S., Jiang, O., Zhao, Y., Hu, T., Ma, Y., Zhang, K., Patwardhan, M., & Cohan, A. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1504 · Cited by 1 · see citing works
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews
Idahl, M., & Ahmadi, Z. (2024) · arXiv · doi:10.48550/arxiv.2412.11948 · Cited by 1 · see citing works
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation
Wu, S., Ma, Y., Zhao, Y., Hu, T., Jiang, O., Patwardhan, M., & Cohan, A. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.1696
- Authors
- Faizullah, A. R. B. M., Urlana, A., & Mishra, R.
- Affiliations
- Infosys (India) (IN)
- Tata Consultancy Services (India) (IN)
- International Institute of Information Technology, Hyderabad (IN)
- Corresponding author
- Faizullah, Abdur Rahman Bin Mohammed
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 106-124
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4402011733
- Cited by
- 6
- References
- 6
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 1.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, ACL Anthology, scholarly review, natural language generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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