PEERAssist: Leveraging on Paper-Review Interactions to Predict Peer Review Decisions
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing peer review datasets have limited data diversity, being based on a few conferences and relatively small amounts of data.
“existing peer review datasets face three major limitations: (1) limited data diversity”
From Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions (2025) · cites this paper as Bharti et al., 2021
The majority of existing peer review datasets are based only on review data from the ICLR conference.
“the majority of existing datasets [Kennard et al., 2021, Bharti et al., 2021, Wu et al., 2022, Zhang et al., 2022, Zhou et al., 2024, Weng et al., 2024, Jin et al., 2024, Zhu et al., 2025] are only based on the review data from the ICLR conference”
From Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions (2025) · cites this paper as Bharti et al., 2021
Cites
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218 · see citing works
Duration and quality of the peer review process: the author’s perspective
Huisman, J., & Smits, J. (2017) · Scientometrics · doi:10.1007/s11192-017-2310-5 · Cited by 303 · see citing works
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Kang, D., Ammar, W., Dalvi, B., Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1149 · Cited by 171 · see citing works
Tools used to assess the quality of peer review reports: A methodological systematic review
Superchi, C., González, J. A., Solà, I., Cobo, E., Hren, D., & Boutron, I. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0688-x · Cited by 110 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
An Analysis of Tasks and Datasets in Peer Reviewing
Staudinger, M., Kusa, W., Piroi, F., & Hanbury, A. (2024) · Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) · doi:10.18653/v1/2024.sdp-1.24 · Cited by 6 · see citing works
Co-Reviewer: can AI review like a human? An agentic framework for LLM-human alignment in peer review
Bharti, P. K., Dalal, V., & Panchal, M. (2026) · Scientometrics · doi:10.1007/s11192-026-05557-6 · Cited by 2 · see citing works
Self-citationRe2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions
Zhang, D., Bao, Z., Du, S., Zhao, Z., Zhang, K., Bao, D., & Yang, Y. (2025) · arXiv · doi:10.48550/arxiv.2505.07920
“…in the availability and formatting of the review information across different conferences, the majority of existing datasets [Kennard et al., 2021, Bharti et al., 2021, Wu et al., 2022, Zhang et al., 2022, Zhou et al., 2024, Weng et al., 2024, Jin et al., 2024, Zhu et al., 2025] are only based on…”
- Authors
- Bharti, P. K., Ranjan, S., Ghosal, T., Agrawal, M., & Ekbal, A.
- Affiliations
- Indian Institute of Technology Patna (IN)
- Charles University (CZ)
- Corresponding author
- Bharti, Prabhat Kumar
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 421-435
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3215247326
- Cited by
- 16
- References
- 4
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 1 · 2023: 5 · 2022: 4
- FWCI
- 25.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- peer review, human-AI collaboration, cross-attention, sentiment analysis
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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