How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
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Abstract (arXiv)
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,000 submitting authors and 9,000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews. The salient results are: (1) Authors have roughly a three-fold overestimate of the acceptance probability of their papers: The median prediction is 70% for an approximately 25% acceptance rate. (2) Female authors exhibit a marginally higher (statistically significant) miscalibration than male authors; predictions of authors invited to serve as meta-reviewers or reviewers are similarly calibrated, but better than authors who were not invited to review. (3) Authors' relative ranking of scientific contribution of two submissions they made generally agree (93%) with their predicted acceptance probabilities, but there is a notable 7% responses where authors think their better paper will face a worse outcome. (4) The author-provided rankings disagreed with the peer-review decisions about a third of the time; when co-authors ranked their jointly authored papers, co-authors disagreed at a similar rate -- about a third of the time. (5) At least 30% of respondents of both accepted and rejected papers said that their perception of their own paper improved after the review process. The stakeholders in peer review should take these findings into account in setting their expectations from peer review.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 61 references that are in the map. First posted on arXiv 22 Nov 2022.
Builds on
14 of 61 references.
References · 14
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2 more
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewingcited by 118
- 2023Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experimentcited by 31
- 2024The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Reviewcited by 7
- 2025How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Reviewcited by 3
- 2026Agreement Between Large Language Models, Human Reviewers, and Authors in Evaluating STROBE Checklists for Observational Studies in Rheumatologycited by 0
- 2026Agreement Between Large Language Models, Human Reviewers, and Authors in Evaluating STROBE Checklists for Observational Studies in Rheumatology12 Mar 2026
- 2025How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review2 Oct 2025
- 2024The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review24 Aug 2024
- 2023Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment5 Jun 2023
- 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing1 Jun 2023