mlsanityData to 7 Oct 2026

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?

arXiv 2211.12966 ↗First posted 22 Nov 2022cs.LG cs.DB cs.DLcited by 5

Not on the map. Why?

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

  1. 2021Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experimentcited by 49
  2. 2020Mitigating Manipulation in Peer Review via Randomized Reviewer Assignmentscited by 38
  3. 2017Design and Analysis of the NIPS 2016 Review Processcited by 33
  4. 2018Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratingscited by 28
  5. 2021You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanismcited by 18
  6. 2020A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferencescited by 16
  7. 2021Making Paper Reviewing Robust to Bid Manipulation Attackscited by 12
  8. 2022Cite-seeing and Reviewing: A Study on Citation Bias in Peer Reviewcited by 7
  9. 2015Calibration with confidence: A principled method for panel assessmentcited by 6
  10. 2020A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussionscited by 6
  11. 2022Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignmentcited by 4
  12. 2020Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Reviewcited by 3
2 more
  1. 2022Calibration with Privacy in Peer Reviewcited by 2
  2. 2022To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Onlinecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.