Learning Checklists to Reduce Lottery Effects in Scientific Peer Review

Abstract

Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We show this error can be reduced by surfacing a checklist of criticisms learned from a historical review corpus. Our approach generates a tree of checklists representing coarsenings of the atomic review claim space, then selects the coarsening that maximizes a theoretically-motivated measure of correlated agreement among reviewers. We realize this approach with an LLM-assisted pipeline and test it on NeurIPS 2021 and ICLR 2022 review data. In simulated review with LLM reviewers, the learned checklist reduces the mean squared error (MSE) of a three reviewer committee by 58% on NeurIPS and 21% on ICLR, and on both venues the checklist with the highest correlated agreement has the lowest error for committees of three or more. In a human study with 20 participants, the learned NeurIPS checklist lowers the MSE of a paper ranking, measured against the NeurIPS 2021 order, from 0.48 to 0.22.