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arXiv · 2306.12587

ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews

Mike D'Arcy·Alexis Ross·Erin Bransom·Bailey Kuehl·Jonathan Bragg·Tom Hope·Doug Downey
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Abstract

We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their corresponding paper edits. The data is drawn from real reviewer-author interactions from computer science, and we provide labels linking each reviewer comment to the specific paper edits made by the author in response. We automatically create a high-precision silver training set, as well as an expert-labeled test set that shows high inter-annotator agreement. In experiments with 10 models covering the state of the art, we find that they struggle even to identify which edits correspond to a comment -- especially when the relationship between the edit and the comment is indirect and requires reasoning to uncover. We also extensively analyze GPT-4's ability to generate edits given a comment and the original paper. We find that it often succeeds on a superficial level, but tends to rigidly follow the wording of the feedback rather than the underlying intent, and lacks technical details compared to human-written edits.

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Mike D'Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, Doug Downey. 2023-06-21. ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews. https://arxiv.org/abs/2306.12587

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