@inproceedings{darcy-etal-2024-aries,
title = "{ARIES}: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews",
author = "D{'}Arcy, Mike and
Ross, Alexis and
Bransom, Erin and
Kuehl, Bailey and
Bragg, Jonathan and
Hope, Tom and
Downey, Doug",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.377",
pages = "6985--7001",
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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<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.</abstract>
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%0 Conference Proceedings
%T ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
%A D’Arcy, Mike
%A Ross, Alexis
%A Bransom, Erin
%A Kuehl, Bailey
%A Bragg, Jonathan
%A Hope, Tom
%A Downey, Doug
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F darcy-etal-2024-aries
%X 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.
%U https://aclanthology.org/2024.acl-long.377
%P 6985-7001
Markdown (Informal)
[ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://aclanthology.org/2024.acl-long.377) (D’Arcy et al., ACL 2024)
ACL
- Mike D’Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, and Doug Downey. 2024. ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6985–7001, Bangkok, Thailand. Association for Computational Linguistics.