@inproceedings{garg-etal-2025-revieweval,
title = "{R}eview{E}val: An Evaluation Framework for {AI}-Generated Reviews",
author = "Garg, Madhav Krishan and
Prasad, Tejash and
Singhal, Tanmay and
Kirtani, Chhavi and
Mandal, Murari and
Kumar, Dhruv",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1120/",
pages = "20542--20564",
ISBN = "979-8-89176-335-7",
abstract = "The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78{\%} and 47.62{\%} over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97{\%} and 12.73{\%}, enhances adherence to guidelines by 10.11{\%} and 47.26{\%} respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research."
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<abstract>The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.</abstract>
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%0 Conference Proceedings
%T ReviewEval: An Evaluation Framework for AI-Generated Reviews
%A Garg, Madhav Krishan
%A Prasad, Tejash
%A Singhal, Tanmay
%A Kirtani, Chhavi
%A Mandal, Murari
%A Kumar, Dhruv
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F garg-etal-2025-revieweval
%X The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.
%U https://aclanthology.org/2025.findings-emnlp.1120/
%P 20542-20564
Markdown (Informal)
[ReviewEval: An Evaluation Framework for AI-Generated Reviews](https://aclanthology.org/2025.findings-emnlp.1120/) (Garg et al., Findings 2025)
ACL
- Madhav Krishan Garg, Tejash Prasad, Tanmay Singhal, Chhavi Kirtani, Murari Mandal, and Dhruv Kumar. 2025. ReviewEval: An Evaluation Framework for AI-Generated Reviews. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20542–20564, Suzhou, China. Association for Computational Linguistics.