@inproceedings{ryu-etal-2026-reviewscore,
title = "{R}eview{S}core: Misinformed Peer Review Detection with Large Language Models",
author = "Ryu, Hyun and
Jang, Doohyuk and
Lee, Hyemin S. and
Jeong, Joonhyun and
Kim, Gyeongman and
Cho, Donghyeon and
Chu, Gyouk and
Hwang, Minyeong and
Jang, Hyeongwon and
Kim, Changhun and
Kim, Haechan and
Kim, Jina and
Kim, Joowon and
Kim, Yoonjeon and
Lee, Kwanhyung and
Park, Chanjae and
Yun, Heecheol and
Betz, Gregor and
Yang, Eunho",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.10/",
pages = "173--208",
ISBN = "979-8-89176-462-0",
abstract = "Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either ``weaknesses'' in a review that contain incorrect premises, or ``questions'' in a review that can be already answered by the paper. We verify that 15.2{\%} of weaknesses and 26.4{\%} of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4{--}0.5 and kappa scores of 0.3{--}0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models' incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality."
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<abstract>Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either “weaknesses” in a review that contain incorrect premises, or “questions” in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4–0.5 and kappa scores of 0.3–0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models’ incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality.</abstract>
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%0 Conference Proceedings
%T ReviewScore: Misinformed Peer Review Detection with Large Language Models
%A Ryu, Hyun
%A Jang, Doohyuk
%A Lee, Hyemin S.
%A Jeong, Joonhyun
%A Kim, Gyeongman
%A Cho, Donghyeon
%A Chu, Gyouk
%A Hwang, Minyeong
%A Jang, Hyeongwon
%A Kim, Changhun
%A Kim, Haechan
%A Kim, Jina
%A Kim, Joowon
%A Kim, Yoonjeon
%A Lee, Kwanhyung
%A Park, Chanjae
%A Yun, Heecheol
%A Betz, Gregor
%A Yang, Eunho
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ryu-etal-2026-reviewscore
%X Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either “weaknesses” in a review that contain incorrect premises, or “questions” in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4–0.5 and kappa scores of 0.3–0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models’ incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality.
%U https://aclanthology.org/2026.findings-emnlp.10/
%P 173-208
Markdown (Informal)
[ReviewScore: Misinformed Peer Review Detection with Large Language Models](https://aclanthology.org/2026.findings-emnlp.10/) (Ryu et al., Findings 2026)
ACL
- Hyun Ryu, Doohyuk Jang, Hyemin S. Lee, Joonhyun Jeong, Gyeongman Kim, Donghyeon Cho, Gyouk Chu, Minyeong Hwang, Hyeongwon Jang, Changhun Kim, Haechan Kim, Jina Kim, Joowon Kim, Yoonjeon Kim, Kwanhyung Lee, Chanjae Park, Heecheol Yun, Gregor Betz, and Eunho Yang. 2026. ReviewScore: Misinformed Peer Review Detection with Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 173–208, Budapest, Hungary. Association for Computational Linguistics.