@inproceedings{yu-etal-2024-automated,
title = "Automated Peer Reviewing in Paper {SEA}: Standardization, Evaluation, and Analysis",
author = "Yu, Jianxiang and
Ding, Zichen and
Tan, Jiaqi and
Luo, Kangyang and
Weng, Zhenmin and
Gong, Chenghua and
Zeng, Long and
Cui, RenJing and
Han, Chengcheng and
Sun, Qiushi and
Wu, Zhiyong and
Lan, Yunshi and
Li, Xiang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.595/",
doi = "10.18653/v1/2024.findings-emnlp.595",
pages = "10164--10184",
abstract = "In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers."
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<abstract>In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.</abstract>
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%0 Conference Proceedings
%T Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
%A Yu, Jianxiang
%A Ding, Zichen
%A Tan, Jiaqi
%A Luo, Kangyang
%A Weng, Zhenmin
%A Gong, Chenghua
%A Zeng, Long
%A Cui, RenJing
%A Han, Chengcheng
%A Sun, Qiushi
%A Wu, Zhiyong
%A Lan, Yunshi
%A Li, Xiang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yu-etal-2024-automated
%X In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.
%R 10.18653/v1/2024.findings-emnlp.595
%U https://aclanthology.org/2024.findings-emnlp.595/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.595
%P 10164-10184
Markdown (Informal)
[Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis](https://aclanthology.org/2024.findings-emnlp.595/) (Yu et al., Findings 2024)
ACL
- Jianxiang Yu, Zichen Ding, Jiaqi Tan, Kangyang Luo, Zhenmin Weng, Chenghua Gong, Long Zeng, RenJing Cui, Chengcheng Han, Qiushi Sun, Zhiyong Wu, Yunshi Lan, and Xiang Li. 2024. Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10164–10184, Miami, Florida, USA. Association for Computational Linguistics.