@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}