@inproceedings{chang-etal-2025-treereview,
    title = "{T}ree{R}eview: A Dynamic Tree of Questions Framework for Deep and Efficient {LLM}-based Scientific Peer Review",
    author = "Chang, Yuan  and
      Li, Ziyue  and
      Zhang, Hengyuan  and
      Kong, Yuanbo  and
      Wu, Yanru  and
      So, Hayden Kwok-Hay  and
      Guo, Zhijiang  and
      Zhu, Liya  and
      Wong, Ngai",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.790/",
    pages = "15662--15693",
    ISBN = "979-8-89176-332-6",
    abstract = "While Large Language Models (LLMs) have shown significant potential in assisting peer review, current methods often struggle to generate thorough and insightful reviews while maintaining efficiency. In this paper, we propose TreeReview, a novel framework that models paper review as a hierarchical and bidirectional question-answering process. TreeReview first constructs a tree of review questions by recursively decomposing high-level questions into fine-grained sub-questions and then resolves the question tree by iteratively aggregating answers from leaf to root to get the final review. Crucially, we incorporate a dynamic question expansion mechanism to enable deeper probing by generating follow-up questions when needed. We construct a benchmark derived from ICLR and NeurIPS venues to evaluate our method on full review generation and actionable feedback comments generation tasks. Experimental results of both LLM-based and human evaluation show that TreeReview outperforms strong baselines in providing comprehensive, in-depth, and expert-aligned review feedback, while reducing LLM token usage by up to 80{\%} compared to computationally intensive approaches."
}