  Large Language Models (LLMs) have demonstrated wide-ranging applications
across various fields and have shown significant potential in the academic
peer-review process. However, existing applications are primarily limited to
static review generation based on submitted papers, which fail to capture the
dynamic and iterative nature of real-world peer reviews. In this paper, we
reformulate the peer-review process as a multi-turn, long-context dialogue,
incorporating distinct roles for authors, reviewers, and decision makers. We
construct a comprehensive dataset containing over 26,841 papers with 92,017
reviews collected from multiple sources, including the top-tier conference and
prestigious journal. This dataset is meticulously designed to facilitate the
applications of LLMs for multi-turn dialogues, effectively simulating the
complete peer-review process. Furthermore, we propose a series of metrics to
evaluate the performance of LLMs for each role under this reformulated
peer-review setting, ensuring fair and comprehensive evaluations. We believe
this work provides a promising perspective on enhancing the LLM-driven
peer-review process by incorporating dynamic, role-based interactions. It
aligns closely with the iterative and interactive nature of real-world academic
peer review, offering a robust foundation for future research and development
in this area. We open-source the dataset at
https://github.com/chengtan9907/ReviewMT.
