Meta-reviewing is a pivotal stage in the peer-review process, serving as the final step in determining whether a paper is recommended for acceptance. Prior research on meta-reviewing has treated this as a summarization problem over review reports. However, complementary to this perspective, meta-reviewing is a decision-making process that requires weighing reviewer arguments and placing them within a broader context. Prior research has demonstrated that decision-makers can be effectively assisted in such scenarios via dialogue agents. In line with this framing, we explore the practical challenges for realizing dialog agents that can effectively assist meta-reviewers. Concretely, we first address the issue of data scarcity for training dialogue agents by generating synthetic data using Large Language Models (LLMs) based on a self-refinement strategy to improve the relevance of these dialogues to expert domains. Our experiments demonstrate that this method produces higher-quality synthetic data and can serve as a valuable resource towards training meta-reviewing assistants. Subsequently, we utilize this data to train dialogue agents tailored for meta-reviewing and find that these agents outperform \emph{off-the-shelf} LLM-based assistants for this task. Finally, we apply our agents in real-world meta-reviewing scenarios and confirm their effectiveness in enhancing the efficiency of meta-reviewing.\footnote{Code available at: https://github.com/UKPLab/eacl2026-meta-review-as-dialog

翻译：元评审是同行评审流程中的关键环节，作为决定论文是否推荐录用的最终步骤。先前关于元评审的研究将其视为对评审报告的摘要生成问题。然而，与此视角形成互补的是，元评审本质上是一种决策过程，需要权衡评审人的论点并将其置于更广泛的背景中进行考量。已有研究表明，在此类场景中，对话智能体能够有效辅助决策者。基于此框架，我们探讨了实现能有效辅助元评审者的对话智能体所面临的实际挑战。具体而言，我们首先通过基于自优化策略的大型语言模型生成合成数据，以解决训练对话智能体时数据稀缺的问题，从而提升这些对话与专家领域的相关性。实验表明，该方法能生成更高质量的合成数据，并可作为训练元评审助手的重要资源。随后，我们利用该数据训练专为元评审定制的对话智能体，发现其在此任务上优于现成的基于大型语言模型的通用助手。最后，我们将所开发的智能体应用于实际元评审场景，并证实其在提升元评审效率方面的有效性。\footnote{代码发布于：https://github.com/UKPLab/eacl2026-meta-review-as-dialog