基于 deliberation 的决策：作为文档基础对话的元评审
Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue
AI总结：
本文提出通过生成合成数据和训练专用对话代理，提升元评审效率。
摘要：
元评审是同行评审过程中的关键阶段，是决定是否推荐论文接受的最终步骤。先前对元评审的研究将其视为对评审报告的总结问题。然而，从另一个角度来看，元评审是一种决策过程，需要权衡评审员的论点并将其置于更广泛的背景下。先前的研究表明，通过对话代理可以有效地帮助决策者在这种情况下。在此框架下，我们探讨了实现能够有效协助元评审员的对话代理的实际挑战。具体而言，我们首先通过使用大型语言模型（LLMs）基于自我完善策略生成合成数据来解决训练对话代理的数据稀缺问题，以提高这些对话与专家领域相关性。我们的实验表明，这种方法生成了高质量的合成数据，并可以作为训练元评审助手的宝贵资源。随后，我们利用这些数据训练专门用于元评审的对话代理，并发现这些代理在该任务上优于现成的基于LLM的助手。最后，我们将我们的代理应用于实际的元评审场景，并确认它们在提高元评审效率方面的有效性。\footnote{代码可在：https://github.com/UKPLab/eacl2026-meta-review-as-dialog 上获得}
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
