发表机构
University of Virginia; Biocomplexity Institute; Virginia Tech; University of Pittsburgh（弗吉尼亚大学; 生物复杂性研究所; 弗吉尼亚理工大学; 匹兹堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在生成公共卫生叙事，提出EpiNarrate框架，将数值推理与自然语言生成分离，通过提取情景轴、构建数据集等步骤，经实验验证该模型能生成事实依据更充分、覆盖范围更广且风格类似专家报告的叙事。
AI 中文摘要
生成清晰易懂的公共卫生叙事对于向政策制定者和公众传达复杂的流行病学预测至关重要。此类叙事不仅要报告数字，还需在多维度上进行情境化和定量基础构建。然而，直接使用大语言模型总结和情境化此类数据常导致不一致、遗漏和脆弱行为。我们引入了一个用于公共卫生报告生成的智能体框架（EpiNarrate），将结构化数值推理与自然语言生成分离。该框架首先提取情景轴并组织成偏序模式，然后构建增强数据集并通过比较语法得出有效的定量陈述。为平衡覆盖范围和非冗余性，引入基于最大熵原理的兴趣驱动选择机制。实验表明，我们的模型能生成事实依据更充分、涵盖更广泛显著流行病学模式且保留专家撰写报告风格的叙事。
英文摘要
Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must be contextualized and quantitatively grounded across multiple dimensions. Further, projections are often derived from large ensemble datasets which combine intervention assumptions, geographic and demographic strata, outcomes, time horizons, and uncertainty quantiles. However, directly using large language models (LLMs) to summarize and contextualize such data often leads to inconsistencies, omissions, and fragile behavior. We introduce an agentic framework (EpiNarrate) for public health report generation that separates structured numerical reasoning from natural-language generation. The framework first extracts scenario axes and organizes them into a partial-order schema, enabling systematic traversal of the underlying multidimensional space. It then constructs an augmented dataset and derives valid quantitative statements through a comparison grammar that enforces semantic and arithmetic consistency. To balance coverage and non-redundancy, we introduce an interestingness-driven selection mechanism based on maximum-entropy principles. Experiments on the COVID-19 Scenario Modeling Hub demonstrate that our model produces narratives with improved factual grounding and broader coverage of salient epidemiological patterns, while preserving the style of expert-written reports.
