发表机构
Huazhong University of Science and Technology; Philosophy and Social Sciences Laboratory of Big Data and National Communication Strategy, Ministry of Education; The National Key Laboratory of Multispectral Information Intelligent Processing Technology（华中科技大学; 大数据与国家传播战略教育部哲学社会科学实验室; 多谱信息智能处理技术全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出个性化可靠科普写作任务，构建数据集与基准PSCB，引入DA-MoE解耦受众适配与领域知识，并提出多智能体事实核查机制，实现最先进性能。
AI 中文摘要
我们提出了个性化与可靠科普写作这一新任务，该任务要求在保持事实准确性的同时，将科学解释适配给具有不同认知水平的受众。然而，提升个性化程度往往会引入简化，从而增加幻觉和事实扭曲的风险。为应对这些挑战，我们首先构建了一个包含39,134条数据的数据集，以及一个以读者为中心的个性化科学传播基准（PSCB），该基准同时评估受众适配度和事实准确性。为降低数据和计算需求，同时提升跨领域和跨受众的泛化能力，我们引入了DA-MoE，通过分离建模显式地将受众适配与领域知识解耦。为在证据稀缺场景下实现稳健的验证与修订，我们提出了一种多智能体事实核查机制，该机制通过角色特定智能体辩论来增强有限证据，并在图上传播置信度。在PSCB上的实验表明，我们的方法达到了最先进的性能。我们的代码已开源，可在该https链接获取。
英文摘要
We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance. Our code is open-sourced at https://github.com/DPInnovationWorks/CWF.
