面向健康模拟的个性化解释：以利益相关者为中心的摘要生成混合方法框架
Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
AI总结：
针对健康仿真模型因复杂难用、现有LLM生成的摘要“一刀切”无法匹配不同利益相关者需求的问题，提出一种混合方法框架，通过收集需求、优化LLM可控生成、综合评估来产出个性化健康模拟解释。
摘要：
建模与仿真（Modeling & Simulation, M&S）方法（如基于智能体的模型）在支持健康领域决策活动方面具有巨大潜力，近期应用案例包括疫苗接种推广，同时还有大量关于健康饮食行为和身体活动行为的研究文献。这些模型可供不同利益相关者群体使用：它们能帮助政策制定者评估潜在干预措施的效果，也能指导个体在复杂环境中做出健康选择。然而，由于模型本身的复杂性，这一潜力可能无法充分发挥，导致最能从中受益的利益相关者难以使用这些模型。\n尽管大语言模型（Large Language Models, LLMs）能够将仿真输出和模型设计转化为文本，但现有方法通常依赖“一刀切”的摘要，无法满足临床医生、政策制定者、患者、护理人员和健康倡导者等群体多样化的信息需求与风格偏好。这一局限源于一个根本缺口：我们尚未系统了解这些利益相关者对解释的具体需求，也不清楚如何据此定制解释内容。\n为填补这一缺口，我们提出了一个分步框架，用于识别利益相关者需求，并指导LLMs生成定制化的健康模拟解释。我们的流程采用混合方法设计：首先收集各类健康利益相关者的解释需求与风格偏好，随后优化LLMs生成定制化输出的能力（例如通过可控属性调优），最后通过一系列综合指标进行评估，以进一步优化定制化摘要的生成效果。
英文摘要：
Modeling & Simulation (M&S) approaches such as agent-based models hold significant potential to support decision-making activities in health, with recent examples including the adoption of vaccines, and a vast literature on healthy eating behaviors and physical activity behaviors. These models are potentially usable by different stakeholder groups, as they support policy-makers to estimate the consequences of potential interventions and they can guide individuals in making healthy choices in complex environments. However, this potential may not be fully realized because of the models' complexity, which makes them inaccessible to the stakeholders who could benefit the most. While Large Language Models (LLMs) can translate simulation outputs and the design of models into text, current approaches typically rely on one-size-fits-all summaries that fail to reflect the varied informational needs and stylistic preferences of clinicians, policymakers, patients, caregivers, and health advocates. This limitation stems from a fundamental gap: we lack a systematic understanding of what these stakeholders need from explanations and how to tailor them accordingly. To address this gap, we present a step-by-step framework to identify stakeholder needs and guide LLMs in generating tailored explanations of health simulations. Our procedure uses a mixed-methods design by first eliciting the explanation needs and stylistic preferences of diverse health stakeholders, then optimizing the ability of LLMs to generate tailored outputs (e.g., via controllable attribute tuning), and then evaluating through a comprehensive range of metrics to further improve the tailored generation of summaries.
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。
