用多智能体模拟亲子性健康对话，提升真实性和多样性。
SafeTalkCoach: Diversity-Driven Multi-Agent Simulation for Parent-Teen Health Conversations
- 构建多智能体框架，融合真实指南与个性化角色设定。
- 生成对话兼具多样性、真实性和高质量沟通特征。
- 适合心理健康教育与AI对话系统研究者使用。
父母与子女关于性健康的沟通至关重要，但因隐私敏感，真实数据稀缺且难采集。尽管大语言模型广泛用于对话生成，常偏离最佳实践，缺乏真实感与多样性。本文提出SafeTalkCoach，一个以多样性驱动的多智能体对话生成框架，用于模拟亲子性健康对话，并构建配套数据集。该框架整合众包与合成场景、既定性健康指南、基于证据的角色设定、自适应控制模块及分层多样性机制。评估表明，SafeTalkCoach在保持对话真实性、沟通质量与可控性的前提下，生成多样化对话。本研究旨在为人工智能与健康传播实践提供支持。
原文摘要 · Abstract (English)
The importance of effective parent-child communication about sexual health is widely acknowledged, but real-world data on these conversations is scarce and challenging to collect, due to their private and sensitive nature. Although LLMs have been widely adopted in dialogue generation, they may deviate from best practices and frequently lack realism and diversity. We introduce SafeTalkCoach, a diversity-driven multi-agent dialogue generation framework that simulates parent-child conversations about sexual health, and present an accompanying dataset. SafeTalkCoach integrates crowd-sourced and synthesized scenarios, established sexual health guidelines, evidence-based personas, adaptive control modules, and hierarchical diversification. Through evaluations, we demonstrate that SafeTalkCoach generates diverse conversations while maintaining realism, communication quality, and controllability in practice. Our goal is that the SafeTalkCoach framework and the dataset support both AI research and health communications practices.
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