SafeTalkCoach: Diversity-Driven Multi-Agent Simulation for Parent-Teen Health Conversations
Benyamin Tabarsi, Wenbo Li, Tahreem Yasir, Aryan Santhosh Kumar, Laura Widman, Dongkuan Xu, and Tiffany Barnes

TL;DR
SafeTalkCoach is a multi-agent simulation framework that generates diverse, realistic parent-teen conversations about sexual health, aiding AI research and health communication practices.
Contribution
It introduces a novel diversity-driven multi-agent dialogue system and dataset for sensitive health conversations, addressing realism and diversity challenges in LLMs.
Findings
Generates diverse and realistic conversations
Maintains communication quality and controllability
Supports AI research and health communication
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Social Robot Interaction and HRI · Persona Design and Applications
