Title: Role-Playing LLM-Based Multi-Agent Support Framework for Detecting and Addressing Family Communication Bias Show Chinese title

Title: 基于角色扮演的LLM多智能体支持框架用于检测和解决家庭沟通偏见

Abstract: Well-being in family settings involves subtle psychological dynamics that conventional metrics often overlook. In particular, unconscious parental expectations, termed ideal parent bias, can suppress children's emotional expression and autonomy. This suppression, referred to as suppressed emotion, often stems from well-meaning but value-driven communication, which is difficult to detect or address from outside the family. Focusing on these latent dynamics, this study explores Large Language Model (LLM)-based support for psychologically safe family communication. We constructed a Japanese parent-child dialogue corpus of 30 scenarios, each annotated with metadata on ideal parent bias and suppressed emotion. Based on this corpus, we developed a Role-Playing LLM-based multi-agent dialogue support framework that analyzes dialogue and generates feedback. Specialized agents detect suppressed emotion, describe implicit ideal parent bias in parental speech, and infer contextual attributes such as the child's age and background. A meta-agent compiles these outputs into a structured report, which is then passed to five selected expert agents. These agents collaboratively generate empathetic and actionable feedback through a structured four-step discussion process. Experiments show that the system can detect categories of suppressed emotion with moderate accuracy and produce feedback rated highly in empathy and practicality. Moreover, simulated follow-up dialogues incorporating this feedback exhibited signs of improved emotional expression and mutual understanding, suggesting the framework's potential in supporting positive transformation in family interactions. Show Chinese abstract

Abstract: 在家庭环境中，幸福感涉及传统度量标准往往忽视的微妙心理动态。 特别是无意识的父母期望，称为理想父母偏差，可能会抑制孩子的感情表达和自主性。 这种压抑情绪，被称为被压抑的情绪，通常源于出于好意但带有价值观的沟通，从家庭外部很难察觉或处理。 专注于这些潜在的动态，本研究探讨了基于大型语言模型（LLM）的心理安全家庭沟通支持。 我们构建了一个包含30个场景的日本亲子对话语料库，每个场景都标注了关于理想父母偏差和被压抑情绪的元数据。 基于这个语料库，我们开发了一个基于角色扮演的LLM多智能体对话支持框架，该框架分析对话并生成反馈。 专业代理检测被压抑的情绪，描述父母话语中的隐含理想父母偏差，并推断上下文属性，如孩子的年龄和背景。 一个元代理将这些输出整理成结构化报告，然后传递给五个选定的专家代理。 这些代理通过结构化的四步讨论过程协作生成富有同理心且可操作的反馈。 实验表明，该系统可以以中等准确度检测被压抑情绪的类别，并生成在同理心和实用性方面评价很高的反馈。 此外，包含此反馈的模拟后续对话显示出情感表达和相互理解改善的迹象，表明该框架在支持家庭互动积极转变方面的潜力。