用心理理论打造个性育儿大模型，能懂孩子脾气并定制建议
PediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment
- 结合儿童心理学构建气质知识图谱，支持个性化推理
- 两阶段训练提升逻辑性与共情能力，通过多选测试验证效果
- 适合智能育儿、儿童心理研究等敏感场景的定制化应用
本文提出PediaMind-R1，一种面向早期儿童照护的领域专用大语言模型，旨在实现智能育儿中的主动个性化。与提供通用建议的传统系统不同，PediaMind-R1借鉴发展心理学思想，引入Thomas-Chess气质理论，构建0-3岁婴幼儿气质知识图谱。采用两阶段训练：先通过监督微调教授结构化思维链推理，再利用基于GRPO的对齐阶段强化逻辑一致性、领域专业性和共情照护策略。我们设计了包含气质敏感型多选题和人工评估的评测框架。结果表明，PediaMind-R1能准确解读婴幼儿气质特征，并主动进行个性化推理。该工作凸显了垂直领域建模与心理理论融合的价值，为发展以用户为中心的个性化大模型提供了新范式。
原文摘要 · Abstract (English)
This paper presents PediaMind-R1, a domain-specialized large language model designed to achieve active personalization in intelligent parenting scenarios. Unlike conventional systems that provide generic suggestions, PediaMind-R1 draws on insights from developmental psychology. It introduces temperament theory from the Thomas-Chess framework and builds a temperament knowledge graph for infants and toddlers (0-3 years). Our two-stage training pipeline first uses supervised fine-tuning to teach structured chain-of-thought reasoning, and then applies a GRPO-based alignment stage to reinforce logical consistency, domain expertise, and empathetic caregiving strategies. We further design an evaluation framework comprising temperament-sensitive multiple-choice tests and human assessments. The results demonstrate that PediaMind-R1 can accurately interpret early childhood temperament profiles and proactively engage in individualized reasoning. This work highlights the value of integrating vertical-domain modeling with psychological theory. It offers a novel approach to developing user-centered LLMs that advance the practice of active personalization in sensitive caregiving contexts.
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