CAMA: A Culturally Adaptive Multi-Agent Framework for Postpartum Depression Support in Multilingual and Low-Resource Settings

Zhiqi Zhang, Ziyi LIU, rite Bo

Keywords: Cultural Adaptation, Dialect-aware NLP, Multi-agent Systems, Mental Health Support, Empathetic Dialogue, Human-centred AI, Postpartum Support

Abstract:

Large language models (LLMs) enable scalable conversational support for postpartum depression (PPD), yet current systems insufficiently account for intra-lingual cultural variation even within high-resource languages such as Chinese. Dialectal phrasing, local idioms, and culturally embedded expressions (e.g., Northeastern Mandarin "zhabayue de teng" (humorous discomfort) or the Southern Min "xin-gua-a-tia$^\text{n}$" (deep sorrow)) often produce misinterpretation, safety-critical ambiguity, or emotionally inappropriate responses in PPD-related dialogues.

We introduce CAMA (Culturally Adaptive Multi-Agent Co-Design Framework), a lightweight cultural-sensitivity detection and alignment framework that identifies dialect-specific linguistic cues and supplements LLMs with contextual socio-cultural grounding without performing clinical diagnosis. Our approach integrates culturally aware prompting and intervention logic to enhance empathy, safety, relevance, and user trust.

This work highlights that cultural fairness in mental-health LLMs must consider intra-language diversity, not only cross-lingual disparity. CAMA provides a practical pathway towards culturally aligned, safe, and trustworthy mental-health dialogue systems.

Submission Number: 34