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Mental health challenges in China are rising, yet early intervention remains limited due to stigma, high costs, and therapist shortages. This paper presents a culturally adapted Large Language Model (LLM) that integrates Cognitive Behavioral Therapy (CBT), psychodynamic therapy, and psychoanalysis to provide scalable, anonymous support. Using 10,000 anonymized dialogues from “壹心理,” we generate multi-framework responses with GPT-4o-mini and fine-tune Qwen-0.5B using Guided Reinforcement Parameter Optimization (GRPO). Evaluated against GPT-3.5, untuned Qwen-0.5B, and licensed therapists, our model shows superior performance in triage relevance and user motivation, despite its compact size. The system reduces labeling effort, aligns with Chinese cultural norms, and offers a promising direction for AI-assisted early intervention.

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Fine-Tuning Large Language Models for Early Mental Health Intervention in China: A Culturally Adapted Triage and Therapy Framework

  • 会议论文

  • 发表日期：

    2025年

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  • Yuting Shi

摘要

Mental health challenges in China are rising, yet early intervention remains limited due to stigma, high costs, and therapist shortages. This paper presents a culturally adapted Large Language Model (LLM) that integrates Cognitive Behavioral Therapy (CBT), psychodynamic therapy, and psychoanalysis to provide scalable, anonymous support. Using 10,000 anonymized dialogues from “壹心理,” we generate multi-framework responses with GPT-4o-mini and fine-tune Qwen-0.5B using Guided Reinforcement Parameter Optimization (GRPO). Evaluated against GPT-3.5, untuned Qwen-0.5B, and licensed therapists, our model shows superior performance in triage relevance and user motivation, despite its compact size. The system reduces labeling effort, aligns with Chinese cultural norms, and offers a promising direction for AI-assisted early intervention.