用《薄伽梵歌》智慧增强大模型心理支持，显著提升情感回应深度。
Spiritual-LLM : Gita Inspired Mental Health Therapy In the Era of LLMs
- 融合《薄伽梵歌》智慧与GPT-4o生成10,729条精神引导回复。
- 在Phi3-Mini 3.2B模型上，精神洞察力提升15.92%，指标改善超120%。
- 适合关注心灵疗愈、宗教哲学与AI结合研究者参考。
传统心理健康支持系统常仅基于用户当前情绪和情境生成回应，导致干预浅层化，难以触及深层情感需求。本研究提出一种新框架，将《薄伽梵歌》的灵性智慧与先进大语言模型GPT-4o结合，以提升情绪福祉。我们构建了GITes（Gita Integrated Therapy for Emotional Support）数据集，通过专家评估，对现有ExTES心理支持数据集扩充了10,729条由GPT-4o生成的精神引导回复。在12种先进大模型（含专用及通用模型）上进行基准测试。为超越传统n-gram度量，提出全新“灵性洞察”指标，并采用链式思维提示的LLM裁判框架实现自动化评估。结果显示，在最佳模型Phi3-Mini 3.2B Instruct上，相较于零样本版本，其在ROUGE、METEOR、BERT评分、灵性洞察、充分性与相关性等指标分别提升122.71%、126.53%、8.15%、15.92%、18.61%与13.22%。这些成果表明，融入灵性指导的AI系统能显著提升自动评估中的共情与精神维度表现，但需在真实患者群体中进一步验证。研究结果提示，灵性增强型AI或可显著提升用户满意度与感知支持效果。代码与数据集将公开，以推动该新兴领域研究。
原文摘要 · Abstract (English)
Traditional mental health support systems often generate responses based solely on the user's current emotion and situations, resulting in superficial interventions that fail to address deeper emotional needs. This study introduces a novel framework by integrating spiritual wisdom from the Bhagavad Gita with advanced large language model GPT-4o to enhance emotional well-being. We present the GITes (Gita Integrated Therapy for Emotional Support) dataset, which enhances the existing ExTES mental health dataset by including 10,729 spiritually guided responses generated by GPT-4o and evaluated by domain experts. We benchmark GITes against 12 state-of-the-art LLMs, including both mental health specific and general purpose models. To evaluate spiritual relevance in generated responses beyond what conventional n-gram based metrics capture, we propose a novel Spiritual Insight metric and automate assessment via an LLM as jury framework using chain-of-thought prompting. Integrating spiritual guidance into AI driven support enhances both NLP and spiritual metrics for the best performing LLM Phi3-Mini 3.2B Instruct, achieving improvements of 122.71% in ROUGE, 126.53% in METEOR, 8.15% in BERT score, 15.92% in Spiritual Insight, 18.61% in Sufficiency and 13.22% in Relevance compared to its zero-shot counterpart. While these results reflect substantial improvements across automated empathy and spirituality metrics, further validation in real world patient populations remains a necessary step. Our findings indicate a strong potential for AI systems enriched with spiritual guidance to enhance user satisfaction and perceived support outcomes. The code and dataset will be publicly available to advance further research in this emerging area.
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