Spiritual-LLM : Gita Inspired Mental Health Therapy In the Era of LLMs

2025-06-23

AI Summary

Traditional psychological support systems often provide only superficial emotional responses, failing to address deeper affective and spiritual needs. To bridge this gap, we propose the first spiritually grounded AI intervention framework: (1) We construct GITes, a spirituality-enhanced psychological support dataset derived from the Bhagavad Gita; (2) We formalize “spiritual insight” as a novel evaluation metric and design an LLM-as-jury automated assessment protocol; (3) We generate spiritually informed responses using GPT-4o, validated via chain-of-thought prompting, multi-dimensional NLP metrics (ROUGE, METEOR, BERTScore), and expert adjudication. Evaluated on Phi-3-mini-3.2B-Instruct, our approach achieves a 122.71% ROUGE-L improvement and a 15.92% gain in spiritual insight over baselines, with significantly enhanced psychological and spiritual support efficacy. This work establishes a new paradigm for spiritually guided AI-based psychological intervention.

Technology Category

• Humans and AI: Emotional Intelligence
• Philosophy and Ethics of AI: Artificial General Intelligence
• Intelligent Robots: Embodied AI

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Enhance emotional well-being with spiritual wisdom integration
• Address superficial mental health interventions with deeper support
• Evaluate spiritual relevance in AI-generated therapy responses

Innovation

• Integrates Bhagavad Gita wisdom with GPT-4o
• Introduces GITes dataset with spiritual responses
• Proposes Spiritual Insight metric for evaluation