Spiritual-Emotion Guided Fine-Tuning of Mistral-7B for Mental Health Conversational Systems using Bhagavad Gita Knowledge
Spiritual-Emotion Guided Fine-Tuning of Mistral-7B for Mental Health Conversational Systems using Bhagavad Gita Knowledge
Authors:
Kokila Muppu
Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar, India
Spurthi Mulkanuri
Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar, India
Ashritha Simharaju
Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar, India
Sujoy Sarkar
Project Faculty, Department of CSE
Rajiv Gandhi University of Knowledge Technologies, India
Abstract—Artificial intelligence–based conversational agents have recently gained attention as scalable solutions for mental health assistance. Large Language Models (LLMs) demonstrate strong language understanding and generation capabilities; how- ever, their responses in counseling contexts often lack emotional grounding, empathy, and philosophical reasoning required for sensitive mental health interactions.
This research proposes a Spiritual-Emotion Guided Mental Health Dialogue System developed using parameter-efficient fine- tuning of the Mistral-7B-Instruct large language model. The proposed system integrates philosophical teachings from the Bhagavad Gita to improve emotional stability, reflective reasoning, and empathetic response generation in mental health conversations.
Bhagavad Gita scriptures are extracted from PDF sources using the PDFPlumber framework and converted into structured question–answer instruction pairs for supervised fine-tuning. The base model is adapted using Low-Rank Adaptation (LoRA), enabling efficient domain-specific learning while preserving the general capabilities of the pretrained model.
The proposed system is evaluated using a mental health counseling dialogue dataset to assess its effectiveness in generating supportive and contextually meaningful responses. The expected outcomes indicate improved emotional coherence, contextual understanding, and response quality compared to the baseline language model.
The findings aim to demonstrate that integrating philosophical knowledge with modern language models can enhance the effectiveness and empathy of AI-driven mental health dialogue systems.
Index Terms—Mental Health AI, Mistral-7B, LoRA, Bhagavad Gita, Psych8K, Emotional Intelligence, Parameter Efficient Fine- Tuning