Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support

• URL: http://arxiv.org/abs/2509.04456v1
• Date: Wed, 27 Aug 2025 03:44:56 GMT
• Title: Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support
• Authors: Anandi Dutta, Shivani Mruthyunjaya, Jessica Saddington, Kazi Sifatul Islam,
• Abstract summary: Mentalic Net Conversational AI has a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges.<n>We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of large language models (LLMs) has unlocked boundless possibilities, along with significant challenges. In response, we developed a mental health support chatbot designed to augment professional healthcare, with a strong emphasis on safe and meaningful application. Our approach involved rigorous evaluation, covering accuracy, empathy, trustworthiness, privacy, and bias. We employed a retrieval-augmented generation (RAG) framework, integrated prompt engineering, and fine-tuned a pre-trained model on novel datasets. The resulting system, Mentalic Net Conversational AI, achieved a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges. We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies, recognizing both their potential to change lives and the risks they may pose if not carefully managed.

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