Mindwell: A Psychological Guide for Well-being

Year : 2024 | Volume :11 | Issue : 01 | Page : 70-83
By

Ashwini Garole

Aditya Asabe

Mohini Jadhav

Shreepad Chavan

Shifa Gadiwale

  1. Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
  2. Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
  3. Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
  4. Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
  5. Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India

Abstract

Mental health is a crucial aspect of overall well-being, yet access to professional therapy remains a significant challenge for many individuals due to various barriers, including cost, availability, and stigma. This research aims to develop an accessible and effective mental health therapy chatbot, named Mindwell Psychology, leveraging the power of large language models (LLMs) and state-of-the-art natural language processing techniques. The primary objective of this study is to create a conversational AI system capable of providing personalized, empathetic, and evidence-based psychological support to users. By harnessing the capabilities of Google’s GEMMA-2B, a cutting-edge LLM, and employing the LLaMA-Factory framework, we fine-tuned the model on a curated dataset of therapeutic conversations and mental health resources. The proposed approach involves curating a large dataset of mental health-related conversations and resources, preprocessing and formatting the data for model training, and fine-tuning the GEMMA-2B model using the LLaMA-Factory framework. The fine-tuned model is then integrated into a user-friendly web application, enabling seamless interaction with the Mindwell Psychology chatbot. Evaluation of the chatbot’s performance was conducted using a combination of quantitative metrics, such as perplexity and BLEU scores, and qualitative analysis of sample conversations. The results demonstrate the chatbot’s ability to provide empathetic and relevant responses, offering psychological support and coping strategies tailored to the user’s specific needs. The creation of the Mindwell Psychology chatbot marks a notable advancement in improving access to mental health assistance and fostering overall wellness. By leveraging cutting-edge language models and natural language processing techniques, this research contributes to the field of conversational AI and its application in the mental health domain.

Keywords: Mental Health Therapy, Chatbot, Artificial intelligence, Machine learning, Power, Large Language Models

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Ashwini Garole, Aditya Asabe, Mohini Jadhav, Shreepad Chavan, Shifa Gadiwale. Mindwell: A Psychological Guide for Well-being. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):70-83.
How to cite this URL: Ashwini Garole, Aditya Asabe, Mohini Jadhav, Shreepad Chavan, Shifa Gadiwale. Mindwell: A Psychological Guide for Well-being. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):70-83. Available from: https://journals.stmjournals.com/joaira/article=2024/view=143959




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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received March 28, 2024
Accepted April 7, 2024
Published April 22, 2024