Ashwini Garole
Aditya Asabe
Mohini Jadhav
Shreepad Chavan
Shifa Gadiwale
- Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
- Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
- Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
- Student Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai Maharashtra India
- 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)]
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Journal of Artificial Intelligence Research & Advances
| Volume | 11 |
| Issue | 01 |
| Received | March 28, 2024 |
| Accepted | April 7, 2024 |
| Published | April 22, 2024 |
