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

arXiv: https://arxiv.org/abs/2509.04456 · HTML: https://mlsanity.com/paper/2509.04456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025)
- [Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support](https://mlsanity.com/paper/2310.08017/) (2023)
- [Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling](https://mlsanity.com/paper/2301.12729/) (2023)
- [Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study](https://mlsanity.com/paper/2405.19519/) (2024)
- [Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders](https://mlsanity.com/paper/2308.07407/) (2023)
- [Habit Coach: Customising RAG-based chatbots to support behavior change](https://mlsanity.com/paper/2411.19229/) (2024)
- [Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor](https://mlsanity.com/paper/2412.17572/) (2024)

## Abstract (arXiv)

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.