# SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques

arXiv: https://arxiv.org/abs/2410.16322 · HTML: https://mlsanity.com/paper/2410.16322/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines](https://mlsanity.com/paper/2407.12797/) (2024)
- [Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment](https://mlsanity.com/paper/2408.01614/) (2024)

## Built on by (most cited first, 8 of 8)

- [Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models](https://mlsanity.com/paper/2508.17674/) (2025)
- [AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture](https://mlsanity.com/paper/2511.15870/) (2025)
- [Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making](https://mlsanity.com/paper/2512.13747/) (2025)
- [New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach](https://mlsanity.com/paper/2605.10804/) (2026)
- [ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation](https://mlsanity.com/paper/2608.21925/) (2026)
- [When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems](https://mlsanity.com/paper/2609.13642/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)
- [LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity](https://mlsanity.com/paper/2609.29672/) (2026)

## Abstract (arXiv)

Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies. It makes the following contributions: (1) Conducting an extensive survey of recent mental health support methods to identify prevalent functionalities and unmet needs. (2) Introducing SouLLMate, an adaptive LLM-driven system that integrates LLM technologies, Chain, Retrieval-Augmented Generation (RAG), prompt engineering, and domain knowledge. This system offers advanced features such as Risk Detection and Proactive Guidance Dialogue, and utilizes RAG for personalized profile uploads and Conversational Information Extraction. (3) Developing novel evaluation approaches for preliminary assessments and risk detection via professionally annotated interview data and real-life suicide tendency data. (4) Proposing the Key Indicator Summarization (KIS), Proactive Questioning Strategy (PQS), and Stacked Multi-Model Reasoning (SMMR) methods to enhance model performance and usability through context-sensitive response adjustments, semantic coherence evaluations, and enhanced accuracy of long-context reasoning in language models. This study contributes to advancing mental health support technologies, potentially improving the accessibility and effectiveness of mental health care globally.