# Enhancing Mental Health Counseling Support in Bangladesh using Culturally-Grounded Knowledge

arXiv: https://arxiv.org/abs/2604.14576 · HTML: https://mlsanity.com/paper/2604.14576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [PAL: Persona-Augmented Emotional Support Conversation Generation](https://mlsanity.com/paper/2212.09235/) (2022)
- [AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation](https://mlsanity.com/paper/2506.00551/) (2025)
- [An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives](https://mlsanity.com/paper/2310.17017/) (2023)
- [Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis](https://mlsanity.com/paper/2402.09151/) (2024)
- [ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data](https://mlsanity.com/paper/2505.14038/) (2025)
- [Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation](https://mlsanity.com/paper/2505.20014/) (2025)
- [A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit](https://mlsanity.com/paper/2505.17362/) (2025)
- [Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity](https://mlsanity.com/paper/2508.07902/) (2025)
- [ASHABot: An LLM-Powered Chatbot to Support the Informational Needs of Community Health Workers](https://mlsanity.com/paper/2409.10913/) (2024)

## Abstract (arXiv)

Large language models (LLMs) show promise in generating supportive responses for mental health and counseling applications. However, their responses often lack cultural sensitivity, contextual grounding, and clinically appropriate guidance. This work addresses the gap of how to systematically incorporate domain-specific, clinically validated knowledge into LLMs to improve counseling quality. We utilize and compare two approaches, retrieval-augmented generation (RAG) and a knowledge graph (KG)-based method, designed to support para-counselors. Our KG is constructed manually and clinically validated, capturing causal relationships between stressors, interventions, and outcomes, with contributions from multidisciplinary people. We evaluated multiple LLMs in both settings using BERTScore F1 and SBERT cosine similarity, as well as human evaluation across five metrics, which is designed to directly measure the effectiveness of counseling beyond similarity at the surface level. The results show that KG-based approaches consistently improve contextual relevance, clinical appropriateness, and practical usability compared to RAG alone, demonstrating that structured, expert-validated knowledge plays a critical role in addressing LLMs limitations in counseling tasks.