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

• URL: http://arxiv.org/abs/2604.14576v1
• Date: Thu, 16 Apr 2026 03:14:16 GMT
• Title: Enhancing Mental Health Counseling Support in Bangladesh using Culturally-Grounded Knowledge
• Abstract summary: Large language models (LLMs) show promise in generating supportive responses for mental health and counseling applications.<n>Their responses often lack cultural sensitivity, contextual grounding, and clinically appropriate guidance.<n>This work addresses the gap of how to systematically incorporate domain-specific, clinically validated knowledge into LLMs to improve counseling quality.
• Score: 10.52131905315523
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• A Multi-Agent LLM Framework for Rating the Quality of Surgical Feedback [52.053200111780626]
  Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident skill acquisition.<n>We introduce a two-stage LLM-based framework that discovers interpretable feedback quality criteria grounded in the context of surgical training.<n>Our method uses multi-agent and surgical domain knowledge injection to discover a small set of human interpretable scoring criteria.
  arXiv  Detail & Related papers  (2026-05-25T05:31:44Z)
• CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models [19.027335814014528]
  We present CARE, an LLM-based framework to automatically predict multi-dimensional alliance scores and generate interpretable rationales from counseling transcripts.<n> CARE is built on the CounselingWAI dataset and enriched with 9,516 expert-curated rationales.<n>Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance.
  arXiv  Detail & Related papers  (2026-02-24T07:52:56Z)
• Responsible Evaluation of AI for Mental Health [72.85175110624736]
  Current approaches to evaluating AI tools in mental health care are fragmented and poorly aligned with clinical practice, social context, and first-hand user experience.<n>This paper argues for a rethinking of responsible evaluation by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity.
  arXiv  Detail & Related papers  (2026-01-20T12:55:10Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning [3.5944459851781057]
  Family caregivers often face substantial mental health challenges.<n>This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support.
  arXiv  Detail & Related papers  (2025-06-13T00:47:57Z)
• Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
  We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-05-06T08:22:51Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance.<n>We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions.<n> Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models [10.258261180305439]
  Large language models (LLMs) offer a new approach to assessing complex communication metrics. LLMs offer the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities.
  arXiv  Detail & Related papers  (2024-09-23T16:39:12Z)
• Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT [6.932239020477335]
  Large language models (LLMs) are being used as adhoc therapists.<n>We compare the session-level behaviors of human counselors with those of an LLM prompted by a team of peer counselors to deliver single-session Cognitive Behavioral Therapy.
  arXiv  Detail & Related papers  (2024-09-03T19:19:13Z)
• Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs [18.605352662843575]
  We present an automatic approach using large language models (LLMs) to understand the development of therapeutic alliance in text-based counseling. We collect a comprehensive counseling dataset and conduct multiple expert evaluations on a subset based on this framework. Our findings underscore the challenges counselors face in cultivating strong online relationships with clients.
  arXiv  Detail & Related papers  (2024-02-19T09:00:10Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.