@inproceedings{hasan-etal-2026-enhancing,
title = "Enhancing Mental Health Counseling Support in {B}angladesh using Culturally-grounded Knowledge",
author = "Hasan, Md Arid and
Sp, Azhagu Meena and
Khan, Aditya and
Bhuiyan, Abu and
Ahmed, Helal and
Debi, Joysree and
Sadeque, Farig and
Lee, Annie and
Ahmed, Syed Ishtiaque",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.13/",
pages = "164--177",
ISBN = "979-8-89176-421-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Mental Health Counseling Support in Bangladesh using Culturally-grounded Knowledge
%A Hasan, Md Arid
%A Sp, Azhagu Meena
%A Khan, Aditya
%A Bhuiyan, Abu
%A Ahmed, Helal
%A Debi, Joysree
%A Sadeque, Farig
%A Lee, Annie
%A Ahmed, Syed Ishtiaque
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F hasan-etal-2026-enhancing
%X 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.
%U https://aclanthology.org/2026.clpsych-1.13/
%P 164-177
Markdown (Informal)
[Enhancing Mental Health Counseling Support in Bangladesh using Culturally-grounded Knowledge](https://aclanthology.org/2026.clpsych-1.13/) (Hasan et al., CLPsych 2026)
ACL
- Md Arid Hasan, Azhagu Meena Sp, Aditya Khan, Abu Bhuiyan, Helal Ahmed, Joysree Debi, Farig Sadeque, Annie Lee, and Syed Ishtiaque Ahmed. 2026. Enhancing Mental Health Counseling Support in Bangladesh using Culturally-grounded Knowledge. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 164–177, San Diego, California, USA. Association for Computational Linguistics.