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Addressing the Challenges of Mental Health Conversations with Large Language Models
Shiwakoti, Shuvam Shah, Siddhant Bikram Razzak, Imran Thapa, Surendrabikram Naseem, Usman
Shiwakoti, Shuvam
Shah, Siddhant Bikram
Razzak, Imran
Thapa, Surendrabikram
Naseem, Usman
Supervisor
Department
Computational Biology
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Virtual Mental Health Assistants offer a promising solution to address the growing demand for accessible and scalable mental healthcare. However, existing dialogue generation models struggle with the complexities inherent in mental health conversations. In this paper, we explore the limitations of current Medical Dialogue Generation models by conducting experiments on the large language model ChatMGL. We propose modifications to ChatMGL, including finetuning the model on a mental health dataset without proximal policy optimization and incorporating dialogue act labels, to enhance its ability to handle the complex nature of mental health dialogues. Our results demonstrate that these modifications outperform baseline models in terms of ROUGE and BERT scores. Our work suggests that specialized fine-tuning and incorporating domain-specific knowledge can improve the efficacy of virtual assistants for mental health support. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Citation
S. Shiwakoti, S. B. Shah, I. Razzak, S. Thapa, and U. Naseem, “Addressing the Challenges of Mental Health Conversations with Large Language Models,” pp. 2597–2602, May 2025, doi: 10.1145/3701716.3718374/SUPPL_FILE/WK2301-VIDEO.MP4
Source
WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
Conference
34th ACM Web Conference, WWW Companion 2025
Keywords
Computational Social Science, Large Language Models, Mental Health, Social Media
Subjects
Source
34th ACM Web Conference, WWW Companion 2025
Publisher
Association for Computing Machinery