@inproceedings{zhai-etal-2025-mentalglm,
title = "{M}ental{GLM} Series: Explainable Large Language Models for Mental Health Analysis on {C}hinese Social Media",
author = "Zhai, Wei and
Bai, Nan and
Zhao, Qing and
Li, Jianqiang and
Wang, Fan and
Qi, Hongzhi and
Jiang, Meng and
Wang, Xiaoqin and
Yang, Bing Xiang and
Fu, Guanghui",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.686/",
doi = "10.18653/v1/2025.emnlp-main.686",
pages = "13588--13603",
ISBN = "979-8-89176-332-6",
abstract = "With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain their decisions, yet they still underperform deep learning in complex psychological analysis. We present C-IMHI, the first multi-task Chinese social media interpretable mental health instruction dataset (9K samples) with quality control and manual validation. Additionally, we introduce MentalGLM, the first open-source Chinese LLMs for explainable mental health analysis, trained on 50K instructions. The proposed models excelled in three mental health downstream tasks, outperforming or matching deep learning and LLMs. A portion of the generated decision explanations was validated by experts, demonstrating promising accuracy and reliability. We evaluated the proposed models on a clinical dataset, where they significantly outperformed other LLMs, demonstrating their potential for clinical applications. Our models show strong performance, validated across tasks and domains. The decision explanations enhance usability and facilitate better understanding and practical application of the models. Both the constructed dataset and the models are publicly available via: https://github.com/zwzzzQAQ/MentalGLM."
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<abstract>With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain their decisions, yet they still underperform deep learning in complex psychological analysis. We present C-IMHI, the first multi-task Chinese social media interpretable mental health instruction dataset (9K samples) with quality control and manual validation. Additionally, we introduce MentalGLM, the first open-source Chinese LLMs for explainable mental health analysis, trained on 50K instructions. The proposed models excelled in three mental health downstream tasks, outperforming or matching deep learning and LLMs. A portion of the generated decision explanations was validated by experts, demonstrating promising accuracy and reliability. We evaluated the proposed models on a clinical dataset, where they significantly outperformed other LLMs, demonstrating their potential for clinical applications. Our models show strong performance, validated across tasks and domains. The decision explanations enhance usability and facilitate better understanding and practical application of the models. Both the constructed dataset and the models are publicly available via: https://github.com/zwzzzQAQ/MentalGLM.</abstract>
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%0 Conference Proceedings
%T MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media
%A Zhai, Wei
%A Bai, Nan
%A Zhao, Qing
%A Li, Jianqiang
%A Wang, Fan
%A Qi, Hongzhi
%A Jiang, Meng
%A Wang, Xiaoqin
%A Yang, Bing Xiang
%A Fu, Guanghui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhai-etal-2025-mentalglm
%X With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain their decisions, yet they still underperform deep learning in complex psychological analysis. We present C-IMHI, the first multi-task Chinese social media interpretable mental health instruction dataset (9K samples) with quality control and manual validation. Additionally, we introduce MentalGLM, the first open-source Chinese LLMs for explainable mental health analysis, trained on 50K instructions. The proposed models excelled in three mental health downstream tasks, outperforming or matching deep learning and LLMs. A portion of the generated decision explanations was validated by experts, demonstrating promising accuracy and reliability. We evaluated the proposed models on a clinical dataset, where they significantly outperformed other LLMs, demonstrating their potential for clinical applications. Our models show strong performance, validated across tasks and domains. The decision explanations enhance usability and facilitate better understanding and practical application of the models. Both the constructed dataset and the models are publicly available via: https://github.com/zwzzzQAQ/MentalGLM.
%R 10.18653/v1/2025.emnlp-main.686
%U https://aclanthology.org/2025.emnlp-main.686/
%U https://doi.org/10.18653/v1/2025.emnlp-main.686
%P 13588-13603
Markdown (Informal)
[MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media](https://aclanthology.org/2025.emnlp-main.686/) (Zhai et al., EMNLP 2025)
ACL
- Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, and Guanghui Fu. 2025. MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13588–13603, Suzhou, China. Association for Computational Linguistics.