@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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.686/",
    pages = "13599--13614",
    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."
}