MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media
This work addresses the need for explainable and flexible models for mental health analysis on Chinese social media, offering a novel dataset and models that could benefit researchers and practitioners in psychology and AI, though it is incremental in combining existing LLM techniques with a new domain-specific application.
The authors tackled the problem of analyzing mental health on Chinese social media by introducing the first multi-task Chinese Social Media Interpretable Mental Health Instructions (C-IMHI) dataset with 9K samples and proposing MentalGLM series models, the first open-source LLMs for explainable analysis, trained on 50K instructions, which achieved better or comparable performance on three downstream tasks and outperformed other LLMs on a clinical dataset.
As the prevalence of mental health challenges, social media has emerged as a key platform for individuals to express their emotions.Deep learning tends to be a promising solution for analyzing mental health on social media. However, black box models are often inflexible when switching between tasks, and their results typically lack explanations. With the rise of large language models (LLMs), their flexibility has introduced new approaches to the field. Also due to the generative nature, they can be prompted to explain decision-making processes. However, their performance on complex psychological analysis still lags behind deep learning. In this paper, we introduce the first multi-task Chinese Social Media Interpretable Mental Health Instructions (C-IMHI) dataset, consisting of 9K samples, which has been quality-controlled and manually validated. We also propose MentalGLM series models, the first open-source LLMs designed for explainable mental health analysis targeting Chinese social media, trained on a corpus of 50K instructions. The proposed models were evaluated on three downstream tasks and achieved better or comparable performance compared to deep learning models, generalized LLMs, and task fine-tuned LLMs. We validated a portion of the generated decision explanations with experts, showing promising results. We also evaluated the proposed models on a clinical dataset, where they outperformed other LLMs, indicating their potential applicability in the clinical field. Our models show strong performance, validated across tasks and perspectives. 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.