With the development of web technology, social media texts are becoming a
rich source for automatic mental health analysis. As traditional discriminative
methods bear the problem of low interpretability, the recent large language
models have been explored for interpretable mental health analysis on social
media, which aims to provide detailed explanations along with predictions. The
results show that ChatGPT can generate approaching-human explanations for its
correct classifications. However, LLMs still achieve unsatisfactory
classification performance in a zero-shot/few-shot manner. Domain-specific
finetuning is an effective solution, but faces 2 challenges: 1) lack of
high-quality training data. 2) no open-source LLMs for interpretable mental
health analysis were released to lower the finetuning cost. To alleviate these
problems, we build the first multi-task and multi-source interpretable mental
health instruction (IMHI) dataset on social media, with 105K data samples. The
raw social media data are collected from 10 existing sources covering 8 mental
health analysis tasks. We use expert-written few-shot prompts and collected
labels to prompt ChatGPT and obtain explanations from its responses. To ensure
the reliability of the explanations, we perform strict automatic and human
evaluations on the correctness, consistency, and quality of generated data.
Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA,
the first open-source LLM series for interpretable mental health analysis with
instruction-following capability. We also evaluate the performance of
MentalLLaMA on the IMHI evaluation benchmark with 10 test sets, where their
correctness for making predictions and the quality of explanations are
examined. The results show that MentalLLaMA approaches state-of-the-art
discriminative methods in correctness and generates high-quality explanations.

开发了第一个用于社交媒体上的可解释性心理健康分析的开源语言模型系列 MentalLLaMA，并通过 IMHI 数据集在正确性和解释质量方面接近最先进的判别方法。