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.

翻译：随着网络技术的发展，社交媒体文本成为自动心理健康分析的丰富来源。传统判别方法存在可解释性低的问题，因此近期探索利用大语言模型进行社交媒体上的可解释心理健康分析，旨在提供详细解释及预测结果。研究表明，ChatGPT能够为正确分类生成接近人类的解释。然而，大语言模型在以零样本/少样本方式进行心理健康分析时仍面临分类性能不佳的问题。领域特定微调是有效解决方案，但面临两大挑战：1）缺乏高质量训练数据；2）尚未有开源大语言模型用于可解释心理健康分析以降低微调成本。为解决这些问题，我们构建了首个多任务、多源的可解释心理健康指令数据集（IMHI），包含10.5万条数据样本，原始社交媒体数据来自涵盖8项心理健康分析任务的10个现有来源。我们利用专家编写的少样本提示和已收集标签来激发ChatGPT，并从其回复中提取解释。为确保解释可靠性，我们采用严格的自动和人工评估，检验生成数据的正确性、一致性和质量。基于IMHI数据集和LLaMA2基础模型，我们训练出MentalLLaMA——首个具备指令遵循能力的可解释心理健康分析开源大语言模型系列。我们还利用10个测试集在IMHI评估基准上测试MentalLLaMA的性能，考察其预测正确性和解释质量。结果表明，MentalLLaMA在正确性上接近最先进的判别方法，并能生成高质量解释。