Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales

翻译：传统的判别式方法在心理健康分析中以其强大的分类能力著称，但缺乏可解释性且需要大规模标注数据。基于大型语言模型（LLM）的生成式方法虽有望摆脱繁重标注并提供解释，但其能力仍逊于判别式方法，且由于解释生成过程是一个黑箱机制，其解释可能不可靠。受心理评估实践中使用量表评估心理状态的启发，我们提出的方法——结合心理量表的心理分析（MAIMS）——通过LLM集成了两个流程：首先，患者完成心理量表；其次，心理医生解读从心理量表中收集的信息并做出知情决策。实验结果表明，MAIMS优于其他零样本方法，且能基于心理量表输出生成更严谨的解释。