  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
