Dynamic Strategy Chain: Dynamic Zero-Shot CoT for Long Mental Health
  Support Generation
Qi Chen Dexi Liu
Long counseling Text Generation for Mental health support (LTGM), an
innovative and challenging task, aims to provide help-seekers with mental
health support through a comprehensive and more acceptable response. The
combination of chain-of-thought (CoT) prompting and Large Language Models
(LLMs) is employed and get the SOTA performance on various NLP tasks,
especially on text generation tasks. Zero-shot CoT prompting is one of the most
common methods in CoT prompting. However, in the LTGM task, Zero-shot CoT
prompting can not simulate a counselor or provide personalized strategies
without effective mental health counseling strategy prompts. To tackle this
challenge, we propose a zero-shot Dynamic Strategy Chain (DSC) prompting
method. Firstly, we utilize GPT2 to learn the responses written by mental
health counselors and dynamically generate mental health counseling strategies
tailored to the help-seekers' needs. Secondly, the Zero-shot DSC prompting is
constructed according to mental health counseling strategies and the
help-seekers' post. Finally, the Zero-shot DSC prompting is employed to guide
LLMs in generating more human-like responses for the help-seekers. Both
automatic and manual evaluations demonstrate that Zero-shot DSC prompting can
deliver more human-like responses than CoT prompting methods on LTGM tasks.