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.

翻译：长篇幅心理支持咨询文本生成任务（LTGM）是一项创新且具有挑战性的任务，旨在通过全面且更易接受的回复为求助者提供心理健康支持。思维链提示与大语言模型的结合已在多种自然语言处理任务（尤其是文本生成任务）中取得最优性能。零样本思维链提示是思维链提示中最常用的方法之一。然而在LTGM任务中，零样本思维链提示由于缺乏有效的心理健康咨询策略提示，既无法模拟咨询师的行为，也无法提供个性化策略。为应对这一挑战，我们提出零样本动态策略链提示方法。首先，利用GPT2学习心理健康咨询师撰写的回复，动态生成符合求助者需求的心理健康咨询策略；其次，根据心理健康咨询策略与求助者帖子构建零样本动态策略链提示；最后，采用零样本动态策略链提示引导大语言模型为求助者生成更具人性化的回复。自动评估与人工评估均表明，在LTGM任务中，零样本动态策略链提示相比思维链提示方法能生成更接近人类专家的回复。