动态策略链：用于长文本心理健康支持生成的动态零样本思维链
Dynamic Strategy Chain: Dynamic Zero-Shot CoT for Long Mental Health Support Generation
AI总结：
针对零样本思维链提示在心理健康长咨询文本生成中缺乏策略、无法个性化的问题，提出动态策略链方法，通过GPT2动态生成适配策略引导大模型生成回复，性能优于传统零样本思维链方法。
摘要：
心理健康支持长咨询文本生成（LTGM）是一项兼具创新性与挑战性的任务，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（CoT）提示与大语言模型（LLMs）的结合已在各类自然语言处理（NLP）任务，尤其是文本生成任务中取得了SOTA（当前最优）性能。零样本CoT提示是CoT提示中最常用的方法之一。但在LTGM任务中，零样本CoT提示缺乏有效的心理健康咨询策略提示，无法模拟咨询师的工作模式，也无法提供个性化策略。为解决这一问题，我们提出了零样本动态策略链（DSC）提示方法。首先，我们利用GPT2学习心理健康咨询师撰写的回复，动态生成适配求助者需求的心理健康咨询策略。其次，结合心理健康咨询策略与求助者的发帖内容，构建零样本DSC提示。最后，使用零样本DSC提示引导LLMs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DSC提示相比CoT提示方法能生成更具人性化的回复。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
