标题： MADP：增强认知行为心理健康问答的多智能体演绎规划 显示英文标题

标题： MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral Mental Health Question Answer

摘要： 心理健康问答（MHQA）任务要求寻求帮助者和支持者在一轮对话中完成支持过程。 鉴于求助者帖子内容的丰富性，支持者必须全面理解内容并提供逻辑性、全面性和结构良好的回应。 MHQA领域的先前工作主要集中在基于认知行为疗法（CBT）认知元素的单智能体方法上，但忽略了各种CBT元素之间的相互作用，例如情绪和认知。 这一限制阻碍了模型全面理解求助者困扰的能力。 为了解决这个问题，我们提出了一种名为多智能体演绎规划（MADP）的框架，该框架基于CBT的各种心理元素之间的相互作用。 这种方法引导大型语言模型（LLMs）更深入地理解求助者的上下文，并根据个人情况提供更个性化的帮助。 此外，我们基于MADP框架构建了一个新的数据集，并用它对LLMs进行微调，从而得到一个名为MADP-LLM的专业模型。 我们进行了广泛的实验，包括与多个LLMs的比较、人工评估和自动评估，以验证MADP框架和MADP-LLM的有效性。 显示英文摘要

摘要： The Mental Health Question Answer (MHQA) task requires the seeker and supporter to complete the support process in one-turn dialogue. Given the richness of help-seeker posts, supporters must thoroughly understand the content and provide logical, comprehensive, and well-structured responses. Previous works in MHQA mostly focus on single-agent approaches based on the cognitive element of Cognitive Behavioral Therapy (CBT), but they overlook the interactions among various CBT elements, such as emotion and cognition. This limitation hinders the models' ability to thoroughly understand the distress of help-seekers. To address this, we propose a framework named Multi-Agent Deductive Planning (MADP), which is based on the interactions between the various psychological elements of CBT. This method guides Large Language Models (LLMs) to achieve a deeper understanding of the seeker's context and provide more personalized assistance based on individual circumstances. Furthermore, we construct a new dataset based on the MADP framework and use it to fine-tune LLMs, resulting in a specialized model named MADP-LLM. We conduct extensive experiments, including comparisons with multiple LLMs, human evaluations, and automatic evaluations, to validate the effectiveness of the MADP framework and MADP-LLM.