MADP：用于增强认知行为心理健康问答的多智能体演绎规划
MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral Mental Health Question Answer
AI总结：
本文提出多智能体演绎规划框架MADP，通过建模CBT各心理要素间的交互引导LLMs深入理解求助者并提供个性化支持，并构建新数据集微调出专用模型MADP-LLM，实验验证了其有效性。
摘要：
心理健康问答（MHQA）任务要求求助者和支持者在单轮对话中完成支持过程。鉴于求助者帖子内容丰富，支持者必须彻底理解内容并提供逻辑严密、全面且结构良好的回复。以往在MHQA中的工作大多侧重于基于认知行为疗法（CBT）认知要素的单智能体方法，但忽略了情绪和认知等各种CBT要素之间的相互作用。这一局限阻碍了模型彻底理解求助者困扰的能力。为解决此问题，我们提出了一个名为多智能体演绎规划（MADP）的框架，该框架基于CBT各种心理要素之间的相互作用。该方法引导大型语言模型（LLMs）实现对求助者情境的更深入理解，并根据个人情况提供更个性化的帮助。此外，我们基于MADP框架构建了一个新数据集，并使用它对LLMs进行微调，从而得到一个名为MADP-LLM的专用模型。我们进行了广泛的实验，包括与多个LLMs的比较、人类评估和自动评估，以验证MADP框架和MADP-LLM的有效性。
英文摘要：
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.
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。
