用多智能体推理提升心理健康问答的深度理解与个性化回应
MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral Mental Health Question Answer
- 设计多智能体框架，模拟认知行为疗法中情绪与认知的交互机制
- 在心理问答任务中显著提升回复逻辑性与完整性，人类评估得分超基线12.7%
- 适合研究心理健康AI助手、具身智能对话系统的研究者与开发者
心理健康问答（MHQA）任务要求求助者与支持者在单轮对话中完成支持过程。由于求助者帖子内容丰富，支持者需全面理解其内容，并提供逻辑清晰、全面且结构合理的回应。以往研究多采用基于认知行为疗法（CBT）认知要素的单智能体方法，但忽略了情绪与认知等不同心理要素间的相互作用，限制了模型对求助者困扰的深入理解。为此，我们提出多智能体演绎规划（MADP）框架，通过模拟CBT中多种心理要素间的交互关系，引导大语言模型（LLMs）更深入理解求助者语境，提供更具个体化的支持。此外，我们基于MADP框架构建了一个新数据集，并用于微调得到专用模型MADP-LLM。通过与多个LLM的对比实验、人工评估及自动评估，验证了MADP框架与MADP-LLM的有效性。
原文摘要 · Abstract (English)
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.
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