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PsyFusion-RAG:多源知识融合的心理对话生成模型

PsyFusion-RAG: A Multi-source Knowledge Fusion Model for Psychological Dialogue Generation

  • 摘要: 传统心理咨询系统多局限于通用心理健康问答或基础疾病诊断功能,难以融合专业心理学范式实现对来访者的系统化评估与干预。同时,通用大型语言模型(Large Language Models, LLMs)在心理咨询场景下生成的建议常存在深度不足问题,且易出现模型幻觉现象。现有检索增强生成(Retrieval-Augmented Generation, RAG)方法多依赖单一知识源构建,难以适配复杂语义关联与异构知识结构的任务场景,致使生成内容在知识完备性与语义逻辑一致性方面存在局限。针对上述问题,该文提出 PsyFusion-RAG 方法,一种多源异构知识增强的心理咨询框架。该框架通过多源知识建模与跨结构融合机制,对异质知识结构进行有机整合,构建可多源融合检索的异构知识体系;同时,通过优化知识重排序策略与生成提示结构化设计,实现多源知识的高效检索与语义一致性生成。在心理咨询任务验证中,PsyFusion-RAG 表现出优异的知识整合能力与较高的语义一致性水平,借助多源信息的融合作用,显著提升了生成回答的专业性与解释深度。在多症状心理咨询测试集上,PsyFusion-RAG 的 5分评分比例达到70%,并在 70% 的样本中被评为最优回复,整体性能显著优于各类基线模型,可实现更具深度与专业性的心理咨询决策支持。

     

    Abstract: Current mental health consultation systems are often limited to generic psychological question-answer interactions or basic diagnostic functions, failing to achieve systematic diagnosis and intervention based on professional counseling methodologies. This paper proposes PsyFusion-RAG, a multi-source heterogeneous knowledge–augmented framework for psychological consultation. The framework performs multi-source knowledge modeling and cross-structure fusion to integrate heterogeneous knowledge representations, thereby forming a collaborative retrieval mechanism for precise and semantically consistent knowledge integration. Furthermore, it optimizes retrieval performance through knowledge re-ranking and structured prompting, ensuring efficiency and coherence in multi-source knowledge utilization. Experimental results on multi-symptom psychological consultation datasets demonstrate that PsyFusion-RAG achieves substantially higher high-score rates and optimal response proportions, highlighting its robustness and potential for supporting complex and context-aware psychological decision-making.

     

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