发表机构
Soochow University; Nanyang Technological University（苏州大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对心理健康咨询中双向理解不足和关系知识整合缺失的问题，提出BiGraph-Diffuse双向扩散语言模型及BiGraph-RAG图检索策略，二者相互增强，实验验证有效。
AI 中文摘要
心理健康障碍影响着全球数亿人，但获得专业咨询的机会仍然严重受限。基于人工智能的对话系统提供了一种可扩展的替代方案，但现有模型面临两个基本挑战。首先，它们缺乏捕捉情感表达分层特性所需的双向理解能力，尤其是在渐进式披露的情况下，客户往往在表面层面呈现症状，同时隐藏更深层的创伤。自回归（AR）模型按顺序处理信息，当对话后期出现新证据时，无法修正早期的解释。其次，它们未能有效整合构成临床推理基础的关系知识。在本文中，我们提出了BiGraph-Diffuse，这是首个针对咨询领域定制的大规模扩散语言模型。我们进一步引入了BiGraph-RAG，一种无关系图结构检索策略，仅依赖轻量级实体提取和语义链接。这种设计保留了从可观察症状到潜在根本原因的推理路径，同时在索引过程中不产生任何LLM令牌成本。重要的是，这两个模块并非简单组合，而是相互增强。扩散模型提供了整体的双向上下文，使系统能够在渐进式披露过程中推迟过早的判断。同时，基于图的检索捕获了临床知识的结构化相互联系。大量实验证明了BiGraph-Diffuse的有效性，我们进一步提供了坚实的理论分析来支持其设计。
英文摘要
Mental health disorders affect hundreds of millions of people around the world, yet access to professional counseling remains severely limited. AI-powered dialogue systems offer a scalable alternative, but existing models face two fundamental challenges. First, they lack the bidirectional understanding needed to capture the layered nature of emotional expression, particularly in cases of progressive disclosure, where clients often present symptoms at the surface-level while concealing deeper trauma. Autoregressive (AR) models process information sequentially and cannot revise early interpretations when new evidence emerges later in the conversation. Second, they fail to effectively incorporate the relational knowledge that underlies clinical reasoning. In this paper, we propose \textbf{BiGraph-Diffuse}, the first large-scale diffusion language model tailored for the counseling domain. We further introduce \textbf{BiGraph-RAG}, a relation-free graph-structured retrieval strategy that relies only on lightweight entity extraction and semantic linking. This design preserves inferential pathways from observable symptoms to potential underlying causes, while incurring zero LLM token cost during indexing. Importantly, these two modules are not merely combined but mutually reinforcing. The diffusion model provides a holistic bidirectional context, enabling the system to defer premature judgments during progressive disclosure. Meanwhile, graph-based retrieval captures the structured interconnections of clinical knowledge. Extensive experiments demonstrate the effectiveness of BiGraph-Diffuse, and we further provide a solid theoretical analysis to support its design.
Commentssubmit to ieee transactions on affective computing
