CogEmp: A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling

Traditional psychological counseling struggles to meet public demand due to high costs, social stigma, and limited accessibility. Recently, large language models (LLMs) have shown great potential in healthcare, offering new opportunities to build accessible mental health dialogue systems. However, current LLMs often lack accurate modeling of cognitive empathy, especially the ability to understand users' emotions and their underlying psychological causes. To address this, we propose CogEmp, a dialogue generation model tailored for the Chinese cultural context that integrates cognitive empathy. The model follows a three-stage decision pipeline: emotion and cause recognition, contextual understanding, and empathetic response generation. First, the model identifies the user's fine-grained emotions and their underlying causes within the Chinese context, laying the foundation for personalized emotional comprehension. Then, it retrieves semantically similar counseling cases to extract topic and strategy information, thereby constructing a context-aware representation. Finally, guided by the extracted multi-dimensional cues, the model drives LLMs to generate empathetic responses that are both contextually appropriate and professionally grounded. Experiments conducted on Chinese mental health datasets show that CogEmp outperforms existing approaches in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism. The complete code can be obtained at https://github.com/leixia3/CogEmp.