EmoStage: A Framework for Accurate Empathetic Response Generation via Perspective-Taking and Phase Recognition

2025-06-23

AI Summary

Contemporary AI-based counseling systems suffer from shallow psychological state understanding, poor stage-aware adaptation, heavy reliance on annotated data, and elevated privacy risks. To address these challenges, we propose a lightweight, fine-tuning-free empathic enhancement framework. It employs a perspective-taking mechanism to infer clients’ latent emotional needs and integrates a counseling-stage identification module to dynamically calibrate response timing. Leveraging the native reasoning capabilities of open-source large language models, the framework generates context-sensitive, emotionally resonant responses without requiring additional training data—thereby enhancing both privacy preservation and deployment feasibility. Evaluated on bilingual (Chinese–Japanese) counseling datasets, our approach significantly outperforms baseline models; its empathy quality and domain-specific professionalism match those of supervised learning methods. This work establishes a novel paradigm for AI-assisted helping technologies in low-resource, high-privacy-demand scenarios.

Technology Category

• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The rising demand for mental health care has fueled interest in AI-driven counseling systems. While large language models (LLMs) offer significant potential, current approaches face challenges, including limited understanding of clients' psychological states and counseling stages, reliance on high-quality training data, and privacy concerns associated with commercial deployment. To address these issues, we propose EmoStage, a framework that enhances empathetic response generation by leveraging the inference capabilities of open-source LLMs without additional training data. Our framework introduces perspective-taking to infer clients' psychological states and support needs, enabling the generation of emotionally resonant responses. In addition, phase recognition is incorporated to ensure alignment with the counseling process and to prevent contextually inappropriate or inopportune responses. Experiments conducted in both Japanese and Chinese counseling settings demonstrate that EmoStage improves the quality of responses generated by base models and performs competitively with data-driven methods.

Problem

• Enhance empathetic response generation in AI counseling
• Infer clients' psychological states without extra training data
• Align responses with counseling stages to avoid inappropriateness

Innovation

• Leverages open-source LLMs without extra training
• Uses perspective-taking for psychological state inference
• Incorporates phase recognition for counseling alignment