An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling
🤖 AI Summary
Existing open dialogue datasets for mental health lack authenticity and diversity, while the implicit nature of psychological processes—particularly in clients—hampers fidelity in synthetic dialogue generation. Method: This paper proposes an Embodied Conversational Agent (ECA) simulation framework tailored for psychological counseling, integrating embodied cognition theory with clinical practice and leveraging large language models for memory-augmented generation. It establishes the first theory-guided embodied memory space and a scalable ECA paradigm. Six principled design objectives are introduced, with dialogue modeling driven by high-frequency counseling questions and empirically validated on the D4 dataset. Contribution/Results: Experiments demonstrate that generated dialogues significantly outperform baselines in authenticity and necessity; licensed counselors confirm their professional validity; and a high-quality public ECA dataset has been released.
Application Category
📝 Abstract
Simulation is crucial for validating algorithmic strategies in real-world scenarios. While LLM-based social simulation shows promise as a mainstream tool, simulating complex scenarios like psychological counseling remains challenging. We present ECAs (short for Embodied Conversational Agents), a framework for simulating psychological counseling clients' embodied memory, integrating embodied cognition and counseling theories. We formulate six design goals based on a comprehensive review of psychological counseling theories. Using LLMs, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogues based on high-frequency counseling questions. We validate our framework using the D4 dataset, with evaluations by licensed counselors. Results show our approach significantly outperforms baselines in simulation authenticity and necessity. To demonstrate scalability, we created a public ECAs dataset through batch simulations. This research provides valuable insights for future social simulation studies in psychological counseling and Embodied Counseling Agents research.
Problem
Research questions and friction points this paper is trying to address.
Addressing authenticity challenges in synthetic mental health dialogue data
Improving diversity of psychological counseling simulations through embodied agents
Incorporating psychological theories into LLM-based conversational agent frameworks
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-based embodied conversational agent simulation framework
Expands counseling data into cognitive memory space
Generates dialogue using high-frequency counseling questions
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2024-02-28Citations: 1
