增强AI驱动的心理咨询：基于大语言模型的分层提示
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models
AI总结：
针对心理咨询专业人员短缺和可扩展性差的问题，提出基于GPT-4的分层提示系统，结合共情驱动和场景提示增强情感智能，实验证明其显著提升了AI心理咨询的响应质量。
摘要：
心理咨询对改善心理健康和幸福感至关重要，但合格专业人员短缺和可扩展性问题等挑战限制了其可及性。为应对这些挑战，我们探索使用GPT-4等大语言模型（LLMs）来增强心理咨询服务。我们的方法引入了一种新颖的分层提示系统，该系统能动态适应用户输入，从而实现全面且相关的信息收集。我们还开发了共情驱动和基于场景的提示，以增强LLM在治疗环境中的情感智能和情境理解能力。我们通过使用新收集的心理咨询对话数据集进行实验验证了该方法，结果显示响应质量有显著提升。结果突显了我们的提示工程技术在增强AI驱动心理咨询方面的潜力，为满足日益增长的心理健康支持需求提供了可扩展且易于获取的解决方案。
英文摘要：
Psychological consultation is essential for improving mental health and well-being, yet challenges such as the shortage of qualified professionals and scalability issues limit its accessibility. To address these challenges, we explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input, enabling comprehensive and relevant information gathering. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence and contextual understanding in therapeutic settings. We validated our approach through experiments using a newly collected dataset of psychological consultation dialogues, demonstrating significant improvements in response quality. The results highlight the potential of our prompt engineering techniques to enhance AI-driven psychological consultation, offering a scalable and accessible solution to meet the growing demand for mental health support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
