利用指令微调大语言模型优化心理咨询
Optimizing Psychological Counseling with Instruction-Tuned Large Language Models
AI总结：
针对心理健康服务需求增长的问题，本文提出用专业提示对大语言模型进行指令微调的心理咨询优化方法，经专业咨询师反馈优化数据集与多维度评估，模型性能优于基线模型，可提供可扩展的心理支持工具。
摘要：
大语言模型（LLMs）的出现已显著推动了自然语言处理、自动化对话系统等多个领域的发展。本文探讨大语言模型在心理咨询领域的应用，以应对不断增长的心理健康服务需求。我们提出一种使用专用提示对大语言模型进行指令微调的方法，以提升其生成共情性、相关性与支持性回复的表现。该方法包括构建一个涵盖心理咨询专用提示的综合数据集，通过专业咨询师的反馈对提示进行优化，并采用自动指标与人工评估相结合的方式开展严格评测。结果表明，我们的指令微调模型性能优于多个基线大语言模型，凸显了其作为可扩展、易获取的心理健康支持工具的潜力。
英文摘要：
The advent of large language models (LLMs) has significantly advanced various fields, including natural language processing and automated dialogue systems. This paper explores the application of LLMs in psychological counseling, addressing the increasing demand for mental health services. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses. Our approach involves developing a comprehensive dataset of counseling-specific prompts, refining them through feedback from professional counselors, and conducting rigorous evaluations using both automatic metrics and human assessments. The results demonstrate that our instruction-tuned model outperforms several baseline LLMs, highlighting its potential as a scalable and accessible tool 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。
