探索大型语言模型在总结心理健康咨询会议中的有效性:基准研究
Prottay Kumar Adhikary1, Aseem Srivastava2, Shivani Kumar2
1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
JMIR mental health
|July 23, 2024
概括
最先进的大型语言模型 (LLM) 显示出对总结心理健康治疗会话的希望. 然而,专家评估表明,这些模型需要进一步改进,以便可靠的临床使用.
科学领域:
- 在心理健康中的自然语言处理.
- 人工智能用于临床支持
- 在心理治疗中的计算语言学.
背景情况:
- 手动总结心理健康咨询会议是耗时的.
- 有效的会议摘要对于护理连续性和治疗规划至关重要.
- 现有的自动总结方法往往无法捕捉咨询细微差别.
研究的目的:
- 评估最先进的大型语言模型 (LLM) 用于基于方面的治疗会议总结.
- 在选择性地总结不同的咨询组件中对LLM绩效进行基准测试.
- 评估人工智能驱动的总结对心理健康专业人员的有用性.
主要方法:
- 开发一个新的数据集:心理健康咨询-组件指导对话摘要 (191次会议).
- 评估11个LLM的咨询对话的基于方面的总结.
- 使用ROUGE和BERTScore进行定量评估,并通过定性专家审查进行补充.
主要成果:
- 特定任务的LLM (MentalLlama,Mistral,MentalBART) 在量化指标 (ROUGE,BERTScore) 上表现优于其他.
- 专家审查表明,在6个关键参数中,Mistral超越了MentalLlama和MentalBART.
- 所有模型都在总结机会成本和感知效率方面表现出局限性.
结论:
- 对心理健康数据进行微调的LLM可以获得更高的自动评估分数.
- 专家评估显示,目前的LLM尚未可靠直接临床应用.
- 进一步的研究和验证对于在精神卫生保健中的实际实施至关重要.
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