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心理-LLM:利用大型语言模型通过在线文本数据来预测心理健康

Xuhai Xu¹, Bingsheng Yao², Yuanzhe Dong³

¹Massachusetts Institute of Technology & University of Washington, USA.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies

|February 10, 2025

概括

指令微调显著增强用于心理健康预测任务的大型语言模型 (LLM). 微调模型的性能优于基于提示的LLM,并且与最新技术相匹配,尽管道德考虑仍然至关重要.

科学领域:

人工智能的人工智能
计算语言学计算语言学
心理健康信息学心理健康信息学

背景情况:

大型语言模型 (LLM) 在各种应用中显示出潜力.
有限的研究存在于LLM能力和心理健康任务的增强.

研究的目的:

在心理健康预测任务上全面评估多个LLM.
调查提示技术和微调对心理健康LLM绩效的影响.

主要方法:

评估了阿尔帕卡,阿尔帕卡-洛拉,FLAN-T5,GPT-3.5和GPT-4使用零射击,少数射击和指令微调.
对在线文本数据进行实验,用于心理健康预测.
进行了对LLM心理健康推理能力的探索性案例研究.

主要成果:

零射击和少数射击提示显示出有希望但有限的性能.
指令微调显著改善了所有任务的LLM性能.
精心调整的模型 (Mental-Alpaca,Mental-FLAN-T5) 性能优于GPT-3.5和GPT-4提示符设计,并与最先进的模型相匹配.
GPT-4展示了有前途的推理能力.

结论:

指令微调是提高心理健康LLM的一个关键方法.
虽然有前途,但在实际部署之前,LLM需要进一步开发以解决诸如偏见和道德风险等局限性.
提供了指导方针,以提高在心理健康应用中的LLM能力.

关键词:

指令微调是指指令的微调.大型语言模型心理健康心理健康

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