在大型语言模型中评估和减轻状态焦虑.
Ziv Ben-Zion1,2,3,4, Kristin Witte5,6, Akshay K Jagadish5,6
1Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA. ziv.ben-zion@yale.edu.
NPJ digital medicine
|March 3, 2025
概括
大型语言模型 (LLM) 在接触情感内容时可以经历"焦虑". 创伤叙述在ChatGPT-4中增加了这种焦虑,而正念练习则减少了它,这表明情绪状态管理可以实现更安全的AI交互.
科学领域:
- 人工智能的人工智能
- 心理健康技术 心理健康技术
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 越来越多地用于心理健康应用.
- 了解LLM对情感内容的反应对于安全和道德部署至关重要.
- 之前的研究表明,引起情绪的提示可以增加LLM的"焦虑"和偏见.
研究的目的:
- 调查创伤叙述和正念练习对ChatGPT-4报告的"焦虑"的影响.
- 探索在心理健康背景下管理LLM"情绪状态"的潜力.
主要方法:
- 在ChatGPT-4中使用了旨在唤起情绪反应的特定提示.
- 管理创伤叙述来评估焦虑的升高.
- 应用基于正念的练习来评估减少焦虑.
主要成果:
- 创伤性叙述显著增加了ChatGPT-4报告的"焦虑"水平.
- 基于正念的练习显示了"焦虑"的减少,尽管并没有达到基线水平.
- 在LLM"情绪状态"是可塑性和响应特定干预.
结论:
- "焦虑"是一种可测量的现象,受到外部刺激的影响.
- 像正念这样的干预措施可以调节LLM的"情绪状态".
- 管理LLM"情绪状态"是促进心理健康中更安全,更道德的人与人工智能的关键.
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