了解用户对个性化LLM增强叙述干预的看法
Ananya Bhattacharjee1, Yuchen Zeng1, David C Mohr2
1Computer Science, University of Toronto Toronto, Ontario, Canada.
概括
大型语言模型 (LLM) 创建个性化故事, 这些人工智能增强的故事比人类写的更容易理解和有效.
科学领域:
- 心理学
- 人工智能
- 数字心理健康
背景情况:
- 个性化的叙述可以说明心理理论, 但可能缺乏个人共.
- 传统的故事难以与独特的个人挑战相联系.
- 数字心理健康干预需要相关且真实的内容.
研究的目的:
- 调查大型语言模型 (LLM) 在为年轻人创造个性化,可联系的心理健康叙述方面的有效性.
- 为了比较LLM增强的故事与人类撰写的故事对关键心理收藏和对负面思想的信念的影响.
- 确定数字心理健康中人工智能驱动的叙事干预的设计考虑因素.
主要方法:
- 一项涉及346名年轻成年人的研究.
- 使用大型语言模型 (LLM) 创建针对个体挑战的定制叙述.
- 比较参与者对LLM增强的故事与人类写的故事的看法.
主要成果:
- 通过LLM增强的故事被认为更好地传达关键的见解和促进反思.
- 个性化的人工智能叙述显示,人们对负面想法的信念有所减少.
- 参与者发现LLM增强的故事比人类写的更容易理解,更真实.
结论:
- 大型语言模型显示出创造有效,个性化的数字心理健康内容的巨大潜力.
- 未来以叙事为基础的干预应专注于保持人工智能产生的内容的相关性和可信性.
- 改进人工智能增强叙述的语气和措辞对于心理健康支持的最佳影响至关重要.
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