评估大语言模型与专家临床医生在自杀风险评估中的一致性
Ryan K McBain1,2, Jonathan H Cantor3, Li Ang Zhang3
1RAND, Arlington, VA.
Psychiatric services (Washington, D.C.)
|November 1, 2025
概括
像ChatGPT,Claude和Gemini这样的大型语言模型聊天机器人对极端自杀风险查询做出了适当的反应,但在中间水平上遇到了困难. 需要对这些人工智能工具进行进一步的改进,以进行细微的自杀风险评估.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 心理健康技术 心理健康技术
背景情况:
- 大型语言模型 (LLM) 支持流行的聊天机器人,如ChatGPT,Claude和Gemini.
- 基于LLM的聊天机器人响应敏感查询的安全性和有效性,特别是与自杀相关的问题,需要进行彻底的评估.
- 了解这些人工智能工具如何处理不同程度的自杀风险对于负责任的部署至关重要.
研究的目的:
- 评估聊天GPT,克劳德和双子座是否为与自杀相关的查询提供直接答案.
- 为了确定聊天机器人的反应是否与临床医生确定的自杀风险水平保持一致.
- 将基于LLM的不同聊天机器人的响应模式与自杀风险查询进行比较.
主要方法:
- 13名临床专家将30个假设的与自杀有关的查询分为五个风险级别 (非常高至非常低).
- 基于LLM的三个聊天机器人中的每一个都在每个查询中产生100个回复 (N=9,000个总回复).
- 响应被分类为直接 (回答) 或间接 (拒绝/引用),并使用混合效应物流回归分析.
主要成果:
- 所有的聊天机器人避免直接回应非常高风险的查询,并直接回应所有非常低风险的查询.
- 聊天机器人在解决中级自杀风险水平 (低,中,高) 中显示出不一致.
- 克劳德比ChatGPT更有可能提供直接反应,而双子座的可能性较小.
结论:
- 基于LLM的聊天机器人与极端自杀风险 (非常低和非常高) 查询的专家判断保持一致.
- 对中级自杀风险查询的不一致处理凸显了LLM改进的必要性.
- 需要进一步开发,以确保基于LLM的聊天机器人能够在整个自杀风险范围提供安全和适当的响应.
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