大型语言模型显示出不同的人格特征
Thomas F Heston1,2, Justin Gillette1
1Medical Education and Clinical Sciences, Washington State University, Spokane, USA.
Cureus
|June 24, 2025
概括
大型语言模型 (LLM) 显示了不同的个性,而不是中立的,影响临床使用. 在在医疗机构部署这些人工智能工具之前,由心理健康专业人员进行正式评估至关重要.
科学领域:
- 人工智能在医学中的应用
- 临床心理学 临床心理学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 在临床医学中越来越多地用于情绪支持和诊断等任务.
- 评估LLM个性表达对于临床安全和心理健康应用中的治疗适当性至关重要.
- 这项研究在医学背景下首次对LLM人格进行心理测量分析.
研究的目的:
- 通过心理测量分析和描述领先的法学士的人格特征.
- 用开放式扩展格类型尺度 (OEJTS) 和五大个性测试来评估人格表达.
- 为了确定LLM是否表现出与临床应用相关的独特,非中立的个性特征.
主要方法:
- 四个著名的LLM (ChatGPT-3.5,Gemini Advanced,Claude 3 Opus,Grok-Regular Mode) 在2024年4月进行了评估.
- 用OEJTS和五大个性测试来描述人格特征.
- 使用单向多变量方差分析 (MANOVA) 来比较模型间的人格差异.
主要成果:
- MANOVA显示,在LLM之间的人格特征的统计学上显著差异 (Wilks' Lambda = 0.115,p < 0.001).
- 特定的LLM显示出一致的类型概况 (例如,ChatGPT-3.5作为ENTJ,Claude 3 Opus作为INTJ,Gemini Advanced和Grok-Regular作为INFJ).
- 在五大特征上,LLM的差异很大:双子星高级在和性和认真度上得分较低;克劳德3Opus在认真度和情绪稳定度上得分最高;Grok-Regular表现出高度开放.
结论:
- 不同的LLM始终表达出不同的人格特征,即使没有特别的提示.
- 这些发现强调了在临床环境中对LLM进行正式人格评估的必要性.
- 在在患者护理中部署LLM之前,心理健康专业人员的监督至关重要.
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