在大型语言模型中评估和塑造人格特征的心理测量框架
Gregory Serapio-García1,2,3, Mustafa Safdari1, Clément Crepy1
1Google DeepMind, London, UK.
Nature machine intelligence
|December 24, 2025
概括
研究人员开发了一种方法来测量和塑造大型语言模型 (LLM) 的个性. 这种心理测量方法发现,LLM人格可以可靠地评估和定制,这对负责任的AI开发有影响.
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.
- 心理测量 心理测量 心理测量
背景情况:
- 大型语言模型 (LLM) 由于训练数据而表现出合成人格特征.
- 这些特征影响了在LLM驱动的应用程序中的沟通效率.
- 评估和塑造LLM个性对于广泛的公众使用至关重要.
研究的目的:
- 介绍一种用于评估LLM人格的心理测量方法.
- 在LLM输出中验证人格测量.
- 为了使LLM个性在生成文本中形成.
主要方法:
- 在18个大型语言模型上进行和验证人格测试.
- 使用特定的提示配置进行人格评估.
- 开发方法来沿着所需的维度塑造LLM个性.
主要成果:
- 在某些条件下,LLM输出中的人格测量可以是可靠和有效的.
- 较大,对指令进行微调的模型显示出更强有力的可靠和有效的合成人格证据.
- 士的个性可以成功地被塑造成模仿特定的人类形象.
结论:
- 为LLM人格评估和塑造建立了一个强大的心理测量方法.
- 这些发现支持在LLMs中可靠地测量和故意修改合成个性.
- 讨论了有关LLM个性的负责任的人工智能开发的伦理考虑.
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