大规模比较人类和大型语言模型的不同创造力
Dawei Wang1,2, Difang Huang3,4, Haipeng Shen5
1Faculty of Business and Economics, University of Hong Kong, Hong Kong, China. dwdw@hku.hk.
Nature human behaviour
|December 23, 2025
概括
人类在产生新想法方面略高于大型语言模型 (LLM). 虽然人类表现出更大的创造性变化,但LLM的表现可以通过特定提示来提高,但不是通过人格采用.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 人机伙伴关系对于解决复杂的社会问题至关重要.
- 了解人类和人工智能的比较创新能力至关重要,但未被充分探索.
研究的目的:
- 为了比较人类和大型语言模型 (LLM) 的想法生成创造力.
- 调查影响LLM创意的因素,并确定最佳的人机协作策略.
主要方法:
- 这是一项大规模的创造性任务,涉及9,198名人类参与者和215,542个LLM观测.
- 分析不同条件下的创造力分布和性能变化.
主要成果:
- 人类的创造力,平均而言,略高于LLM的创造力.
- 人类在创造力方面表现出更大的变化,特别是在最高水平.
- 通过某些提示,LLM创造力得到了改善,但基于人格的指令是无效的或反作用的.
结论:
- 人类目前在创造性创意产生方面比LLM有轻微的优势.
- 士创造力对提示工程敏感,但类似人类的人格采用不是可靠的增强策略.
- 这些发现有助于开发有效的人机协作,用于创新和解决问题.
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