创造力的语言:来自人类和大型语言模型的证据
William Orwig1, Emma R Edenbaum1, Joshua D Greene1
1Harvard University.
The Journal of creative behavior
|May 3, 2024
概括
研究人员发现,丰富的感知细节和多样化的词汇的故事是语言创造力的关键指标. 人工智能生成的故事显示出与人类相似的创造力,暗示了先进的机器能力.
科学领域:
- 计算语言学计算语言学
- 创造力的心理学.
- 人工智能的人工智能是人工智能.
背景情况:
- 通过计算评分和语义距离,口头创造力的自动评估正在推进.
- 以前的研究重点是语义多样性作为创意文本的预测因素.
研究的目的:
- 扩展计算语言学方法来表征创意文本特征.
- 研究感知细节和语义多样性对创造力的预测能力.
- 探索生成语言模型 (如GPT-3和GPT-4) 以模仿人类创造力和补充数据收集.
主要方法:
- 收集了来自人类参与者和GPT-3的600篇短篇故事.
- 评估随机故事的创造性质量.
- 通过GPT-4生成的120个故事重复发现.
- 分析故事的语义多样性和感知细节的存在.
主要成果:
- 感知细节,以及语义多样性,强烈预测创造力.
- 人类和人工智能生成的故事之间的创造力评分没有显著差异.
- 在人类和人工智能对创造力的评估之间观察到积极的相关性.
结论:
- 感知细节对于创意写作至关重要,增强读者传输.
- 生成性语言模型展示了类似人类的创造性写作能力.
- 计算语言方法为创造力评估提供了强大的工具.
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