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大型语言模型可以对叙事事件进行细分,类似于人类

Sebastian Michelmann¹, Manoj Kumar^2,3, Kenneth A Norman^2,3

¹Department of Psychology, New York University, New York, NY, USA. s.michelmann@nyu.edu.

Behavior research methods

|January 3, 2025

概括

研究人员使用GPT-3自动识别文本中的事件边界,模仿人类事件感知. 在 GPT-3 中使用.

科学领域:

认知科学认知科学
计算语言学计算语言学
人工智能的人工智能

背景情况:

人类事件感知包括将连续的经验细分为离散的事件.
量化事件边界通常依赖于汇总多个人类注释.
需要自动化方法来有效地分析大型数据集中的事件结构.

研究的目的:

调查使用大型语言模型 (GPT-3) 在叙事文本中自动检测事件边界的可行性.
为了比较GPT-3的事件注释与人类衍生的注释.
评估GPT-3作为研究人类事件感知工具的潜力.

主要方法:

GPT-3被用来将连续的叙事文本分割成不同的事件.
由GPT-3识别的事件边界与人类观察员识别的事件边界在统计学上是相关的.
将GPT-3的注释与从平均人类注释中得出的共识解决方案进行了比较.

主要成果:

GPT-3成功地将叙事文本细分为有意义的事件.
来自GPT-3的事件注释与人类注释有显著的相关性.
GPT-3确定了接近人类共识的事件边界,平均而言,其表现优于个人人类注释者.

结论:

像GPT-3这样的大型语言模型为自动事件注释提供了可行的计算方法.
在事件细分方面的GPT-3的表现表明其预测能力与人类认知过程之间的平行.
使用GPT-3的自动事件注释可以加速对人类事件感知原理的研究.

关键词:

事件边界事件的边界.事件认知事件认知.事件细分事件细分事件细分大型语言模型.

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