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通过LLMs进行文化注释的逐步方法

Edgar Dubourg¹, Valentin Thouzeau¹, Nicolas Baumard¹

¹Département d'études cognitives, Institut Jean Nicod, École normale supérieure, Université PSL, Paris, France.

Frontiers in artificial intelligence

|May 17, 2024

概括

本研究介绍了使用大型语言模型 (LLM) 进行注释的文化大数据的管道. 这种方法使研究人员能够对文化现象进行高效,可扩展的分析.

科学领域:

计算社会科学计算社会科学
数字人文学科数字人文学科
文化分析文化分析

背景情况:

大型语言模型 (LLM) 在处理复杂数据方面展示了越来越大的能力.
现有研究强调了LLMs在各种分析任务中的潜力.
文化大数据对传统分析方法提出了独特的挑战.

研究的目的:

通过使用LLM,特别是生成预训练型变换器 (GPT) 来说明文化大数据的结构化管道.
为研究人员提供详细的方法来利用GPT的计算能力进行文化数据分析.
展示LLMs在人类文化的实证研究中的潜力.

主要方法:

基于LLM的数据注释结构化管道的开发.
用生成预训练变压器 (GPT) 来处理和解释文化数据的应用.
为分析大型文化数据集的效率和可扩展性而设计的方法.

主要成果:

证明法学士在注释非工业社会描述方面的熟练程度.
在不同文化背景的叙事中成功测量主题的重要性.
在跨越不同社会和历史时期的文本中评估心理构造.

结论:

法学士课程为人类文化的实证研究提供了一个多功能工具.
拟议的管道有助于对文化现象进行有效和可扩展的分析.
这种方法在文化人类学,心理学,历史和相关领域有广泛的应用.

关键词:

这是一个注释循环.自动注释自动注释人类文化人类文化大型语言模型.这是一个自学教程.

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