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A step-by-step method for cultural annotation by LLMs.

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

Summary

This summary is machine-generated.

This study introduces a pipeline using Large Language Models (LLMs) for annotating cultural big data. This method enables efficient, scalable analysis of cultural phenomena for researchers.

Keywords:

annotation loop automatic annotation human cultures large language models tutorial

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Area of Science:

Computational Social Science
Digital Humanities
Cultural Analytics

Background:

Large Language Models (LLMs) demonstrate increasing capabilities in processing complex data.
Existing research highlights the potential of LLMs for various analytical tasks.
Cultural big data presents unique challenges for traditional analysis methods.

Purpose of the Study:

To present a structured pipeline for annotating cultural big data using LLMs, specifically Generative Pre-trained Transformers (GPT).
To offer researchers a detailed methodology for leveraging GPT's computational power for cultural data analysis.
To showcase the potential of LLMs in the empirical study of human cultures.

Main Methods:

Development of a structured pipeline for LLM-based data annotation.
Application of Generative Pre-trained Transformers (GPT) for processing and interpreting cultural data.
Methodology designed for efficiency and scalability in analyzing large cultural datasets.

Main Results:

Demonstration of LLMs' proficiency in annotating descriptions of non-industrial societies.
Successful measurement of theme importance in narratives across different cultural contexts.
Evaluation of psychological constructs within texts spanning diverse societies and historical periods.

Conclusions:

LLMs offer a versatile tool for the empirical study of human cultures.
The proposed pipeline facilitates efficient and scalable analysis of cultural phenomena.
This approach has broad applications in cultural anthropology, psychology, history, and related fields.