Large language models for folktale type automation based on motifs: Cinderella case study

TL;DR

This paper presents a methodology using large language models and NLP to analyze motifs in Cinderella folktale variants, enabling large-scale, cross-lingual folkloristic analysis.

Contribution

It introduces a novel approach combining machine learning and NLP for motif detection and analysis in folklore studies, demonstrating large language models' effectiveness.

Findings

Large language models detect complex interactions in folktales

Method enables cross-lingual comparison of folktale motifs

Facilitates large-scale computational analysis of folklore texts

Abstract

Artificial intelligence approaches are being adapted to many research areas, including digital humanities. We built a methodology for large-scale analyses in folkloristics. Using machine learning and natural language processing, we automatically detected motifs in a large collection of Cinderella variants and analysed their similarities and differences with clustering and dimensionality reduction. The results show that large language models detect complex interactions in tales, enabling computational analysis of extensive text collections and facilitating cross-lingual comparisons.