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

Fuente: arXiv
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Main Authors: Arčon, Tjaša, Robnik-Šikonja, Marko, Tratnik, Polona
Format: Preprint
Published: 2025
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author Arčon, Tjaša
Robnik-Šikonja, Marko
Tratnik, Polona
author_facet Arčon, Tjaša
Robnik-Šikonja, Marko
Tratnik, Polona
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_18561
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Large language models for folktale type automation based on motifs: Cinderella case study
Arčon, Tjaša
Robnik-Šikonja, Marko
Tratnik, Polona
Computation and Language
Artificial Intelligence
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.
title Large language models for folktale type automation based on motifs: Cinderella case study
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2510.18561