Large language models for folktale type automation based on motifs: Cinderella case study
This work addresses the need for large-scale analyses in folkloristics, specifically for automating motif detection in folktales, but it is incremental as it builds on existing AI methods in digital humanities.
The researchers tackled the problem of automating motif detection in folktales by applying large language models to a collection of Cinderella variants, resulting in successful detection of complex interactions and enabling computational analysis and cross-lingual comparisons.
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.