ArTLLaMA: Adaptating LLaMA to Performative Art Applications

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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Performative Arts represent a compelling and underexplored domain for the application of Generative AI, given their rich conceptual complexity and cultural depth. This paper presents ArTLLaMA, a domain-adapted version of the LLaMA language model, designed to support natural language querying of ArTBase, the first national database of Italian theatres and theatre archives. We focus on the Text-to-SQL task: automatically translating user questions into executable SQL queries. Off-the-shelf models often fail in this setting due to a lack of domain knowledge and schema awareness. To bridge this gap, we propose a two-stage fine-tuning methodology: first, we train the model to internalize the Entity-Relationship (ER) schema of ArTBase; then, we fine-tune it on a curated set of over 800 natural language-SQL query pairs reflecting real use cases in the domain. Our results show that schema-informed fine-tuning significantly boosts accuracy, with the best model achieving over 70% exact match andgenerating correct SQL even for complex queries involving multi-table joins and aggregations. Compared to general purpose models like ChatGPT, our approach yields more accurate, schema-compliant outputs. Beyond technical improvements, this work underscores the value of interdisciplinary collaboration: by embedding domain knowledge from the humanities into AI systems, we enable new forms of access, interaction, and understanding of cultural heritage data.
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CCS Concepts: Artificial Intelligence → Machine learning; Natural Language Processing; Natural Language Generation

        
@inproceedings{
10.2312:dh.20253113
, booktitle = {
Digital Heritage
}, editor = {
Campana, Stefano
and
Ferdani, Daniele
and
Graf, Holger
and
Guidi, Gabriele
and
Hegarty, Zackary
and
Pescarin, Sofia
and
Remondino, Fabio
}, title = {{
ArTLLaMA: Adaptating LLaMA to Performative Art Applications
}}, author = {
Passone, Elisa
and
Borazio, Federico
and
Hromei, Claudiu Daniel
and
Croce, Danilo
and
Basili, Roberto
}, year = {
2025
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-277-6
}, DOI = {
10.2312/dh.20253113
} }
Citation