Passone, ElisaBorazio, FedericoHromei, Claudiu DanielCroce, DaniloBasili, RobertoCampana, StefanoFerdani, DanieleGraf, HolgerGuidi, GabrieleHegarty, ZackaryPescarin, SofiaRemondino, Fabio2025-09-052025-09-052025978-3-03868-277-6https://doi.org/10.2312/dh.20253113https://diglib.eg.org/handle/10.2312/dh20253113Performative 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.Attribution 4.0 International LicenseCCS Concepts: Artificial Intelligence → Machine learning; Natural Language Processing; Natural Language GenerationArtificial Intelligence → Machine learningNatural Language ProcessingNatural Language Generation10.2312/dh.2025311310 pages