Large Language Models for Museum Label Optimization: A Case Study on Ministerial Compliance and Cultural Accessibility in Galleria Sabauda's Flemish Collection

This paper examines how Large Language Models (LLMs) can be applied to optimize art museum labels to meet specific cultural accessibility needs and ministerial guidelines and best practices in matters of exhibit texts redaction. The paper illustrates the first results of an experiment on optimizing the current labels of the Flemish section of the Galleria Sabauda in Turin. The study tests three state-of-art-LLMs, GPT4.o mini, Claude 3.7 and DeepSeek V-3, according to three different few-shot prompting techniques. An evaluation grid is created to assess the performance of LLMs in this rewriting task considering both content optimization and readability and formatting aspects.