A Context-Aware Cultural Heritage Guide Powered by LLMs

Abstract:We present an extension of Triangolazioni (a Cultural Heritage webapp) to enrich curated content with context-dependent, external information provided by Large Language Models (LLMs) within a loosely-coupled architecture agnostic to the LLM. The system supports context-dependent information search and presentation within an architecture agnostic to the exploited LLM.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2608.00549 [cs.IR]

(or arXiv:2608.00549v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2608.00549 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

In Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '26). 2026

https://doi.org/10.1145/3774935.3812714 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.