Large Language Models for Structured Information Processing in Construction and Facility Management

Abstract

This study examines how the integration of structured information affects the performance of large language models (LLMs) in the context of facility management. The aim is to determine to what extent structured data such as maintenance schedules, room information, and asset inventories can improve the accuracy, correctness, and contextual relevance of LLM-generated responses. We focused on scenarios involving function calling of a database with building information. Three use cases were developed to reflect different combinations of structured and unstructured input and output. The research follows a design science methodology and includes the implementation of a modular testing prototype, incorporating empirical experiments using various LLMs (Gemini, Llama, Qwen, and Mistral). The evaluation pipeline consists of three steps: user query translation (natural language into SQL), query execution, and final response (translating the SQL query results into natural language). The evaluation was based on defined criteria such as SQL execution validity, semantic correctness, contextual relevance, and hallucination rate. The study found that the use cases involving function calling are mostly successful. The execution validity improved up to 67% when schema information is provided.