Abstract:Building Information Modeling (BIM) is widely used in the Architecture, Engineering, and Construction (AEC) industry, but the complexity of Industry Foundation Classes (IFC) limits accessibility for non-expert users. To address this, we introduce IfcLLM, a hybrid framework for natural language interaction with IFC-based BIM models. It transforms IFC models into complementary representations: a relational representation for structured element properties and geometry, and a graph representation for topological relationships. These representations are integrated through iterative retry-and-refine LLM reasoning. We implement the framework using an open-weight LLM (GPT OSS 120B), supporting reproducible and deployment-oriented workflows. Evaluation on three IFC models with queries derived from 30 scenarios shows first-attempt accuracy of 93.3%-100%, with all failures recovered using a fallback LLM. The results show that combining complementary representations with iterative reasoning enables more accessible natural language querying of IFC data while supporting routine BIM analysis tasks.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.13236 [cs.CL] |
| (or arXiv:2605.13236v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13236 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rabindra Lamsal [view email]
[v1]
Wed, 13 May 2026 09:24:51 UTC (4,096 KB)