# Qwen-BIM: developing large language model for BIM-based design with domain-specific benchmark and dataset

arXiv: https://arxiv.org/abs/2602.20812 · HTML: https://mlsanity.com/paper/2602.20812/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements](https://mlsanity.com/paper/2605.22079/) (2026)
- [FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design](https://mlsanity.com/paper/2609.36064/) (2026)

## Abstract (arXiv)

As the construction industry advances toward digital transformation, BIM (Building Information Modeling)-based design has become a key driver supporting intelligent construction. Despite Large Language Models (LLMs) have shown potential in promoting BIM-based design, the lack of specific datasets and LLM evaluation benchmarks has significantly hindered the performance of LLMs. Therefore, this paper addresses this gap by proposing: 1) an evaluation benchmark for BIM-based design together with corresponding quantitative indicators to evaluate the performance of LLMs, 2) a method for generating textual data from BIM and constructing corresponding BIM-derived datasets for LLM evaluation and fine-tuning, and 3) a fine-tuning strategy to adapt LLMs for BIM-based design. Results demonstrate that the proposed domain-specific benchmark effectively and comprehensively assesses LLM capabilities, highlighting that general LLMs are still incompetent for domain-specific tasks. Meanwhile, with the proposed benchmark and datasets, Qwen-BIM is developed and achieves a 21.0% average increase in G-Eval score compared to the base LLM model. Notably, with only 14B parameters, performance of Qwen-BIM is comparable to that of general LLMs with 671B parameters for BIM-based design tasks. Overall, this study develops the first domain-specific LLM for BIM-based design by introducing a comprehensive benchmark and high-quality dataset, which provide a solid foundation for developing BIM-related LLMs in various fields.