Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling
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Abstract (arXiv)
This study explores the potential of generative artificial intelligence (AI) models, specifically OpenAI's generative pre-trained transformer (GPT) series, when integrated with building information modeling (BIM) tools as an interactive design assistant for architectural design. The research involves the development and implementation of three key components: 1) BIM2XML, a component that translates BIM data into extensible markup language (XML) format; 2) Generative AI-enabled Interactive Architectural design (GAIA), a component that refines the input design in XML by identifying designer intent, relevant objects, and their attributes, using pre-trained language models; and 3) XML2BIM, a component that converts AI-generated XML data back into a BIM tool. This study validated the proposed approach through a case study involving design detailing, using the GPT series and Revit. Our findings demonstrate the effectiveness of state-of-the-art language models in facilitating dynamic collaboration between architects and AI systems, highlighting the potential for further advancements.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 25 Jun 2023.
Builds on
3 of 13 references.
References · 3
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023GPT-4 Technical Reportcited by 18,396
- 2021An adaptive artificial neural network-based generative design method for layout designscited by 5
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2025Large Language Model-Driven Code Compliance Checking in Building Information Modelingcited by 10
- 2023Generative AI in the Construction Industry: Opportunities & Challengescited by 8
- 2024A Generalized LLM-Augmented BIM Framework: Application to a Speech-to-BIM systemcited by 2
- 2025Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Studycited by 2
- 2026Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodingscited by 0
- 2026Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodings17 Feb 2026
- 2025Large Language Model-Driven Code Compliance Checking in Building Information Modeling25 Jun 2025
- 2025Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study13 Jun 2025
- 2024A Generalized LLM-Augmented BIM Framework: Application to a Speech-to-BIM system26 Sep 2024
- 2023Generative AI in the Construction Industry: Opportunities & Challenges19 Sep 2023