Translating Regulatory Clauses into Executable Codes for Building Design Checking via Large Language Model Driven Function Matching and Composing
Not on the map. Why?
Abstract (arXiv)
Translating clauses into executable code is a vital stage of automated rule checking (ARC) and is essential for effective building design compliance checking, particularly for rules with implicit properties or complex logic requiring domain knowledge. Thus, by systematically analyzing building clauses, 66 atomic functions are defined first to encapsulate common computational logics. Then, LLM-FuncMapper is proposed, a large language model (LLM)-based approach with rule-based adaptive prompts that match clauses to atomic functions. Finally, executable code is generated by composing functions through the LLMs. Experiments show LLM-FuncMapper outperforms fine-tuning methods by 19% in function matching while significantly reducing manual annotation efforts. Case study demonstrates that LLM-FuncMapper can automatically compose multiple atomic functions to generate executable code, boosting rule-checking efficiency. To our knowledge, this research represents the first application of LLMs for interpreting complex design clauses into executable code, which may shed light on further adoption of LLMs in the construction domain.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 46 references that are in the map. First posted on arXiv 17 Aug 2023.
Builds on
3 of 46 references.
References · 3
- 2021Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?cited by 21
- 2022Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domaincited by 12
- 2017First-Order Logic with Counting: At Least, Weak Hanf Normal Forms Always Exist and Can Be Computed!cited by 9
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 90
- 2025FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinementcited by 5
- 2026LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliancecited by 1
- 2026Reinforcement learning to improve large language model-based automated code compliance systemscited by 0
- 2026BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Modelcited by 0
- 2026BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model2 Aug 2026
- 2026Reinforcement learning to improve large language model-based automated code compliance systems21 Jun 2026
- 2026LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance16 Apr 2026
- 2025FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement26 May 2025
- 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insights22 Jan 2024