# Translating Regulatory Clauses into Executable Codes for Building Design Checking via Large Language Model Driven Function Matching and Composing

arXiv: https://arxiv.org/abs/2308.08728 · HTML: https://mlsanity.com/paper/2308.08728/

## 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 (most cited first, 3 of 3)

- [Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?](https://mlsanity.com/paper/2112.12653/) (2021)
- [Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain](https://mlsanity.com/paper/2203.04729/) (2022)
- [First-Order Logic with Counting: At Least, Weak Hanf Normal Forms Always Exist and Can Be Computed!](https://mlsanity.com/paper/1703.01122/) (2017)

## Built on by (most cited first, 5 of 5)

- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement](https://mlsanity.com/paper/2505.20192/) (2025)
- [LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance](https://mlsanity.com/paper/2604.15589/) (2026)
- [Reinforcement learning to improve large language model-based automated code compliance systems](https://mlsanity.com/paper/2606.22402/) (2026)
- [BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model](https://mlsanity.com/paper/2608.11244/) (2026)

## 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.