## 2 Related Work
### Compliance Checks
#### 2.2.3 Automated Compliance Check  
Automating compliance checks and verification processes can significantly enhance efficiency and accuracy [79]. This empowers human experts to concentrate on higher-value endeavors, such as strategic decision making and intricate problem solving. Early approaches to automation, such as \"Black Box\" and \"Gray Box\" methods [80], relied heavily on hardcoded rules and manual interpretation [79]. These methods proved to be costly to maintain and challenging to adapt, and they lacked a generalized framework for modeling rules and regulations [79]. Semi-automated methods, exemplified by the RASE (requirement (R), applicabilities (A), selection (S), and exceptions (E)) methodology, aimed to translate regulatory texts into machine-processable formats [81]. While this approach improved efficiency, it still necessitated substantial manual intervention [79]. More advanced methods leverage natural language processing (NLP) techniques [82]. Rule-based NLP methods, although effective, demand significant manual effort in data training [79]. Domain-specific regulatory texts are generally more amenable to NLP analysis than general texts [83]. Semantic and syntactic information extraction methods, frequently combined with ontology and machine learning, offer a more automated approach [84]. In the general compliance automation field, some researchers focus on provision interpretation or requirement analysis [85; 86; 87; 88; 89; 90; 91], while others also cover compliance checking [92; 93; 94]. The technologies involved in these above works include AI, NLP, ML, etc. However, these methods encounter challenges in scalability and adaptability. NLP techniques, such as the rule-based approach, often necessitate extensive manual feature engineering [95] or manual verification for information [82], while deep learning models typically require large, annotated datasets for optimal performance [96; 97; 98]. Large language models (LLMs) such as GPT-4 [99], PaLM [100], Galactica [101], and Llama [102] offer promising solutions to these challenges. These models excel in few-shot learning and can understand complex language structures with minimal labeled data [103; 104]. They have the potential to adapt to evolving regulations and extract structured information from regulatory texts, making them useful tools for automated compliance checking [79; 105; 106; 107; 108]. Ref. [109] delved into the potential of large language models (LLMs) to enhance the explainability of AI systems. Ref. [110] explored the potential of using large language models (LLMs) like ChatGPT in the field of diagnostic medicine, particularly in digital pathology. Despite some challenges, LLMs offer significant potential to revolutionize diagnostic medicine by improving accuracy, efficiency, and accessibility [110]. In the architecture,engineering, construction, and operations (AECO) industry, the current methods for compliance evaluation are time-consuming and prone to errors [111]. Ref. [112] developed an LLM-based automatic compliance check (ACC) artifact to evaluate its performance in classifying regulations, identifying rule dependencies, and extracting information from BIM (building information modeling) models. The results demonstrate the potential of LLMs to significantly improve efficiency, transparency, and trust in building permitting. Ref. [113] provided a comprehensive overview of the potential applications of Chat-GPT and other large language models (LLMs) in the fields of accounting and finance. The review highlighted the potential benefits of LLMs in various tasks, including financial analysis, auditing, tax compliance, financial reporting, and educational purposes. Ref. [114] also discussed LLM's transformative potential in financial analysis, risk management, fraud detection, customer service, and regulatory compliance. Ref. [115] confirmed these opportunities but also outlined the challenges, such as data quality, model bias, and ethical considerations. Ref.