## 6 Conclusions  
In this study, we not only explored and compared the performance of three pre-trained language models--ERNIE, Llama, and SQLCoder--in executing NL2 SQL tasks within Building Information Modeling (BIM) scenarios but also constructed a high-quality dataset tailored explicitly for the BIM field to support this advanced research. Through customized model fine-tuning, we found that the SQLCoder model excels in most standard query tasks. In contrast, the ERNIE model stands out for its robust generalization ability in handling fuzzy and colloquial natural language queries. By combining the strengths of these two models, we designed an innovative two-stage query scheme: first, the ERNIE model is used for raw language input, and then, a fine-tuned SQLCoder model is used to generate precise, structured BIM query instructions, significantly improving query efficiency.  
However, our research also revealed some potential limitations. The accuracy of the models when dealing with queries involving specific complex conditions, such as retrieving components with specific geometric shapes, still has room for improvement. Additionally, due to specific application scenarios in the BIM field, such as disaster planning, actual cases are relatively rare, resulting in a lack of relevant training data, affecting the model's performance in these scenarios. Furthermore, optimizing model parameters requires dynamic adjustments based on different corpora, posing challenges for the continuous optimization and maintenance of the model.  
Future research will focus on exploring multiple directions. On the one hand, we will utilize data augmentation techniques and transfer learning strategies to enhance the performance of models in data-scarce environments. On the other hand, developing an automated parameter search mechanism to achieve adaptive optimization of the model configuration enhances its cross-scenario adaptability and practicality. More importantly, we will strive to integrate the advantages of different models, such as ERNIE's generalization ability and BIMcoder's precise matching ability, to create a more intelligent and flexible query system. Meanwhile, with the continuous development of BIM technology and the increasingly diverse application scenarios, continuous data accumulation and model iteration will be vital to improving the system's long-term performance.  
In summary, this study provides an innovative solution for natural language query processing in the BIM field and constructs a specialized dataset to promote research progress in this area. It also clarifies the direction and focus of future research, laying a solid foundation for promoting the continuous innovation and application of BIM technology.  
Conceptualization, B.L. and H.C.; methodology, B.L. and H.C.; software, B.L. and H.C.; validation, B.L. and H.C.; writing--original draft preparation, B.L.; writing--review and editing, H.C.; funding acquisition and resources, H.C.; supervision, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (72301250).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.