Building codes are regulations that establish standards for the design, construction, and safety of buildings to ensure structural integrity, fire protection, and accessibility. They are often extensive, complex, and subject to frequent updates, making manual querying challenging and time-consuming. Key difficulties include navigating large volumes of text, interpreting technical language, and identifying relevant clauses across different sections. A potential solution is to build a Question-Answering (QA) system that answers user queries based on building codes. Among the various methods for building a QA system, Retrieval-Augmented Generation (RAG) stands out in performance. RAG consists of two components: a retriever and a language model. This study focuses on identifying a suitable retriever method for building codes and optimizing the generational capability of the language model using fine-tuning techniques. We conducted a detailed evaluation of various retrieval methods by performing the retrieval on the National Building Code of Canada (NBCC) and explored the impact of domain-specific fine-tuning on several language models using the dataset derived from NBCC. Our analysis included a comparative assessment of different retrievers and the performance of both pre-trained and fine-tuned models to determine the efficacy and domain-specific adaptation of language models using fine-tuning on the NBCC dataset. Experimental results showed that Elasticsearch proved to be the most robust retriever among all. The findings also indicate that fine-tuning language models on an NBCC-specific dataset can enhance their ability to generate contextually relevant responses. When combined with context retrieved by a powerful retriever like Elasticsearch, this improvement in LLM performance can optimize the RAG system, enabling it to better navigate the complexities of the NBCC.

本研究解决了建筑规范问答系统中的手动查询挑战，如复杂文本导航和技术语言理解。通过对不同检索方法的评估，研究发现使用Elasticsearch作为检索器，与在建筑规范特定数据集上微调的语言模型相结合，可以显著提升生成的相关性和有效性，从而优化问答系统的性能。

对大型语言模型进行微调并评估检索方法，以改进建筑规范的问答能力