## 3 Research Methodology
### Fine-Tuning LLMs  
This study employed representative LLMs from each of the closed-source and open-source LLMs considering the accessibility and trialability of each LLM for the purpose of comparison, namely, Davinci-002 model, which is one of GPT series, introduced by OpenAI and LLaMA2-13B model released by Meta. The Davinci-002 model was fine-tuned through the API service provided by OpenAI and the LLaMA2 model by using the Quantized Low-Rank Adaptation (QLoRA) method [42]. One LLM this study fine-tuned is the Davince-002 model. Because GPT series are closed-source LLMs, the authors used the API service provided by OpenAI for fine-tuning. The GPT-3 tuning procedure requires specificity. Data sets should conform to a prompt-completion architecture, preferably in JSONL format. This structure makes capturing the correlation between the prompt and its corresponding completion easier for the model. A prompt is a query that a user provides to elicit a response from the LLM. The LLM utilizes the prompt to produce a response based on pretrained data. The authors prepared prompt-completion pair datasets in JSONL format in accordance with the instructions guided by OpenAI [43]. Each prompt-completion pair should be in a dictionary format as shown in Figure 3. Here, '\
n####n\
' in a prompt and '#END' in a completion indicate the end of a prompt and completion, respectively. After preparing and uploading the dataset, the fine-tuning process is performed automatically through the API. Another LLM this study fine-tuned is the LLaMA-13B model. Traditional fine-tuning of pre-trained language models commonly update all parameters based on additional training datasets. However, researchers have been investigating how to fine-tune a pre-trained language model efficiently as the size of language models increases. PEFT aims to solve specific problems in a short period with low resources and computation by further training \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specification Type** & **AAA** & **BBB** & **CCC** & **Total** \\ \hline Anchor bolt & 3 & 1 & 5 & 9 \\ Corrosion allowance & 4 & 2 & 3 & 9 \\ Welding seam & 1 & 7 & 3 & 11 \\ Positive material identification & 2 & 1 & 1 & 4 \\ Total & 10 & 11 & 12 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of extracted Q&A pairs. a small number of model parameters. Various PEFT methods have been introduced recently such as Adapters [44], Low-Rank Adaptation of Large Language Models (LoRA) [45], and (IA)3[46]. QLoRA is one of the representative PEFT methods recently employed, and this study fine-tuned the LLAMA-13B model using the QLoRA method. The QLoRA method combines two core PEFT techniques: quantization and LoRA. Quantization is a method that converts a data type of the original transformer model to the 4-bit NormalFloat data type to reduce memory requirements for fine-tuning [42]. Although there is a trade-off between precision loss and memory efficiency, leveraging the quantization is worthwhile because it enables fine-tuning a larger size of LLM with lower resources. LoRA is one of the PEFT techniques used in fine-tuning LLMs. It offers an efficient and effective approach to modifying pre-trained language models for specific tasks. In order to reduce computing costs, the LoRA method trains an additional small subset of the parameters while freezing all the parameters of an original LLM [45]. LoRA decomposes the weight matrices of the original transformer model into lower-rank matrices, which are trainable. Thus, it makes fine-tuning process more parameter-efficient compared to full model fine-tuning. This study fine-tuned the LLaMA2-13B model in a local development environment while the Davinci-002 was fine-tuned through API service provided by OpenAI. The local development environment was Ubuntu 20.04 and Python version 3.11.5 with an AMD Ryzen 9-5950X CPU and a GeForce RTX-3090 Ti GPU. In addition, this study used the same hyperparameters used in QLoRA fine-tuning experiments performed by [PERSON] et al.