## 4 Results
The authors verified the results by classifying responses into four categories according to their characteristics: 'Search for other related specification', 'Explicit specification requirement', 'How is it determined?' and 'Refer to [another specification no.]'. 'Search for other related specification' category indicates that the current specification does not contain the information requested by the questioner and that additional specifications should be consulted to obtain relevant details. 'Explicit specification requirement' category is when a specification contains clear, direct statements, instructions, or numerical values that detail a specific requirement or standard to be followed. 'How is it determined?' refers to the method or process for establishing specific requirements. This may refer to the process for determining appropriate specifications, the decision maker or responsible entity, or a reference to a specific data sheet. Lastly, 'Refer to [another specification no.]' specifies the exact document number that the reader should consult another document to find detailed information about a particular aspect of the specification that is not included in the current specification. Table 7 shows the performance of the LLaMA2-13B and Davinci-002 models by response type. The Davinci-002 model showed poor results in all response types compared to the LLaMA2-13B model. Especially, the LLaMA2-13 model showed significant performance in responding to the 'Search for other related specification' category with 80% of perfect answers, followed by 'Refer to [another specification no.]' with 68% of correct answers. Meanwhile, both Davinci-002 and LLaMA2-13B models showed poor performance in extracting 'Explicit specification requirement'. Although the responses of the LLaMA2-13B model were better than that of the Davinci-002 model overall, it is noteworthy that the LLaMA2-13B model was rated with a higher percentage of lowest scores than the Davinci-002 model in three response types except the 'Search for other related specification'. In-depth analysis of the results will be discussed in the following section. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rating**} & \multicolumn{6}{c}{**LLaMA2-13B**} & \multicolumn{6}{c}{**Davinci-002**} \\ \cline{2-10}  & **5** & **4** & **3** & **2** & **1** & **5** & **4** & **3** & **2** & **1** \\ \hline Anchor bolt & 60\% & 4\% & 4\% & 7\% & 25\% & 7\% & 13\% & 22\% & 13\% & 45\% \\ Corrosion allowance & 39\% & 11\% & 11\% & 13\% & 26\% & 18\% & 18\% & 19\% & 37\% & 8\% \\ Welding seam & 51\% & 4\% & 9\% & 9\% & 27\% & 15\% & 18\% & 4\% & 42\% & 22\% \\ Positive material identification & 75\% & 4\% & 7\% & 11\% & 4\% & 39\% & 32\% & 11\% & 18\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fine-tuned LLMs performance by specification type. Figure 4: Overall fine-tuned LLMs performance. This study also compared the performance of the LLaMA2-13B and Davinci-002 models by prompt-completion type. The authors classified the prompt-completion pairs into four categories. Type 1 indicates that only one of the three companies has appropriate specifications related to the user query, and the other two companies do not have relevant content in their specifications. The type 2 prompt-completion pairs have ambiguous content to distinguish each company's specification. Type 3 represents that each company's specification contains content that distinguishes it from the user query, but the content is vague. Finally, the type 4 prompt-completion pairs refer to the specifications of all three companies and contain numerical values and clear content. In all types of prompt-completion pairs, the LLaMA2-13B model recorded a higher average score as shown in Table 8. In detail, the result of the LLaMA2-13B model tends to be polarized into the highest and lowest scores. The LLaMA2-13B model responded well to the user query, at least twice as much as the Davinci-002 model.