## 3 Model Design and Implementation
### Pre-Processing
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**NL2 SQL Model** & **Simple F1** & **Moderate F1** & **Challenging F1** & **Total F1** \\ \hline NL2 PY2 SQL & 70.79\% & 53.52\% & 52.33\% & 63.82\% \\ \hline SuperSQL & 68.51\% & 47.35\% & 47.20\% & 60.10\% \\ \hline SFT\_Code5\_15B\_EK & 67.26\% & 48.06\% & 44.19\% & 59.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of F1 prediction results on BIRD dataset, compared with 11 NL2 SQL models on Challenging, Moderate, and Simple datasets. Overall, the FI-NL2 PY2 SQL F1 comparison results are significantly better than those of other models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**NL2 SQL Model** & **Simple EX** & **Moderate EX** & **Challenging EX** & **Total EX** \\ \hline NL2 PY2 SQL & 67.78\% & 48.49\% & 46.90\% & 59.97\% \\ \hline SuperSQL & 65.95\% & 44.18\% & 43.45\% & 57.24\% \\ \hline SFT\_Code5\_15B\_EK & 64.97\% & 44.83\% & 40.00\% & 56.52\% \\ \hline SFT\_Code5\_7B\_EK & 63.68\% & 42.46\% & 38.62\% & 54.89\% \\ \hline DAILSQL\_SC & 62.49\% & 42.67\% & 42.76\% & 54.63\% \\ \hline SFT\_Code5\_3B\_EK & 61.84\% & 42.24\% & 35.86\% & 53.46\% \\ \hline DAILSQL & 62.05\% & 39.87\% & 35.86\% & 52.87\% \\ \hline SFT\_Code5\_1B\_EK & 58.05\% & 34.48\% & 34.48\% & 48.70\% \\ \hline C3\_SQL & 58.05\% & 34.91\% & 29.66\% & 48.37\% \\ \hline RESDSQL\_3B\_EK & 52.43\% & 30.60\% & 15.86\% & 42.37\% \\ \hline RESDSQL\_Large\_EK & 45.95\% & 23.92\% & 20.00\% & 36.83\% \\ \hline RESDSQL\_Base\_EK & 40.54\% & 17.67\% & 14.48\% & 31.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of EX prediction results on BIRD dataset, compared with 11 NL2 SQL models on Challenging, Moderate, and Simple datasets. Overall, the FI-NL2 PY2 SQL EX comparison results are significantly better than those of other models. #### 4.2.3 VES Prediction Results

From the VES experimental results, in the Simple examples, the model improved by at least 5.87% and by as much as 33.49% compared to subsequent models. As shown in Table 5, in the Moderate examples, it achieved a minimum improvement of 7.6% and a maximum improvement of 35.93% over other models. In the Challenging examples, the model improved by at least 5.37% and up to 38.07% compared to other models. Overall, the model showed an improvement of at least 6.34% and a maximum of 34.66% compared to others. #### 4.2.4 Statistical Significance Test

Statistical significance testing provides a scientific basis for decision-making, optimizes resource allocation, and improves the credibility of results by verifying assumptions, assessing causality, and reducing subjective bias. It is widely used in multi-field data analysis and verification. To understand the effect of our proposed NL2 PY2 SQL method, we conducted a significance test, and the results are shown in Table 6. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**NL2 SQL Model** & **Simple F1** & **Moderate F1** & **Challenging F1** & **Total F1** \\ \hline DAILSQL\_SC & 65.26\% & 45.23\% & 44.84\% & 57.27\% \\ \hline SFT\_Code5\_7B\_EK & 65.66\% & 44.62\% & 40.24\% & 56.89\% \\ \hline SFT\_Code5\_3B\_EK & 64.34\% & 45.16\% & 39.73\% & 56.21\% \\ \hline DAILSQL & 64.11\% & 42.62\% & 38.60\% & 55.20\% \\ \hline C3\_SQL & 60.38\% & 38.07\% & 32.95\% & 51.04\% \\ \hline SFT\_Code5\_1B\_EK & 60.29\% & 36.78\% & 37.06\% & 50.98\% \\ \hline RESDSQL\_3B\_EK & 54.70\% & 32.90\% & 17.79\% & 44.62\% \\ \hline RESDSQL\_Large\_EK & 48.62\% & 25.29\% & 21.61\% & 39.01\% \\ \hline RESDSQL\_Base\_EK & 42.44\% & 19.88\% & 15.97\% & 33.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._