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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{lcccccccc}
\toprule
\multirow{3}{*}{\textbf{Models}} &
\multicolumn{4}{c}{\rule{0pt}{2ex}\textbf{EN Questions, EN databases}} &
\multicolumn{4}{c}{\rule{0pt}{2ex}\textbf{ZH Questions, EN databases}} \\
[-.5ex]
\cmidrule(lr){2-5} \cmidrule(lr){6-9}
&
\multicolumn{2}{c}{\textbf{Full}} &
\multicolumn{2}{c}{\textbf{Test}} &
\multicolumn{2}{c}{\textbf{Full}} &
\multicolumn{2}{c}{\textbf{Test}} \\
[-.5ex]
\cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9}
&
\textbf{VA} &
\textbf{EX} &
\textbf{VA} &
\textbf{EX} &
\textbf{VA} &
\textbf{EX} &
\textbf{VA} &
\textbf{EX} \\ \hline
\rowcolor{yellow} \multicolumn{9}{c}{\textit{\textbf{LLMs}}} \\
GPT-3.5 + API Doc &
82.63 &
13.24 &
83.65 &
3.85 &
86.18 &
10.65 &
85.58 &
\textbf{3.85} \\
GPT-3.5 + CT-3 &
\textbf{84.17} &
\textbf{13.34} &
80.77 &
3.85 &
\textbf{91.17} &
\textbf{12.86} &
\textbf{91.35} &
1.93 \\
GPT-3.5 + CT-3 + COT &
75.14 &
13.24 &
74.04 &
4.85 &
72.84 &
12.19&
65.38 &
\textbf{3.85}\\
GPT-4 + DIN-SQL &
- &
- &
\textbf{96.15}&
\textbf{6.73}&
- &
- &
- &
- \\ \hline
\rowcolor{yellow}\multicolumn{9}{c}{\textit{\textbf{Fine-tuned Models}}} \\
T5-base/mT5-base &
- &
- &
11.54 &
0.00 &
- &
- &
\multicolumn{1}{r}{9.62} &
0.00 \\
T5-large/mT5-large &
- &
- &
15.38 &
0.00 &
- &
- &
\multicolumn{1}{r}{14.42} &
0.00 \\
T5-3B/mT5-xl &
- &
- &
19.23 &
0.00 &
- &
- &
\multicolumn{1}{r}{17.31} &
0.00 \\
T5-base/mT5-base + Aug &
- &
- &
25.00&
0.00&
- &
- &
24.03 &
0.00\\
T5-large/mT5-large + Aug &
- &
- &
33.65 &
3.84&
- &
- &
30.77 &
0.96\\
T5-3B/mT5-xl + Aug &
- &
- &
\textbf{50.00} &
\textbf{4.81} &
- &
- &
\textbf{61.54} &
\textbf{1.92} \\\bottomrule
\end{tabular}%
\end{adjustbox}
\caption{Table 2: Baseline performance on Archer. GPT-4+DIN-SQL was tested only on the English set due to cost and its English-specific design. We only report the fine-tuned model's performance on the test set.}
\end{table}
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