alabnii/sciclaimeval-shared-task huggingface dataset:
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\begin{document}
\begin{table}[]
\centering
\begin{tabular}{lcc}
\toprule
\multicolumn{1}{c}{\multirow{2}{*}{Method}} & \multicolumn{2}{c}{Acc (\%)} \\
\multicolumn{1}{c}{} & \textsc{WikiTQ} & \textsc{TabFact} \\ \midrule
\rowcolor[rgb]{0.9,0.9,0.9}
\multicolumn{3}{c}{\textit{$\heartsuit$ Pre-LLM era}} \\
TAPEX (Liu et al., 2022) & 57.2 & 85.9 \\
TaCube (Zhou et al., 2022b) & 60.8 & - \\
ReasTAP (Zhao et al., 2022) & 58.6 & \underline{86.2} \\
OmniTab (Jiang et al., 2022) & \underline{62.7} & - \\
CABINET (Patnaik et al., 2024) & \textbf{69.1} & - \\
PASTA (Gu et al., 2022) & - & \textbf{90.8} \\ \midrule
\rowcolor[rgb]{0.9,0.9,0.9}
\multicolumn{3}{c}{\textit{$\spadesuit$ LLM era}} \\
Binder (Cheng et al., 2023) & 55.1 & 85.1 \\
Dater w SC (Ye et al., 2023) & 69.0 & 85.4 \\
ReAcTable w s-vote (Zhang et al., 2023) & 68.0 & 86.1 \\
Mix SC w SC (Liu et al., 2023) & \textbf{73.7} & - \\
Chain-of-Table (Wang et al., 2024b) & 67.3 & 86.6 \\
\textbf{ALTER (ours) w SC} & \underline{70.4} & \textbf{87.2} \\
\hdashline
Dater w/o sc (Ye et al., 2023) & 65.0 & \underline{83.5} \\
ReAcTable (Zhang et al., 2023)
& \textbf{67.4} & \textbf{84.3} \\
Mix SC w/o SC (Liu et al., 2023) & 64.2 & - \\
\textbf{ALTER (ours) w/o SC} & \underline{65.8} & 83.1 \\
\bottomrule
\end{tabular}
\label{tab:main}
\caption{Table 1: Results of different methods on WikiTQ and TabFact. (We use underline to denote the second-best performance, bold to denote the best performance for each region: Pre-LLM era, LLM era with result ensemble and without ensemble)}
\end{table}
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