alabnii/sciclaimeval-shared-task huggingface dataset:
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\begin{table}
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\toprule
\textbf{Method} & \textbf{GSM8K} & \textbf{StrategyQA} & \textbf{MBPP} & \textbf{HumanEval} \\
\midrule
Greedy & 61.79 & 72.83 & 46.20 & 33.54 \\
\midrule
\multicolumn{5}{c}{\it{Routing-based}} \\
\midrule
Dynamic Routing & 61.11 & 74.41
& 47.80 & 38.41\\
Ensemble Routing & 63.84 & 74.37 & 46.20 & 37.20\\
\midrule
\multicolumn{5}{c}{\it{Search-based}} \\
\midrule
Contrastive Search & 60.96 & 74.85 & 46.20 & 36.59 \\
DoLa & 49.96 & 71.04 & 33.00 & 12.80 \\
Contrastive Decoding & 62.24 & 74.45 & 45.20 & 35.98 \\
\midrule
SCMoE & \textbf{66.94} & \textbf{76.29} & \textbf{48.80} & \textbf{41.46} \\
\bottomrule
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\caption{Table 4: Experimental results on GSM8K, StrategyQA, MBPP and HumanEval with Mixtral 8x7B. We report the best results for each method here. The performance of each method with different hyperparameters can be found in the Appendix Table~tab:a:mixtral.}
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