\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & EM & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
BioMistral (7B) & 0.449 & 1000.0 &  &  & 78.995 & 1.0 \\
Meditron (7B) & 0.268 & 1000.0 &  &  & 79.638 & 1.0 \\
Llama 2 (7B) & 0.357 & 1000.0 &  &  & 79.638 & 1.0 \\
Llama 3 (70B) & 0.706 & 1000.0 &  &  & 68.403 & 1.0 \\
Mistral Instruct v0.1 (7B) & 0.313 & 1000.0 &  &  & 78.995 & 1.0 \\
Mixtral (8x22B) & 0.61 & 1000.0 &  &  & 78.995 & 1.0 \\
Claude 3 Haiku (20240307) & 0.598 & 1000.0 &  &  & 80.214 & 1.0 \\
Claude 3 Sonnet (20240229) & 0.561 & 1000.0 &  &  & 80.214 & 1.0 \\
Claude 3 Opus (20240229) & 0.664 & 1000.0 &  &  & 80.214 & 1.0 \\
Gemini 1.5 Pro (0409 preview) & 0.074 & 1000.0 &  &  & 67.476 &  \\
PaLM-2 (Bison) & 0.372 & 1000.0 &  &  & 67.476 &  \\
PaLM-2 (Unicorn) & 0.655 & 1000.0 &  &  & 68.659 &  \\
MedLM (Medium) & 0.491 & 1000.0 &  &  & 68.659 &  \\
MedLM (Large) & 0.691 & 1000.0 &  &  & 68.659 &  \\
GPT-3.5 Turbo (0125) & 0.548 & 1000.0 &  &  & 68.472 & 1.0 \\
GPT-4 Turbo (2024-04-09) & 0.691 & 1000.0 &  &  & 68.472 & 1.0 \\
GPT-4o (2024-05-13) & 0.753 & 1000.0 &  &  & 74.29 & 1.0 \\
\bottomrule
\end{tabular}}
\caption{Results for med_mcqa_ (med_mcqa)}
\label{fig:med_mcqa_ (med_mcqa)}
\end{table*}