\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & EM & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
BioMistral (7B) & 0.471 & 1000.0 &  &  & 262.574 & 1.0 \\
Meditron (7B) & 0.331 & 1000.0 &  &  & 272.404 & 1.0 \\
Llama 2 (7B) & 0.349 & 1000.0 &  &  & 272.404 & 1.0 \\
Llama 3 (70B) & 0.776 & 1000.0 &  &  & 223.323 & 1.0 \\
Mistral Instruct v0.1 (7B) & 0.403 & 1000.0 &  &  & 262.574 & 1.0 \\
Mixtral (8x22B) & 0.679 & 1000.0 &  &  & 262.574 & 1.0 \\
Claude 3 Haiku (20240307) & 0.669 & 1000.0 &  &  & 235.901 & 1.0 \\
Claude 3 Sonnet (20240229) & 0.634 & 1000.0 &  &  & 235.901 & 1.0 \\
Claude 3 Opus (20240229) & 0.79 & 1000.0 &  &  & 235.901 & 1.0 \\
Gemini 1.5 Pro (0409 preview) & 0.011 & 1000.0 &  &  & 226.739 &  \\
PaLM-2 (Bison) & 0.6 & 1000.0 &  & 0.001 & 226.736 &  \\
PaLM-2 (Unicorn) & 0.661 & 1000.0 &  &  & 227.599 &  \\
MedLM (Medium) & 0.606 & 1000.0 &  &  & 227.599 &  \\
MedLM (Large) & 0.717 & 1000.0 &  &  & 227.599 &  \\
GPT-3.5 Turbo (0125) & 0.626 & 1000.0 &  &  & 223.437 & 1.0 \\
GPT-4 Turbo (2024-04-09) & 0.737 & 1000.0 &  &  & 223.437 & 1.0 \\
GPT-4o (2024-05-13) & 0.875 & 1000.0 &  &  & 226.209 & 1.0 \\
\bottomrule
\end{tabular}}
\caption{Results for med_qa_ (med_qa)}
\label{fig:med_qa_ (med_qa)}
\end{table*}