alabnii/sciclaimeval-shared-task huggingface dataset:
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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{l||cccc}
\toprule
Model & PER & LOC & ORG & Weighted \\
\midrule
\multicolumn{5}{c}{Fine-Tuned Language Models} \\
\midrule
BERT & \textbf{0.9664} & 0.8674 & 0.8313 & 0.8744 \\
RoBERTa & 0.9663 & \textbf{0.8748} & \textbf{0.8379} & \textbf{0.8792} \\
\midrule
\multicolumn{5}{c}{LLMs} \\
\midrule
GPT-mini & 0.8296 & 0.7669 & \textbf{0.6824} & 0.7396 \\
LLaMA-8B & 0.8467 & 0.7505 & 0.7005 & \textbf{0.7494} \\
Gemini-8B & \textbf{0.8536} & \textbf{0.7773} & 0.6732 & 0.7434 \\
\midrule
GPT & 0.9023 & 0.8009 & \textbf{0.7312} & \textbf{0.7910} \\
LLaMA-70B & \textbf{0.9042} & 0.7958 & 0.7073 & 0.7781 \\
Gemini & 0.8802 & \textbf{0.8228} & 0.7238 & 0.7868 \\
\midrule
\multicolumn{5}{c}{Few-Shot Learning (5-shot)} \\
\multicolumn{5}{c}{In-Context Learning} \\
\midrule
GPT-mini & 0.9265 & \textbf{0.8061} & 0.6841 & 0.7743 \\
LLaMA-8B & 0.8681 & 0.7681 & 0.7132 & 0.7655 \\
Gemini-8B & \textbf{0.9308} & 0.7991 & \textbf{0.7468} & \textbf{0.8059} \\
\midrule
GPT & 0.9372 & \textbf{0.8381} & 0.7541 & 0.8203 \\
LLaMA-70B & 0.9415 & 0.7947 & 0.7948 & 0.8321 \\
Gemini & \textbf{0.9418} & 0.8106 & \textbf{0.7966} & \textbf{0.8368} \\
\midrule
\multicolumn{5}{c}{Chain-of-Thought (CoT)} \\
\midrule
GPT-mini & 0.9221 & \textbf{0.8072} & 0.7389 & 0.8015 \\
LLaMA-8B
& 0.8799 & 0.7973 & 0.7299 & 0.7839 \\
Gemini-8B & \textbf{0.9343} & 0.7900 & \textbf{0.7408} & \textbf{0.8016} \\
\midrule
GPT & 0.9361 & \textbf{0.8295} & 0.7466 & 0.8142 \\
LLaMA-70B & 0.9122 & 0.7996 & 0.7514 & 0.8036 \\
Gemini & \textbf{0.9378} & 0.8171 & \textbf{0.7958} & \textbf{0.8369} \\
\bottomrule
\end{tabular}
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\caption{Table 1: Performance of different fine-tuned language models and LLMs under different prompts on FiNER-ORD task.}
\end{table}
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