RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\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          & 0.6824          & 0.7396          \\
			LLaMA-8B  & \textbf{0.8799} & \textbf{0.7973} & \textbf{0.7299} & \textbf{0.7839} \\
			Gemini-8B & 0.8536          & 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  & 0.8467          & 0.7505          & 0.7005          & 0.7494          \\
			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}
	\end{adjustbox}
	\caption{Table 1: Performance of different fine-tuned language models and LLMs under different prompts on FiNER-ORD task.}
\end{table}