# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 4 Experiments
### Analysis of the Effectiveness of Various Methods in Prompt Design  
In this section, we first examine the effectiveness of incorporating type definitions and few-shot examples in our prompt. We then investigate whether prompt engineering can further enhance overall performance. 1. Analysis of the Effectiveness of Type Definitions and Few-Shot Examples in In-Context Learning \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scope of the Labels to Be Predicted**} & \multicolumn{6}{c}{**1-Shot**} & \multicolumn{6}{c}{**5-Shot**} \\ \cline{2-10}  & **CONLL** & **GUM** & **WNUT** & **Onto** & **Avg.** & **CONLL** & **GUM** & **WNUT** & **Onto** & **Avg.** \\ \hline All & 63.76 & **23.92** & 48.81 & **55.49** & **48.0** & **72.78** & 40.09 & 54.41 & 66.34 & 58.41 \\ Top 1 Labels & 63.53 & 23.13 & 49.5 & 52.94 & 47.28 & 72.61 & **40.48** & **55.03** & **67.12** & **58.81** \\ Top 2 Labels & 63.78 & 23.27 & **49.61** & 53.29 & 47.49 & 72.50 & 40.41 & 54.88 & 67.04 & 58.71 \\ Top 3 Labels & 63.65 & 23.20 & 49.51 & 54.41 & 47.69 & 72.24 & 40.30 & 54.92 & 67.08 & 58.64 \\ Top 3 Labels & **63.90** & 23.16 & 49.49 & 53.42 & 47.49 & 72.62 & 40.48 & 54.85 & 67.02 & 58.74 \\ Top 3 Unique Labels & 63.48 & 23.02 & 49.06 & 55.24 & 47.70 & 72.30 & 40.16 & 54.88 & 66.87 & 58.55 \\ \hline \hline \end{tabular} Best results are indicated in bold.  
\end{table} 
Table 6: Effects of predicted label scope on the CrossNER with the micro-F1 metric (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CONLL** & **GUM** & **WNUT** & **OntoNotes** & **Few-NERD** \\ \hline Number of entity types & 4 & 12 & 7 & 18 & 17 \\ Micro-F1(Qwen2.5-14B) & 62.43 & 20.29 & 45.01 & 44.28 & 25.17 \\ Micro-F1(LACNER) & 57.08 & 21.65 & 40.62 & 46.63 & 63.57 \\ \(\Delta\) Micro-F1 (Qwen2.5-14B, LACNER) & 5.35 & \(-\)1.36 & 4.39 & \(-\)2.35 & \(-\)38.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of Qwen2.5-14B and LACNER across datasets with varying numbers of entity types in the 1-shot setting. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**1 NTRA**} & \multicolumn{6}{c}{**1 NTER**} \\ \cline{2-10}  & \multicolumn{2}{c}{**1\(\sim\)2 shot**} & \multicolumn{2}{c}{**5\(\sim\)10 shot**} & \multicolumn{2}{c}{**1\(\sim\)2 shot**} & \multicolumn{2}{c}{**5\(\sim\)10 shot**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-10}  & **5 way** & **10 way** & **5 way** & **10 way** & & **5 way** & **10 way** & **5 way** & **10 way** & **Avg.** \\ \hline All & 62.29 & 59.13 & 74.58 & 71.10 & 66.78 & 67.73 & 67.75 & 73.31 & 75.70 & 71.12 \\ Top 1 Labels & **64.01** & **60.12** & **75.69** & 71.76 & **67.90** & **69.48** & **69.06** & 73.83 & **76.54** & **72.23** \\ Top 2 Labels & 64.0 & 60.10 & 75.45 & 71.77 & 67.83 & 69.31 & 68.94 & **73.86** & 76.44 & 72.14 \\ Top 2 Unique Labels & 63.94 & 59.95 & 75.38 & 71.76 & 67.76 & 69.39 & 68.59 & 73.79 & 76.38 & 72.04 \\ Top 3 Labels & 63.99 & 60.05 & 75.45 & **71.80** & 67.82 & 69.43 & 68.61 & 73.84 & 76.43 & 72.08 \\ Top 3 Unique Labels & 63.67 & 59.95 & 75.28 & 71.69 & 67.65 & 69.50 & 68.71 & 73.82 & 76.39 & 72.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effects of predicted label scope on the Few-NERD with the micro-F1 metric (%). Table 7 presents the performance of two strategies--type definitions and few-shot examples--using ablation on the CrossNER. The results show that employing both type definitions and few-shot examples yields the best performance under both 1-shot and 5-shot settings. Specifically, in the 1-shot setting, the combined approach outperforms the baseline (which does not apply these strategies) by 1.8%, while in the 5-shot setting, it achieves the 0.52% improvement. These findings suggest that incorporating both type definitions and demonstrations enhances the model's comprehension of the task and entity type definitions, thereby improving classification accuracy.