# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 4 Experiments
### Comparison with Other Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**1-Shot**} & \multicolumn{6}{c}{**5-Shot**} \\ \cline{2-11}  & **CONLL** & **GUM** & **WNUT** & **Onto** & **Avg.** & **CONLL** & **GUM** & **WNUT** & **Onto** & **Avg.** \\ \hline Qwen2.5-7B & 51.59 & 18.82 & 33.84 & 37.63 & 35.47 & 51.62 & 18.35 & 35.31 & 35.31 & 35.15 \\ Llama3.1-8B & 45.90 & 18.62 & 29.51 & 26.97 & 30.25 & 42.76 & 16.52 & 25.07 & 25.07 & 27.36 \\ Gemma2-9B & 54.22 & 20.54 & 39.17 & 39.69 & 38.41 & 53.80 & 20.28 & 34.31 & 34.31 & 35.68 \\ Qwen2.5-14B & 62.43 & 20.29 & 45.01 & 44.28 & 43.0 & 61.87 & 21.09 & 37.85 & 41.46 & 40.57 \\ DeepSeek-V2.5 & 62.71 & 24.96 & 48.65 & 51.87 & 47.05 & 63.17 & 27.80 & 44.33 & 49.95 & 46.31 \\ LLM Rerank & 58.62 & 22.65 & 46.73 & 51.16 & 44.79 & 70.85 & 40.12 & 53.55 & 64.69 & 57.30 \\ LACNER & 57.08 & 21.65 & 40.62 & 46.63 & 41.50 & 70.50 & 40.12 & 48.92 & 63.61 & 55.79 \\ [PERSON] \({}_{w/~{}Qwen2.5-7B}\) & 59.80 & 23.71 & 46.90 & 51.19 & 45.40 & 71.51 & 39.80 & 53.56 & 65.48 & 57.59 \\ [PERSON] \({}_{w/~{}Llama3.1-8B}\) & 58.61 & 23.43 & 44.48 & 50.74 & 44.32 & 71.31 & 39.58 & 51.13 & 65.08 & 56.78 \\ [PERSON] \({}_{w/~{}Qwen2.5-9B}\) & 60.36 & 23.69 & 48.19 & 53.12 & 46.34 & 72.48 & 39.82 & 53.09 & 65.33 & 57.68 \\ [PERSON] \({}_{w/~{}Qwen2.5-14B}\) & 63.90 & 23.92 & 49.61 & 55.49 & 48.23 & **72.78** & 40.48 & 55.03 & 67.12 & 58.85 \\ LACNER \({}_{w/~{}DeepSeek-V2.5}\) & **64.18** & **25.25** & **50.36** & **57.42** & **49.30** & 72.75 & **40.49** & **55.83** & **67.56** & **59.16** \\ \hline \hline \end{tabular} Best results are indicated in bold.  
\end{table} 
Table 2: Comparison of performance on Cross-NER with the micro-F1 metric (%). the multiple choice prompting used in LLM Rerank by improving the LLM's understanding of task requirements and providing relevant background knowledge. Compared with CrossNER, the Few-NERD contains a greater variety of entity labels with finer granularity, a larger labeling space, and higher differentiation complexity. On CrossNER, the LLM performed well in the 1-shot setting, benefiting from their extensive prior knowledge. However, when applied independently to Few-NERD, the LLM demonstrated lower performance, particularly in the INTRA, where the disparity is more pronounced. It suggests that as task complexity and label granularity increase, LLMs face greater challenges. Although integrating an LLM with LACNER continues to enhance performance, the improvements become increasingly nuanced. Specifically, in the INTRA setting, LACNER \(w/\)\(Qmen2.5-14B\) improves the average F1 score by 1.76% compared to LACNER, while in the INTER setting, it achieves a 1.52% improvement. The disparity in these collaborative improvements narrows, suggesting that as task complexity increases, LLMs face greater challenges. Finally, compared with LLM Rerank, LACNER \(w/\)\(Qmen2.5-14B\) achieves the average F1 improvement of 0.95% in the INTRA setting and 0.78% in the INTER setting. Moreover, unlike LLM Rerank, it removes the need for manually creating the COT examples.