# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 4 Experiments
### Analysis of the Impact of Predicted Label Scope on Collaborative Capabilities  
As shown in Table 4, the advantage of Qwen2.5-14B over LACNER gradually decreases as the number of entity types increases. In some cases, LACNER even outperforms Qwen2.5-14B. As the number of entity types grows, distinguishing fine-grained categories with subtle differences becomes more challenging. In contrast, the metric learning-based few-shot NER model, trained on diverse examples, effectively learns to differentiate these subtle nuances. Thus, implementing an appropriate label scope filtering strategy during collaboration is crucial for reducing confusion in the LLM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**INTRA**} & \multicolumn{6}{c}{**INTER**} \\ \cline{2-10}  & \multicolumn{2}{c}{**1\(\sim\)2 shot**} & \multicolumn{2}{c}{**5\(\sim\)10 shot**} & \multicolumn{2}{c}{**Avg.**} & \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** & \\ \hline Qwen2.5-7B & 21.76 & 22.54 & 26.47 & 24.75 & 23.88 & 28.51 & 27.88 & 35.18 & 33.87 & 31.36 \\ Llama3.1-8B & 18.59 & 19.96 & 23.52 & 22.62 & 21.17 & 20.98 & 19.47 & 25.77 & 25.42 & 22.91 \\ Gemma2-9B & 26.38 & 26.47 & 28.11 & 26.98 & 26.99 & 30.51 & 31.09 & 33.52 & 34.85 & 32.49 \\ Qwen2.5-14B & 36.42 & 38.0 & 42.15 & 39.63 & 39.05 & 48.15 & 49.80 & 50.40 & 50.63 & 49.75 \\ DeepSeek-V2.5 & 39.06 & 37.56 & 44.73 & 44.64 & 41.50 & 47.88 & 46.80 & 52.0 & 52.94 & 49.91 \\ LLM Rerank & 62.77 & 60.15 & 74.14 & 70.84 & 66.98 & 68.0 & 68.64 & 73.40 & 75.79 & 71.46 \\ LACNER & 61.92 & 58.11 & 74.0 & 70.58 & 66.15 & 66.98 & 67.28 & 72.87 & 75.73 & 70.72 \\ LACNER \(w/\)\(Qwen2.5-7B\) & 63.71 & 59.21 & 75.50 & 71.15 & 67.39 & **69.79** & 69.44 & 73.60 & 76.49 & **72.33** \\ LACNER \(w/\)\(Llama3.1-8B\) & 63.56 & 58.38 & 75.10 & 70.86 & 66.98 & 68.30 & 68.11 & 72.82 & 75.41 & 71.16 \\ LACNER \(w/\)\(Gemma2.9-9B\) & **64.83** & 59.37 & 75.46 & 71.19 & 67.71 & 68.51 & 68.64 & 73.26 & 75.73 & 71.54 \\ LACNER \(w/\)\(Qwen2.5-14B\) & 64.01 & 60.12 & **75.69** & 71.80 & 67.91 & 69.48 & 69.06 & **73.86** & **76.54** & 72.24 \\ [PERSON] \(w/\)\(DeepSeek–V2.5\) & **64.83** & **60.50** & 75.65 & **71.81** & **68.20** & 69.27 & **69.46** & 73.67 & 76.36 & 72.19 \\ \hline \hline \end{tabular} Best results are indicated in bold.  
\end{table}
Table 3: Comparison of performance on Few-NERD with the micro-F1 metric (%).  
Tables 5 and 6 present the impact of varying the scope of predicted labels on the Few-NERD and CrossNER. Table 5 shows that when all entity types are provided, the classification space for the LLM becomes excessively large. Compared to the \"retain top-1 label\" strategy, performance drops by up to 1.72%, suggesting that limiting the label scope improves model focus. Similarly, Table 6 reveals a comparable trend in the 5-shot setting of CrossNER, where the \"retain top-\(N\) labels\" strategy achieves the best performance. This approach prevents the LLM from being overwhelmed by excessive predicted labels while enabling it to reference additional labels when the metric learning-based few-shot NER model's predictions are uncertain or when test samples lie near cluster boundaries. However, in the 1-shot setting of CrossNER, the significant domain discrepancy between the source and target domains leads to suboptimal representations learned by LACNER. In this case, providing all predicted labels more effectively leverages the LLM's capabilities.