# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 6 Conclusions  
In this paper, we propose a few-shot NER method based on the collaboration of large and small language models. The method uses the SLM to make preliminary predictions on test samples, filtering out entities with lower confidence levels, which are then re-identified and corrected by the large language model. This paper also designs multiple prediction label filtering strategies to restrict the range of labels predicted by the LLM, reducing the difficulty of entity classification. Additionally, we adopt the structured approach to designing prompts, further optimizing experimental results through prompt engineering.  
Experiments conducted on the Few-NER and CrossNER demonstrate that the proposed method enhances the performance of few-shot NER. In both 1-shot and 5-shot settings, the collaborative approach outperforms baseline models, and its versatility has been validated across various open-source LLMs. Ablation studies further confirm the effectiveness of key components such as the candidate word selecting module, prediction label filtering module, structured prompts, and prompt engineering.  
Conceptualization, Y.X. and Q.Y.; methodology, Y.X.; validation, Y.X., J.Z., and Q.Y.; formal analysis, Y.X.; investigation, Y.X.; resources, Q.Y.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Q.Y.; supervision, Q.Y.; project administra  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Prompt Engineering**} & \multicolumn{6}{c}{**1-Shot**} & \multicolumn{6}{c}{**5-Shot**} \\ \cline{2-11}  & **CONLL GUM** & **WNUT** & **Onto** & **Avg.** & **CONLL GUM** & **WNUT** & **Onto** & **Avg.** \\ \hline Ours & 63.90 & 23.92 & 49.61 & 55.49 & 48.23 & 72.78 & 40.48 & 55.03 & 67.12 & 58.85 \\ + Metacognitive Prompting & 64.28 & **24.32** & **50.91** & **56.46** & **48.99** & **72.91** & **40.59** & **55.35** & **67.45** & **59.08** \\ + Chain of Thought Prompting & 64.20 & 23.99 & 50.19 & 55.41 & 48.45 & 72.70 & 40.48 & 54.92 & 67.22 & 58.83 \\ + Self-Verification Prompting & **64.40** & 23.92 & 49.51 & 56.33 & 48.54 & 72.67 & 40.42 & 55.02 & 66.83 & 58.74 \\ \hline \hline \end{tabular} Best results are indicated in bold.  
\end{table}
Table 8: Impact of different prompt engineering methods on the CrossNER with the micro-F1 metric (%).  
tion, Q.Y.; funding acquisition, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset we used is the newest Few-NERD Arxiv V6 Version. It can be downloaded at [[https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)]([https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)) (accessed on 10 February 2025). CrossNER can be downloaded at [[https://atmahou.github.io/attachments/ACL2020](https://atmahou.github.io/attachments/ACL2020) data.zip]([https://atmahou.github.io/attachments/ACL2020](https://atmahou.github.io/attachments/ACL2020) data.zip) (accessed on 10 February 2025).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NER & Named Entity Recognition \\ LLM & Large Language Model \\ SLM & Small Language Model \\ NLU & Natural Language Understanding \\ COT & Chain-of-Thought \\ \end{tabular}