# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 4 Experiments
### Comparison with Other Methods  
In this section, we conduct experiments on various open-source LLMs with different parameters, comparing the performance of different methods and exploring the contributions of various LLM to LACNER. The experimental results are shown in Tables 2 and 3. Figure 5: Comparison of micro-F1 of models with different cosine similarity intervals. Figure 6: Comparison of performance on CoNLL across various thresholds using the micro-F1 metric (%). As shown in Table 2, the performance of few-shot NER methods based on the LLM improves significantly with increasing the parameter of the LLM. For example, in the 1-shot setting, when the parameters of the Qwen2.5 series increase from 7B to 14B, the F1 score on all datasets improves. Specifically, Qwen2.5-14B achieves up to 10.84% higher F1 score compared to Qwen2.5-7B. Additionally, DeepSeek-V2.5, which has the largest number of parameters, exhibits the maximum F1 improvement of 24.9% over Llama3.1-8B. It indicates that the larger parameter LLM possess richer world knowledge and stronger reasoning capabilities, leading to better generalization and few-shot learning performance in extremely low-resources. However, as the number of examples increases, the performance gains of the LLM tend to plateau, meaning the benefit from more examples becomes limited. In contrast, LACNER leverages metric learning to fully capture domain-specific knowledge, and its performance gap with the LLM widens as the number of training samples grows. Re-identifying low-confidence entity words filtered by LACNER using large models leads to significant improvements across all settings. In the 1-shot setting, compared to using LACNER alone, LACNER \({}_{w/~{}Qwen2.5-14B}\) achieves the F1 improvement ranging from 2.27% to 8.99%, with an average gain of 6.73%. These results indicate that the extensive knowledge and strong reasoning capabilities of the LLM compensate for LACNER's limitations in representation learning for certain entity types, as well as classification errors due to limited support set examples, thereby enhancing overall performance. In the 5-shot setting, as LACNER learns entity representations more effectively, fewer samples require re-identification by the LLM, leading to smaller performance gains compared to the 1-shot setting. Nevertheless, LACNER \({}_{w/~{}Qwen2.5-14B}\) achieves the average F1 improvement of 3.06%. Overall, the integration of the LLM enhances LACNER's performance; both Qwen2.5-14B and DeepSeek-V2.5 significantly boost LACNER's performance in the 1-shot and 5-shot settings. These findings confirm that the parameter scale and pretraining quality of the LLM significantly impact their generalization and knowledge transfer capabilities in downstream tasks. Furthermore, compared with LLM Rerank, LACNER \({}_{w/~{}Qwen2.5-14B}\) improves the average F1 score by 3.44% in the 1-shot setting and by 1.55% in the 5-shot setting. It indicates that restricting the scope of predicted labels can enhance the classification accuracy of the LLM. Additionally, the structured prompting approach proposed in this study outperforms