# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 1 Introduction  
Named entity recognition (NER), as a core task in information extraction, aims to identify words or phrases in text that belong to specific semantic categories. Few-shot NER focuses on leveraging limited data to recognize target categories. In recent years, studies on few-shot NER have predominantly utilized metric learning, which calculates token-level similarities between query and support samples to identify target entities. The core principle of metric learning is to learn distance or similarity functions that ensure favorable geometric properties in the particular representation space. This approach typically comprises two stages: embedding functions and metric-based classification. The embedding function maps high-dimensional vectors into a low-dimensional space, positioning entities of the same type closely together while distinguishing entities of different types. Metric-based classification then assigns labels to entities based on a chosen distance or similarity metric. [PERSON] et al. [1] propose CONTaiNER, which optimizes Gaussian distribution embeddings through contrastive learning, allowing the model to acquire more generalized feature representations and demonstrate superior performance in few-shot NER tasks. Similarly, [PERSON] et al. [2] introduce COFNER, which implements a prompt-based contrastive learning mechanism with class-anchor words, facilitating explicit alignment of entities with their corresponding class labels. However, the metric learning paradigm exhibits a significant limitation: its reliance on token-level similarity betweenquery and support sets can lead to substantially degraded recognition performance when their distributions diverge. Large language models (LLMs) have recently made significant advancements in tasks such as semantic understanding, question answering, and logical reasoning [3; 4; 5; 6]. Several studies have evaluated the performance of LLMs on NER tasks, showing that they still lag behind existing specialized methods [7; 8]. Furthermore, enhancing the performance of LLMs in NER tasks typically requires dividing them into multiple subtasks, thereby increasing resource consumption and computational costs [9]. An alternative approach entails constructing question-answer pairs to fine-tune LLM [10]. However, this approach escalates computational resource demands and conflicts with the constraints of few-shot learning. To address these issues, we propose a collaborative approach that combines small and large language models to enhance entity recognition accuracy in few-shot settings. Specifically, we initially deploy the small language model (SLM) fine-tuned with metric learning for few-shot NER. This few-shot NER model then performs preliminary entity identification and classification of the query. When token-level similarity between the query and support sets is low, the query is often distant from feature clusters, resulting in reduced entity prediction accuracy. To address this issue, we utilize an LLM for secondary recognition of queries exhibiting low token-level similarity to the support set. LLMs typically exhibit higher accuracy in these scenarios by drawing on their extensive prior knowledge, contextual understanding, and capacity to incorporate provided entity-type definitions. Thus, our method effectively mitigates classification uncertainty and performance degradation stemming from low cosine similarity and limited semantic coverage in support sets, enhancing the performance of few-shot NER. To further enhance the performance of LLMs in few-shot NER tasks, we introduce multiple label-filtering strategies. By restricting the label scope predicted by an LLM, we reduce the complexity of entity classification. We also design structured prompts to improve entity classification accuracy. Additionally, we investigate three zero-shot prompting strategies--chain-of-thought prompting [11], metacognitive prompting [12], and self-verification prompting [13]--and evaluate their impact on the performance of few-shot NER.