# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 2 Related Work  
### Few-Shot Named Entity Recognition  
Few-shot NER is the task of identifying and classifying named entities in text when only a limited amount of labeled data are available for each entity type. In recent years, metric learning frameworks such as prototypical networks and contrastive learning have seen widespread application in few-shot NER tasks, significantly improving recognition performance in the few-shot setting. For instance, [PERSON] et al. [14] first apply prototypical networks to NER by generating prototypes for each entity type and classifying test samples based on their distance to these prototypes. Since the non-entity category (\"O\") does not need to closely align with any specific prototype in the vector space, the similarity score is adjusted using hyperparameters and optimized through a modified softmax function to improve classification accuracy for non-entity tokens. The proposal by [PERSON] et al. [15] combines prototypical networks with label semantic information. L-TapNet projects both label prototypes and their semantic representations into a feature space that separates different label distributions, thereby enhancing domain adaptability and cross-domain generalization.  
Despite the effectiveness of prototypical networks in optimizing distances between prototypes of entity types and samples, they often struggle to learn accurate and discriminative prototypes in the presence of noisy labels. To address this challenge, researchers have introduced contrastive learning as an alternative to traditional prototype-based methods. Contrastive learning simultaneously increases the similarity of positive instances and decreases the similarity of negative instances, thereby encouraging semantically similar entities to cluster while pushing dissimilar entities apart [16]. For example, [PERSON] et al. [1] developed CONTaiNER, which optimizes distances in the Gaussian embedding space via contrastive learning to learn more generalized representations, ultimately achieving superior performance in few-shot NER tasks. [PERSON] et al. [17] propose LACNER, which integrates contrastive learning with label words to enhance token representations and employ dropout-induced noise as a form of data augmentation. However, entity classification performance under metric learning frameworks heavily depends on the quality and distribution of the support set. When the support set lacks certain semantic features, the model's performance significantly declines and becomes more vulnerable to noisy sample interference.  
Although metric learning has advanced few-shot NER on the model side, data scarcity remains a challenge. A straightforward solution is to synthesize new data. Current research on word-level transformations for NER primarily focuses on entity replacement or context (non-entity) substitution. For example, [PERSON] et al. [18] explored simple augmentation strategies such as label-wise word replacement, synonym substitution, and random shuffling of words within predefined text segments for few-shot NER. COSINER, a cosine-similarity-based vocabulary replacement strategy, showed significant improvements over random replacement methods [19]. Nevertheless, because NER requires precise syntactic and semantic understanding, direct substitution can disrupt sentence integrity and negatively impact training outcomes.