## 1 Introduction  
Relation extraction (RE) serves as a crucial subtask in natural language processing (NLP) [1] and a key step in constructing Knowledge Graphs (KGs). Its objective is to extract semantic relationships between entities from unstructured text. For example, from the sentence \"The hacker gained access to sensitive information by executing a media-less attack on Android\", the relationship \"gained access to\" can be extracted, resulting in the triple <Hacker, gained access to, sensitive information>. RE can extract numerous relationship instances, which can be applied in downstream tasks, such as intelligent question-answering systems [2], machine translation [3], information retrieval [4], recommendation systems [5], and so on. The existing RE methods commonly employ deep learning techniques and can be categorized into supervised, semi-supervised, and unsupervised approaches. Extensive research has shown that supervised RE methods achieve excellent results. However, supervised methods need to utilize a substantial mass of high-quality manually labeledtraining data during the training phase, which entails significant human and time costs. Meanwhile, supervised RE is hindered by the presence of annotation errors in the manually labeled data, limiting its performance. To address the limitations of manually annotated data, [PERSON] [6] proposed the use of a distant supervision algorithm to automatically generate large-scale labeled data. However, utilizing labeled data generated by distant supervision algorithms has certain drawbacks. It introduces a substantial amount of label noise [7] and also results in a long-tail distribution of data [8], where only a few categories have extremely limited labeled data available. While some established methods [9; 10; 11] have effectively mitigated the issue of noisy data, they encounter a substantial decline in performance when confronted with a limited quantity of training data. To address the issue of data sparsity [12] caused by distant supervision for RE, researchers have advocated for the adoption of few-shot learning (FSL) [13]. This approach typically applies meta-learning [14] to tackle this task. Few-shot relation extraction (FSRE) tasks involve the generalization of a limited number of labeled examples for the extraction of new relations. These tasks often employ the N-way-K-shot setup, as illustrated in Figure 1. Recently, many studies have utilized metric learning within a meta-learning framework to address FSRE, and prototype networks [15] have become a hot research topic due to their simplicity and efficiency. While prototype networks have made significant advancements, many studies have aimed to enhance the model's performance by incorporating external information for better prototype representations. For instance, TDproto [16] leverages relation and entity description information to enhance prototype networks. HCRP [17] employs relation-prototype contrastive learning to better leverage relationship information and obtain diverse and discriminative prototype representations. SimpleFSRE [18] improves model performance by concatenating two representations of relationship information and directly incorporating them into the prototype representation. PRM [19] combines a gating mechanism to utilize relationship description information, determining the degree of preservation and an update of both the prototype and relationship information. CBPM [20] corrects the prototype network by utilizing category information from the query set and hierarchical information from relationship synonyms. Figure 1: A demonstration of 3-way 1-shot scenario. Words with underscores signify entity mentions. The model is trained on support set instances to predict the relationship between the two known entities in the query set. However, these investigations often employ a simplistic strategy, which entails averaging the sentence representations associated with each relation class in the support set to obtain the prototype representations.