Advancing Few-Shot Named Entity Recognition with Large Language Model

Few-shot named entity recognition (NER) involves identifying specific entities using limited data. Metric learning-based methods, which compute token-level similarities between query and support sets to identify target entities, have demonstrated remarkable performance in this task. However, their effectiveness deteriorates when the distribution of the support set differs from that of the query set. To address this issue, we propose a novel approach that leverages the synergy between the large language model (LLM) and the metric learning-based few-shot NER approach. Specifically, we use the LLM to refine low-confidence predictions produced by the metric learning-based few-shot NER model, thus improving overall recognition accuracy. To further reduce the difficulty of entity classification, we introduce multiple label-filtering strategies to reduce the difficulty for LLMs in performing entity classification. Furthermore, we explore the impact of prompt design on enhancing NER performance. Experimental results show that the proposed method increases the micro-F1 score on Few-NERD and CrossNER by 0.86% and 4.9%, respectively, compared to previous state-of-the-art methods.