Few-shot named entity recognition (NER) systems recognize entities using a
few labeled training examples. The general pipeline consists of a span detector
to identify entity spans in text and an entity-type classifier to assign types
to entities. Current span detectors rely on extensive manual labeling to guide
training. Almost every span detector requires initial training on basic span
features followed by adaptation to task-specific features. This process leads
to repetitive training of the basic span features among span detectors.
Additionally, metric-based entity-type classifiers, such as prototypical
networks, typically employ a specific metric that gauges the distance between
the query sample and entity-type referents, ultimately assigning the most
probable entity type to the query sample. However, these classifiers encounter
the sample dependency problem, primarily stemming from the limited samples
available for each entity-type referent. To address these challenges, we
proposed an improved few-shot NER pipeline. First, we introduce a steppingstone
span detector that is pre-trained on open-domain Wikipedia data. It can be used
to initialize the pipeline span detector to reduce the repetitive training of
basic features. Second, we leverage a large language model (LLM) to set
reliable entity-type referents, eliminating reliance on few-shot samples of
each type. Our model exhibits superior performance with fewer training steps
and human-labeled data compared with baselines, as demonstrated through
extensive experiments on various datasets. Particularly in fine-grained
few-shot NER settings, our model outperforms strong baselines, including
ChatGPT. We will publicly release the code, datasets, LLM outputs, and model
checkpoints.

我们的研究提出了一个改进的少样本命名实体识别流程，利用预训练的跨领域跳板标识检测器和大规模语言模型，有效减少了基本特征的重复训练，同时消除了对少样本的依赖，通过广泛的实验表明，在各种数据集上，我们的模型在细粒度少样本命名实体识别方面胜过基线模型，包括 ChatGPT。