Exploring the application of powerful large language models (LLMs) on the
fundamental named entity recognition (NER) task has drawn much attention
recently. This work aims to investigate the possibilities of pushing the
boundary of zero-shot NER with LLM via a training-free self-improving strategy.
We propose a self-improving framework, which utilize an unlabeled corpus to
stimulate the self-learning ability of LLMs on NER. First, we use LLM to make
predictions on the unlabeled corpus and obtain the self-annotated data. Second,
we explore various strategies to select reliable samples from the
self-annotated dataset as demonstrations, considering the similarity, diversity
and reliability of demonstrations. Finally, we conduct inference for the test
query via in-context learning with the selected self-annotated demonstrations.
Through comprehensive experimental analysis, our study yielded the following
findings: (1) The self-improving framework further pushes the boundary of
zero-shot NER with LLMs, and achieves an obvious performance improvement; (2)
Iterative self-improving or naively increasing the size of unlabeled corpus
does not guarantee improvements; (3) There might still be space for improvement
via more advanced strategy for reliable entity selection.

通过自我改进策略研究了零样本实体识别任务中大规模语言模型的应用，并提出了利用无标注语料库刺激模型自学能力的自我改进框架，通过全面实验分析发现，该自我改进框架能够进一步推动零样本实体识别任务的发展，并取得了显著的性能提升。