  Exploring the application of powerful large language models (LLMs) on the
named entity recognition (NER) task has drawn much attention recently. This
work pushes the performance boundary of zero-shot NER with LLMs by proposing a
training-free self-improving framework, which utilizes an unlabeled corpus to
stimulate the self-learning ability of LLMs. First, we use the LLM to make
predictions on the unlabeled corpus using self-consistency and obtain a
self-annotated dataset. Second, we explore various strategies to select
reliable annotations to form a reliable self-annotated dataset. Finally, for
each test input, we retrieve demonstrations from the reliable self-annotated
dataset and perform inference via in-context learning. Experiments on four
benchmarks show substantial performance improvements achieved by our framework.
Through comprehensive experimental analysis, we find that increasing the size
of unlabeled corpus or iterations of self-improving does not guarantee further
improvement, but the performance might be boosted via more advanced strategies
for reliable annotation selection. Code and data are publicly available at
https://github.com/Emma1066/Self-Improve-Zero-Shot-NER
