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

翻译：探索如何将强大的大语言模型（LLMs）应用于命名实体识别（NER）任务近期引起了广泛关注。本文通过提出一种无需训练的自我改进框架，利用无标注语料库激发大语言模型的自学习能力，进一步突破了零样本NER的性能边界。首先，我们采用基于自一致性的大语言模型对无标注语料库进行预测，获得自标注数据集。其次，我们研究多种策略筛选可靠标注结果，形成可信的自标注数据集。最后，针对每个测试输入，从可靠自标注数据集中检索示例，通过上下文学习进行推理。在四个基准数据集上的实验表明，我们的框架能够显著提升性能。通过全面的实验分析，我们发现增加无标注语料库规模或自改进迭代次数并不能保证性能持续提升，但采用更先进的可靠标注选择策略可能带来性能突破。代码与数据已开源至https://github.com/Emma1066/Self-Improve-Zero-Shot-NER