Large language models (LLMs) exhibited powerful capability in various natural
language processing tasks. This work focuses on exploring LLM performance on
zero-shot information extraction, with a focus on the ChatGPT and named entity
recognition (NER) task. Inspired by the remarkable reasoning capability of LLM
on symbolic and arithmetic reasoning, we adapt the prevalent reasoning methods
to NER and propose reasoning strategies tailored for NER. First, we explore a
decomposed question-answering paradigm by breaking down the NER task into
simpler subproblems by labels. Second, we propose syntactic augmentation to
stimulate the model's intermediate thinking in two ways: syntactic prompting,
which encourages the model to analyze the syntactic structure itself, and tool
augmentation, which provides the model with the syntactic information generated
by a parsing tool. Besides, we adapt self-consistency to NER by proposing a
two-stage majority voting strategy, which first votes for the most consistent
mentions, then the most consistent types. The proposed methods achieve
remarkable improvements for zero-shot NER across seven benchmarks, including
Chinese and English datasets, and on both domain-specific and general-domain
scenarios. In addition, we present a comprehensive analysis of the error types
with suggestions for optimization directions. We also verify the effectiveness
of the proposed methods on the few-shot setting and other LLMs.

该论文主要研究大型语言模型在零 - shot 信息提取方面的性能，并提出针对命名实体识别的推理策略，包括分解式问答范式、句法增强和自一致性等方法，并在七个基准测试中取得了显著的改进，对错误类型进行了全面分析，并验证了这些方法在少样本情况和其他大型语言模型中的有效性。