Information Extraction (IE) plays a crucial role in Natural Language
Processing (NLP) by extracting structured information from unstructured text,
thereby facilitating seamless integration with various real-world applications
that rely on structured data. Despite its significance, recent experiments
focusing on English IE tasks have shed light on the challenges faced by Large
Language Models (LLMs) in achieving optimal performance, particularly in
sub-tasks like Named Entity Recognition (NER). In this paper, we delve into a
comprehensive investigation of the performance of mainstream Chinese
open-source LLMs in tackling IE tasks, specifically under zero-shot conditions
where the models are not fine-tuned for specific tasks. Additionally, we
present the outcomes of several few-shot experiments to further gauge the
capability of these models. Moreover, our study includes a comparative analysis
between these open-source LLMs and ChatGPT, a widely recognized language model,
on IE performance. Through meticulous experimentation and analysis, we aim to
provide insights into the strengths, limitations, and potential enhancements of
existing Chinese open-source LLMs in the domain of Information Extraction
within the context of NLP.

本文针对零样本情况下中文开源大型语言模型在信息抽取任务中的性能进行了综合调查和分析，同时通过少样本实验评估了这些模型的能力，并与 ChatGPT 等广泛认可的语言模型在信息抽取性能上进行了比较分析，旨在提供对现有中文开源大型语言模型在自然语言处理中信息抽取领域的优势、限制和潜在增强的洞察。