Text structuralization is one of the important fields of natural language
processing (NLP) consists of information extraction (IE) and structure
formalization. However, current studies of text structuralization suffer from a
shortage of manually annotated high-quality datasets from different domains and
languages, which require specialized professional knowledge. In addition, most
IE methods are designed for a specific type of structured data, e.g., entities,
relations, and events, making them hard to generalize to others. In this work,
we propose a simple and efficient approach to instruct large language model
(LLM) to extract a variety of structures from texts. More concretely, we add a
prefix and a suffix instruction to indicate the desired IE task and structure
type, respectively, before feeding the text into a LLM. Experiments on two LLMs
show that this approach can enable language models to perform comparable with
other state-of-the-art methods on datasets of a variety of languages and
knowledge, and can generalize to other IE sub-tasks via changing the content of
instruction. Another benefit of our approach is that it can help researchers to
build datasets in low-source and domain-specific scenarios, e.g., fields in
finance and law, with low cost.

本文提出了一种简单有效的方法，即在输入到大型语言模型之前添加前缀和后缀指令，以指示所需的信息提取任务和结构类型，以从文本中提取各种结构。通过在两个大型语言模型上进行实验，结果表明该方法能够在各种语言和知识的数据集上执行效果与其他现有的最先进方法相当，并且可以通过更改指令的内容来应用于其他信息提取子任务。此外，该方法还能够帮助研究人员在低资源和特定领域的情况下构建数据集，例如金融和法律领域，成本较低。