  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.
