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.

翻译：文本结构化是自然语言处理的重要领域之一，包含信息抽取和结构形式化。然而，当前文本结构化研究面临人工标注的高质量数据集在跨领域和跨语言上的缺乏问题，这些数据集需专业知识进行构建。此外，大多数信息抽取方法专门针对特定类型的结构化数据（如实体、关系和事件）设计，难以推广至其他类型。本研究提出一种简单高效的方法，通过指令指导大型语言模型从文本中提取多种结构。具体而言，我们在输入文本前分别添加前缀和后缀指令，以指示期望的信息抽取任务和结构类型。基于两个大型语言模型的实验表明，该方法能够使语言模型在多种语言和知识领域的数据集上达到与其他最先进方法相当的性能，并且可通过调整指令内容泛化至其他信息抽取子任务。本方法的另一优势在于，它能帮助研究者在低资源与特定领域场景（如金融和法律领域）中以低成本构建数据集。