Unified Text Structuralization with Instruction-tuned Language Models

• URL: http://arxiv.org/abs/2303.14956v2
• Date: Thu, 30 Mar 2023 13:41:54 GMT
• Title: Unified Text Structuralization with Instruction-tuned Language Models
• Authors: Xuanfan Ni, Piji Li and Huayang Li
• Abstract summary: We propose a simple and efficient approach to instruct large language model (LLM) to extract a variety of structures from texts. Experiments 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.
• Score: 28.869098023025753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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