Information Extraction in Low-Resource Scenarios: Survey and Perspective

Abstract

Information Extraction (IE) seeks to derive structured information from unstructured texts, often encountering obstacles in low-resource scenarios due to data scarcity and unseen classes. This paper presents a review of neural approaches to low-resource IE from traditional and LLM-based perspectives, systematically organizing them into a fine-grained taxonomy. Our empirical studies compare LLM-based methods with prior leading models, revealing that: (1) well-tuned LMs perform relatively best; (2) tuning open-resource LLMs and in-context learning with GPT family are generally effective; (3) LLMs struggle to tackle complex tasks with intricate schema. Furthermore, we compare traditional methods and discuss LLM-based approaches, spotlighting promising applications and delineating future research directions. This survey aims to foster understanding of this field, inspire new ideas, and encourage widespread applications in both academia and industry.