Large Language Models for Generative Information Extraction: A Survey
Derong Xu Wei Chen Wenjun Peng Chao Zhang Tong Xu Xiangyu Zhao Xian Wu Yefeng Zheng Enhong Chen
Information extraction (IE) aims to extract structural knowledge (such as
entities, relations, and events) from plain natural language texts. Recently,
generative Large Language Models (LLMs) have demonstrated remarkable
capabilities in text understanding and generation, allowing for generalization
across various domains and tasks. As a result, numerous works have been
proposed to harness abilities of LLMs and offer viable solutions for IE tasks
based on a generative paradigm. To conduct a comprehensive systematic review
and exploration of LLM efforts for IE tasks, in this study, we survey the most
recent advancements in this field. We first present an extensive overview by
categorizing these works in terms of various IE subtasks and learning
paradigms, then we empirically analyze the most advanced methods and discover
the emerging trend of IE tasks with LLMs. Based on thorough review conducted,
we identify several insights in technique and promising research directions
that deserve further exploration in future studies. We maintain a public
repository and consistently update related resources at:
\url{https://github.com/quqxui/Awesome-LLM4IE-Papers}.