Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs 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 techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a 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 works and resources on GitHub (\href{https://github.com/quqxui/Awesome-LLM4IE-Papers}{LLM4IE repository})
@article{arxiv.2312.17617,
title = {Large Language Models for Generative Information Extraction: A Survey},
author = {Derong Xu and Wei Chen and Wenjun Peng and Chao Zhang and Tong Xu and Xiangyu Zhao and Xian Wu and Yefeng Zheng and Yang Wang and Enhong Chen},
journal= {arXiv preprint arXiv:2312.17617},
year = {2024}
}
Comments
The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {10.1007/s11704-024-40555-y}. You can cite the FCS version