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:
https://github.com/quqxui/Awesome-LLM4IE-Papers.

信息提取是从自然语言文本中提取结构化知识（如实体、关系和事件）的过程。最近，生成式大型语言模型（LLMs）在文本理解和生成方面展示出了显著的能力，允许在各个领域和任务中进行概括。为了对 LLMs 在信息提取任务中的努力进行全面系统的回顾和探索，我们通过调查这个领域的最新进展来进行研究。我们首先通过以各种信息提取子任务和学习范式进行分类来呈现一个全面的概述，然后我们经验性地分析最先进的方法并发现使用 LLMs 的信息提取任务的新兴趋势。通过进行彻底审查，我们确定了一些技术见解和有希望进一步探索的研究方向，值得在未来研究中持续探索。我们在以下网址维护一个公共资源库并定期更新相关资源：https://github.com/quqxui/Awesome-LLM4IE-Papers。