Large Language Models for Generative Information Extraction: A Survey

TL;DR

This survey reviews recent advancements in using generative large language models for information extraction, highlighting techniques, trends, and future research directions in the field.

Contribution

It provides a comprehensive categorization and empirical analysis of LLM-based IE methods, and identifies promising research directions and insights.

Findings

Emerging trend of LLMs in IE tasks

Identification of effective techniques for IE with LLMs

Future research directions in LLM-based IE

Abstract

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})