# Large Language Models for Generative Information Extraction: A Survey

**Authors:** Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Enhong Chen  
**arXiv:** [2312.17617](https://arxiv.org/abs/2312.17617)  
**Date:** 2023-12-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.17617v1/

## Key points

- The goal of information extraction (IE) is to extract structured knowledge from natural language texts.
- Generative Large Language Models (LLMs) have shown remarkable capabilities in text understanding and generation across different domains and tasks.
- Researchers have proposed various methods that utilize LLMs for IE tasks based on a generative paradigm.
- This study provides a comprehensive review of recent advancements in using LLMs for IE tasks.
- Works are categorized based on different IE subtasks and learning paradigms to present an extensive overview.
- Advanced methods are empirically analyzed, and emerging trends in IE tasks with LLMs are identified.
- Insights in technique and promising research directions are highlighted for further exploration.
- The paper contributes to our understanding of utilizing LLMs for information extraction tasks and provides valuable insights for future research.

## Summary

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In the field of information extraction (IE), the goal is to extract structured knowledge, such as entities, relations, and events, from natural language texts. Recently, there has been a significant advancement in the use of generative Large Language Models (LLMs) for text understanding and generation. These LLMs have demonstrated remarkable capabilities in generalizing across different domains and tasks. As a result, researchers have proposed various methods that harness the abilities of LLMs to offer viable solutions for IE tasks based on a generative paradigm. To provide a comprehensive systematic review and exploration of LLM efforts for IE tasks, this study surveys the most recent advancements in this field. The authors categorize these works based on different IE subtasks and learning paradigms to present an extensive overview. They then empirically analyze the most advanced methods and identify the emerging trends in IE tasks with LLMs. Through their thorough review, several insights in technique and promising research directions are identified that deserve further exploration in future studies. The paper titled "Large Language Models for Generative Information Extraction: A Survey" by Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, and Enhong Chen provides an overview of the advancements in using LLMs for IE tasks. The abstract highlights how LLMs have shown remarkable capabilities in text understanding and generation across various domains. The authors aim to conduct a comprehensive review of recent advancements in this field by categorizing works based on different subtasks and learning paradigms. They also analyze advanced methods and identify emerging trends in IE tasks with LLMs. The paper concludes by identifying insights and promising research directions that warrant further exploration. Overall, this study contributes to our understanding of how LLMs can be effectively utilized for information extraction tasks and provides valuable insights for future research in this area. The authors also maintain a public repository where they consistently update related resources.