Large language models for generative information extraction: a survey
University of Science and Technology of China · City University of Hong Kong · +2 more institutions
Abstract
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…
Citation impact
- FWCI
- 64.20
- Percentile
- 100%
- References
- 152
Authors
10- DXDerong XuCorresponding
University of Science and Technology of China, City University of Hong Kong
- WCWei Chen
University of Science and Technology of China
- WPWenjun Peng
University of Science and Technology of China
- CZChao Zhang
University of Science and Technology of China, City University of Hong Kong
- TXTong Xu
University of Science and Technology of China
Topics & keywords
- Computer science
- Generative grammar
- Information extraction
- Natural language processing
- Extraction (chemistry)
- Artificial intelligence
- Generative model
- Information retrieval
- Quality Education