Large language models for generative information extraction: A survey
Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …
C-ICL: contrastive in-context learning for information extraction
There has been increasing interest in exploring the capabilities of advanced large language
models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related …
models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related …
IEPile: unearthing large scale schema-conditioned information extraction corpus
Abstract Large Language Models (LLMs) demonstrate remarkable potential across various
domains; however, they exhibit a significant performance gap in Information Extraction (IE) …
domains; however, they exhibit a significant performance gap in Information Extraction (IE) …
Retrieval augmented instruction tuning for open ner with large language models
The strong capability of large language models (LLMs) has been applied to information
extraction (IE) through either retrieval augmented prompting or instruction tuning (IT) …
extraction (IE) through either retrieval augmented prompting or instruction tuning (IT) …
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
In recent years, the rise of large language models (LLMs) has made it possible to directly
achieve named entity recognition (NER) without any demonstration samples or only using a …
achieve named entity recognition (NER) without any demonstration samples or only using a …
LTNER: Large language model tagging for named entity recognition with contextualized entity marking
F Yan, P Yu, X Chen - International Conference on Pattern Recognition, 2025 - Springer
The use of LLMs for natural language processing has become a popular trend in the past
two years, driven by their formidable capacity for context comprehension and learning …
two years, driven by their formidable capacity for context comprehension and learning …
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Large language models can perform well on general natural language tasks, but their
effectiveness is still suboptimal for information extraction (IE). Recent works indicate that the …
effectiveness is still suboptimal for information extraction (IE). Recent works indicate that the …
Concept-driven knowledge distillation and pseudo label generation for continual named entity recognition
Continual named entity recognition requires models to be continuously updated to
recognize new entity types while retaining learned knowledge. In this task, the inherent …
recognize new entity types while retaining learned knowledge. In this task, the inherent …
From local to global: Leveraging document graph for named entity recognition
Abstract Named Entity Recognition (NER) is a fundamental task in Natural Language
Processing (NLP) that aims to identify the span and category of entities within text. Recent …
Processing (NLP) that aims to identify the span and category of entities within text. Recent …
A Survey of Generative Information Extraction
Z Zhang, W You, T Wu, X Wang, J Li… - Proceedings of the 31st …, 2025 - aclanthology.org
Generative information extraction (Generative IE) aims to generate structured text
sequences from unstructured text using a generative framework. Scaling in model size …
sequences from unstructured text using a generative framework. Scaling in model size …