Information extraction (IE) aims to extract structural knowledge (such as entities, relations,
and events) from plain natural language texts. Recently, generative Large Language Models …

Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Information extraction suffers from its varying targets, heterogeneous structures, and
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …

The identification of complex semantic structures such as events and entity relations, already
a challenging Information Extraction task, is doubly difficult from sources written in under …

Existing works on information extraction (IE) have mainly solved the four main tasks
separately (entity mention recognition, relation extraction, event trigger detection, and …

Structured and grounded representation of text is typically formalized by closed information
extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets …

With the advance of language models, privacy protection is receiving more attention.
Training data extraction is therefore of great importance, as it can serve as a potential tool to …

Named entity recognition (NER) is a fundamental task to recognize specific types of entities
from a given sentence. Depending on how the entities appear in the sentence, it can be …

Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …

Large language models with instruction-following capabilities open the door to a wider
group of users. However, when it comes to information extraction-a classic task in natural …