Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Despite the fact that large-scale Language Models (LLM) have achieved SOTA
performances on a variety of NLP tasks, its performance on NER is still significantly below …

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

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 …

Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Named Entity Recognition (NER) is the task of identifying spans that represent entities in
sentences. Whether the entity spans are nested or discontinuous, the NER task can be …

ChatGPT has stimulated the research boom in the field of large language models. In this
paper, we assess the capabilities of ChatGPT from four perspectives including Performance …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a
collection of 8 Korean natural language understanding (NLU) tasks, including Topic …