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

Abstract Named Entity Recognition (NER), one of the most fundamental problems in natural
language processing, seeks to identify the boundaries and types of entities with specific …

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

The unprecedented performance of large language models (LLMs) necessitates
improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we …

Prompt-learning has become a new paradigm in modern natural language processing,
which directly adapts pre-trained language models (PLMs) to $ cloze $-style prediction …

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 …

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 …

Prompt-based methods have been successfully applied in sentence-level few-shot learning
tasks, mostly owing to the sophisticated design of templates and label words. However …

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low
resource domains. Existing approaches only learn class-specific semantic features and …