Recent advances in pre-trained language models (PLMs) have facilitated the development
ofcommonsense reasoning tasks. However, existing methods rely on multi-hop …

Various large language models (LLMs) have been proposed in recent years, including
closed-and open-source ones, continually setting new records on multiple benchmarks …

Sharing knowledge between information extraction tasks has always been a challenge due
to the diverse data formats and task variations. Meanwhile, this divergence leads to …

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by
varying targets, heterogeneous structures, and demand-specific schemas. However …

We propose a new paradigm for universal information extraction (IE) that is compatible with
any schema format and applicable to a list of IE tasks, such as named entity recognition …

The prediction of chemical synthesis pathways plays a pivotal role in materials science
research. Challenges, such as the complexity of synthesis pathways and the lack of …

Question answering plays a pivotal role in human daily life because it involves our
acquisition of knowledge about the world. However, due to the dynamic and ever-changing …

Medical text classification aims to identify the category to which a short medical text belongs.
Recent research indicates that the “prompt-based learning” paradigm, especially the soft …

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities
of artificial intelligence (AI). However, existing benchmarks either require just a few steps of …

In natural language processing (NLP), zero-shot topic classification requires machines to
understand the contextual meanings of texts in a downstream task without using the …