  Universal Information Extraction (UIE) is an area of interest due to the
challenges posed by varying targets, heterogeneous structures, and
demand-specific schemas. However, previous works have only achieved limited
success by unifying a few tasks, such as Named Entity Recognition (NER) and
Relation Extraction (RE), which fall short of being authentic UIE models
particularly when extracting other general schemas such as quadruples and
quintuples. Additionally, these models used an implicit structural schema
instructor, which could lead to incorrect links between types, hindering the
model's generalization and performance in low-resource scenarios. In this
paper, we redefine the authentic UIE with a formal formulation that encompasses
almost all extraction schemas. To the best of our knowledge, we are the first
to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which
is a Recursive Method with Explicit Schema Instructor for UIE. To avoid
interference between different types, we reset the position ids and attention
mask matrices. RexUIE shows strong performance under both full-shot and
few-shot settings and achieves State-of-the-Art results on the tasks of
extracting complex schemas.
