  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 predictions are unified into a linearized hierarchical
expression under a GLM. Syntactic structure information, a type of effective
feature which has been extensively utilized in IE community, should also be
beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully
unleashing the power of syntactic knowledge for UIE. A heterogeneous structure
inductor is explored to unsupervisedly induce rich heterogeneous structural
representations by post-training an existing GLM. In particular, a structural
broadcaster is devised to compact various latent trees into explicit high-order
forests, helping to guide a better generation during decoding. We finally
introduce a task-oriented structure fine-tuning mechanism, further adjusting
the learned structures to most coincide with the end-task's need. Over 12 IE
benchmarks across 7 tasks our system shows significant improvements over the
baseline UIE system. Further in-depth analyses show that our GLM learns rich
task-adaptive structural bias that greatly resolves the UIE crux, the
long-range dependence issue and boundary identifying. Source codes are open at
https://github.com/ChocoWu/LasUIE.
