  Events refer to specific occurrences, incidents, or happenings that take
place under a particular background. Event reasoning aims to infer events
according to certain relations and predict future events. The cutting-edge
techniques for event reasoning play a crucial role in various natural language
processing applications. Large language models (LLMs) have made significant
advancements in event reasoning owing to their wealth of knowledge and
reasoning capabilities. However, smaller instruction-tuned models currently in
use do not consistently demonstrate exceptional proficiency in managing these
tasks. This discrepancy arises from the absence of explicit modeling of events
and the interconnections of them within their instruction data. Consequently,
these models face challenges in comprehending event structures and semantics
while struggling to bridge the gap between their interpretations and human
understanding of events. Additionally, their limitations in grasping event
relations lead to constrained event reasoning abilities to effectively deduce
and incorporate pertinent event knowledge. In this paper, we propose
Event-Oriented Instruction Tuning (EvIT) to train our LLM. Specifically, we
first propose a novel structure named event quadruple which contains the
structure and semantics of events and is complete in the event representation.
We then design event-relation learning based on the structures. We encapsulate
the learning into the instruction-tuning formulation to better stimulate the
event reasoning capacity of our model. We design a heuristic unsupervised
method to mine event quadruple from a large-scale corpus. At last, we finetune
a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive
experiments on event reasoning tasks on several datasets. Automatic and human
evaluations demonstrate EvIT achieves competitive performances on event
reasoning.
