  Large language models can perform well on general natural language tasks, but
their effectiveness is still suboptimal for information extraction (IE). Recent
works indicate that the main reason lies in the lack of extensive data on IE
instructions. Note that the existing datasets on IE instructions not only have
limited coverage but also involve high construction costs. To address this
issue, we introduce InstructIE, a bilingual instruction-based IE dataset, which
covers 12 diverse domains. We propose KG2Instruction, a framework specifically
for the automatic generation of such datasets. Additionally, we manually
annotate the test set. Experimental results demonstrate that large language
models trained with InstructIE can not only obtain better IE capabilities but
also enhance zero-shot performance compared with baselines.
