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.
@article{arxiv.2305.11527,
title = {InstructIE: A Bilingual Instruction-based Information Extraction Dataset},
author = {Honghao Gui and Shuofei Qiao and Jintian Zhang and Hongbin Ye and Mengshu Sun and Lei Liang and Jeff Z. Pan and Huajun Chen and Ningyu Zhang},
journal= {arXiv preprint arXiv:2305.11527},
year = {2024}
}