InstructIE: A Bilingual Instruction-based Information Extraction Dataset
This addresses the lack of extensive, low-cost instruction data for information extraction, benefiting researchers and practitioners in NLP, though it is incremental as it builds on existing dataset generation methods.
The authors tackled the problem of large language models' suboptimal performance in information extraction due to limited instruction data by introducing InstructIE, a bilingual dataset covering 12 domains, and showed that models trained with it achieve better IE capabilities and enhanced zero-shot performance compared to baselines.
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.