Large language models have unlocked strong multi-task capabilities from
reading instructive prompts. However, recent studies have shown that existing
large models still have difficulty with information extraction tasks. For
example, gpt-3.5-turbo achieved an F1 score of 18.22 on the Ontonotes dataset,
which is significantly lower than the state-of-the-art performance. In this
paper, we propose InstructUIE, a unified information extraction framework based
on instruction tuning, which can uniformly model various information extraction
tasks and capture the inter-task dependency. To validate the proposed method,
we introduce IE INSTRUCTIONS, a benchmark of 32 diverse information extraction
datasets in a unified text-to-text format with expert-written instructions.
Experimental results demonstrate that our method achieves comparable
performance to Bert in supervised settings and significantly outperforms the
state-of-the-art and gpt3.5 in zero-shot settings.

该论文提出了基于指令调整的统一信息提取框架 InstructUIE，它可以统一地建模各种信息提取任务并捕获任务间的依赖关系。在零样本设置下，该方法显著优于现有的大型语言模型和 gpt3.5。