---
license: openrail
---
https://github.com/BeyonderXX/InstructUIE

# InstructUIE
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.


## Data

Our models are trained and evaluated on **IE INSTRUCTIONS**. 
You can download the data from [Baidu NetDisk](https://pan.baidu.com/s/1R0KqeyjPHrsGcPqsbsh1XA?from=init&pwd=ybkt) or [Google Drive](https://drive.google.com/file/d/1T-5IbocGka35I7X3CE6yKe5N_Xg2lVKT/view?usp=share_link).




## Citation

If you are using InstructUIE for your work, please kindly cite our paper:

```latex
@article{wang2023instructuie,
  title={InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction},
  author={Wang, Xiao and Zhou, Weikang and Zu, Can and Xia, Han and Chen, Tianze and Zhang, Yuansen and Zheng, Rui and Ye, Junjie and Zhang, Qi and Gui, Tao and others},
  journal={arXiv preprint arXiv:2304.08085},
  year={2023}
}
```