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| license: openrail |
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| https://github.com/BeyonderXX/InstructUIE |
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| # 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. |
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| ## Data |
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| 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). |
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| ## Citation |
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| If you are using InstructUIE for your work, please kindly cite our paper: |
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| ```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} |
| } |
| ``` |
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