Papers › InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction

InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction

17 Apr 2023arXiv:2304.08085archive 2025-07-28

Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, Jihua Kang, Jingsheng Yang, Siyuan Li, Chunsai Du

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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Code

beyonderxx/instructuie officialmentioned in paperpytorch report

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Tasks

Zero-shot Named Entity Recognition (NER)

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Zero-shot Named Entity Recognition (NER)	CrossNER	InstructUIE	AI	49.0	#3 of 4	Archive leaderboard	report
Zero-shot Named Entity Recognition (NER)	CrossNER	InstructUIE	Literature	47.2	#3 of 4	Archive leaderboard	report
Zero-shot Named Entity Recognition (NER)	CrossNER	InstructUIE	Music	53.2	#3 of 4	Archive leaderboard	report
Zero-shot Named Entity Recognition (NER)	CrossNER	InstructUIE	Politics	48.2	#3 of 4	Archive leaderboard	report
Zero-shot Named Entity Recognition (NER)	CrossNER	InstructUIE	Science	49.3	#3 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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