InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
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

TL;DR
InstructUIE is a unified instruction-tuned framework for information extraction that models multiple tasks simultaneously, outperforming existing large models especially in zero-shot scenarios, validated on a new diverse benchmark.
Contribution
The paper introduces InstructUIE, a novel instruction tuning approach for multi-task information extraction, and presents IE INSTRUCTIONS, a comprehensive benchmark for evaluation.
Findings
InstructUIE achieves comparable results to BERT in supervised settings.
It significantly outperforms GPT-3.5 in zero-shot settings.
The benchmark includes 32 diverse datasets in a unified format.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Software Engineering Research
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Linear Warmup With Linear Decay · WordPiece · Weight Decay · Adam · Dense Connections · Attention Dropout · Dropout
