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- W4366400290 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 significantly outperforms the state-of-the-art and gpt3.5 in zero-shot settings." @default.
- W4366400290 created "2023-04-21" @default.
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- W4366400290 date "2023-04-17" @default.
- W4366400290 modified "2023-09-23" @default.
- W4366400290 title "InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction" @default.
- W4366400290 doi "https://doi.org/10.48550/arxiv.2304.08085" @default.
- W4366400290 hasPublicationYear "2023" @default.
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