InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
This addresses the challenge of improving information extraction for AI applications by providing a unified approach, though it is incremental as it builds on existing instruction tuning methods.
The paper tackles the problem of large language models underperforming on information extraction tasks, such as GPT-3.5 achieving an F1 score of 18.22 on Ontonotes, by proposing InstructUIE, a framework that uses instruction tuning to unify these tasks and capture dependencies, resulting in comparable performance to BERT in supervised settings and significant improvements in zero-shot settings.
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.