RUIE: Retrieval-based Unified Information Extraction using Large Language Model

Abstract

Unified information extraction (UIE) aims to complete all information extraction tasks using a single model or framework. While previous work has primarily focused on instruction-tuning large language models (LLMs) with constructed datasets, these methods require significant computational resources and struggle to generalize to unseen tasks. To address these limitations, we propose RUIE (Retrieval-based Unified Information Extraction), a framework that leverages in-context learning to enable rapid generalization while reducing computational costs. The key challenge in RUIE is selecting the most beneficial demonstrations for LLMs to effectively handle diverse IE tasks. To achieve this, we integrate LLM preferences for ranking candidate demonstrations and design a keyword-enhanced reward model to capture fine-grained relationships between queries and demonstrations. We then train a bi-encoder retriever for UIE through contrastive learning and knowledge distillation. To the best of our knowledge, RUIE is the first trainable retrieval framework for UIE. Experimental results on 8 held-out datasets demonstrate RUIE's effectiveness in generalizing to unseen tasks, with average F1-score improvements of 19.22 and 3.13 compared to instruction-tuning methods and other retrievers, respectively. Further analysis confirms RUIE's adaptability to LLMs of varying sizes and the importance of its key components.