MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction

Information extraction (IE) is a fundamental task in natural language processing (NLP), which encompasses a wide range of subtasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE) [1-4]. Traditionally, these tasks have been addressed by specialized models trained in task-specific datasets. However, the fragmentation of tasks and schemas has hindered the development of generalizable and scalable IE tasks. To address this limitation, recent research has focused on universal information extraction (UIE), which aims to model all IE tasks within a universal framework. A seminal work in this direction is proposed by Lu et al., which introduced a structured generation paradigm that encodes diverse IE tasks into a common semantic representation[5]. Building on this, InstructUIE[6] extended the idea by incorporating multi-task instruction tuning, enabling models to generalize across tasks via natural language instructions. With the emergence of powerful LLMs[7-11], significant advancements have been made across long-standing NLP tasks such as text classification[12-16], intent recognition[17, 18], entity linking[19-22], and beyond. Inspired by their robust performance and adaptability, researchers have explored their potential for information extraction through prompting and in-context learning learning[23, 24]. For example, CodeIE demonstrated that code generation models can serve as strong few-shot IE extractors by using structured code-like commands[25].