RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

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Overview

Challenge

Approach

Outcome

Unified Structure Generation for Universal Information Extraction (2022.acl-long)

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Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

Information Extraction & Text Mining

Challenge:	Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Approach:	They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources.
Outcome:	The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources.

UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)

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Yang Ping, JunYu Lu, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Pingjian Zhang, Jiaxing Zhang

Information Extraction & Text Mining

Challenge:	Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask.
Approach:	They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks.
Outcome:	The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios.

ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)

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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang

Information Extraction & Text Mining

Challenge:	ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines .
Approach:	They propose a Macro-to-Micro progressive learning approach that improves UIE without external information.
Outcome:	ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone.

TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)

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Yucheng Wang, Bowen Yu, Yilin Liu, Shudong Lu

Information Extraction & Text Mining

Challenge:	Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks.
Approach:	They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition .
Outcome:	The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks.

Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction (2025.naacl-long)

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Lu Yang, Jiajia Li, En Ci, Lefei Zhang, Zuchao Li, Ping Wang

Information Extraction & Text Mining

Challenge:	Extractive UIEs can solve model explosion problems using a relatively small model . single-target instruction UIE enables the extraction of only one type of relation at a time .
Approach:	They propose a model that assigns different relations to different levels for understanding and decision-making.
Outcome:	Experiments show that LDNet outperforms state-of-the-art systems on 9 tasks, 33 datasets . LDnet outperformed state- of-the art systems on single-modal and multi-modal tasks .

MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)

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Shuyi Zhang, Zhenbin Chen, Shuting Li, Kewei Tu, Li Jing, Zixia Jia, Zilong Zheng

Information Extraction & Text Mining

Challenge:	Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints.
Approach:	They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts.
Outcome:	The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains.

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)

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Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

Information Extraction & Text Mining

Challenge:	Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges.
Approach:	They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries .
Outcome:	The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs.

DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction (D18-2)

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Heike Adel, Laura Ana Maria Bostan, Sean Papay, Sebastian Padó, Roman Klinger

Information Extraction & Text Mining

Challenge:	Comparability of models across tasks is lacking in most machine learning systems for natural language processing.
Approach:	They propose a framework for declarative specification and compilation of template-based information extraction that uses a generic specification language for the task and for data annotations in terms of spans and frames.
Outcome:	The proposed framework enables representation of a large variety of natural language processing tasks.

RUIE: Retrieval-based Unified Information Extraction using Large Language Model (2025.coling-main)

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Xincheng Liao, Junwen Duan, Yixi Huang, Jianxin Wang

Language Models

Challenge:	Unified information extraction (UIE) aims to extract diverse structured information from unstructured text using a single model or framework.
Approach:	They propose a framework that leverages in-context learning for efficient task generalization by combining LLM preferences with a keyword-enhanced reward model.
Outcome:	The proposed framework performs better on eight held-out datasets than existing methods and instruction-tuning methods.

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)

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Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu, Yao He

Responsible & Trustworthy NLP

Challenge:	Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks .
Approach:	They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks .
Outcome:	The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability.