@inproceedings{liu-etal-2026-prouie,
title = "{P}ro{UIE}: A Macro-to-Micro Progressive Learning Method for {LLM}-based Universal Information Extraction",
author = "Liu, Wenda and
Zhigang, Song and
Nie, Shuai and
Liu, Guangyao and
Chen, Lisung and
Yang, Binyu and
Chen, Yaran and
Zhou, Peng and
Wang, Hongzhen and
Liu, Yuchen and
Hu, Wenyue and
Xu, Jiaming and
Shi, Runyu and
Huang, Ying",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1093/",
pages = "21737--21750",
ISBN = "979-8-89176-395-1",
abstract = "LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yields limited gains. To address this, we propose ProUIE, a Macro-to-Micro progressive learning approach that improves UIE without introducing any external information. ProUIE consists of three stages: (i) macro-level Complete Modeling (CM), which learns NER, RE, and EE along their intrinsic difficulty order on the full training data to build a unified extraction foundation, (ii) meso-level Streamlined Alignment (SA), which operates on sampled data with simplified target formats, streamlining and regularizing structured outputs to make them more concise and controllable, and (iii) micro-level Deep Exploration (DE), which applies GRPO with stepwise fine-grained rewards (SFR) over structural units to guide exploration and improve performance. Experiments on 36 public datasets show that ProUIE consistently improves unified extraction, outperforming strong instruction-tuned baselines on average for NER and RE while using a smaller backbone, and it further demonstrates clear gains in production-oriented information extraction."
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<abstract>LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yields limited gains. To address this, we propose ProUIE, a Macro-to-Micro progressive learning approach that improves UIE without introducing any external information. ProUIE consists of three stages: (i) macro-level Complete Modeling (CM), which learns NER, RE, and EE along their intrinsic difficulty order on the full training data to build a unified extraction foundation, (ii) meso-level Streamlined Alignment (SA), which operates on sampled data with simplified target formats, streamlining and regularizing structured outputs to make them more concise and controllable, and (iii) micro-level Deep Exploration (DE), which applies GRPO with stepwise fine-grained rewards (SFR) over structural units to guide exploration and improve performance. Experiments on 36 public datasets show that ProUIE consistently improves unified extraction, outperforming strong instruction-tuned baselines on average for NER and RE while using a smaller backbone, and it further demonstrates clear gains in production-oriented information extraction.</abstract>
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%0 Conference Proceedings
%T ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction
%A Liu, Wenda
%A Zhigang, Song
%A Nie, Shuai
%A Liu, Guangyao
%A Chen, Lisung
%A Yang, Binyu
%A Chen, Yaran
%A Zhou, Peng
%A Wang, Hongzhen
%A Liu, Yuchen
%A Hu, Wenyue
%A Xu, Jiaming
%A Shi, Runyu
%A Huang, Ying
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-prouie
%X LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yields limited gains. To address this, we propose ProUIE, a Macro-to-Micro progressive learning approach that improves UIE without introducing any external information. ProUIE consists of three stages: (i) macro-level Complete Modeling (CM), which learns NER, RE, and EE along their intrinsic difficulty order on the full training data to build a unified extraction foundation, (ii) meso-level Streamlined Alignment (SA), which operates on sampled data with simplified target formats, streamlining and regularizing structured outputs to make them more concise and controllable, and (iii) micro-level Deep Exploration (DE), which applies GRPO with stepwise fine-grained rewards (SFR) over structural units to guide exploration and improve performance. Experiments on 36 public datasets show that ProUIE consistently improves unified extraction, outperforming strong instruction-tuned baselines on average for NER and RE while using a smaller backbone, and it further demonstrates clear gains in production-oriented information extraction.
%U https://aclanthology.org/2026.findings-acl.1093/
%P 21737-21750
Markdown (Informal)
[ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction](https://aclanthology.org/2026.findings-acl.1093/) (Liu et al., Findings 2026)
ACL
- 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, and Ying Huang. 2026. ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21737–21750, San Diego, California, United States. Association for Computational Linguistics.