Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)
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| 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. |
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| Challenge: | Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. |
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| Challenge: | Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks. |
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| Challenge: | Unified information extraction (UIE) aims to extract diverse structured information from unstructured text using a single model or framework. |
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| Challenge: | Existing unified information extraction approaches face challenges such as noise interference, abstract label semantics, and diverse span granularity. |
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| 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. |
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| Challenge: | Existing robust benchmark datasets generate only a limited range of perturbations for a single Information Extraction (UIE) task, which fails to evaluate the robustness of UIE models effectively. |
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
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MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)
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