---
title: "Capability Decomposition for Unified Information Extraction via"
description: "They propose UC-UIE, an LLM-based extractor with three parts: Unified schema. 99 references."
authors: ["Jing Zhou", "Peng Wang", "Wenjun Ke", "Jiajun Liu", "Yao He"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.561/"
doi: "10.18653/v1/2026.acl-long.561"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2026-capability-decomposition-unified-information-extraction/"
---
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# Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts

**Authors:** Jing Zhou, [Peng Wang](https://md.research.elamur.ai/free/en/authors/peng-wang/), [Wenjun Ke](https://md.research.elamur.ai/free/en/authors/wenjun-ke/), [Jiajun Liu](https://md.research.elamur.ai/free/en/authors/jiajun-liu/) and [Yao He](https://md.research.elamur.ai/free/en/authors/yao-he/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.561/) · [PDF](https://aclanthology.org/2026.acl-long.561.pdf) · [DOI 10.18653/v1/2026.acl-long.561](https://doi.org/10.18653/v1/2026.acl-long.561) · [OpenAlex](https://openalex.org/W7166850952)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses Unified Information Extraction (UIE): handling heterogeneous IE tasks (NER, RE, EE, ABSA) with one model. The authors identify three weaknesses in earlier systems: task-specific output formats, reasoning left implicit inside shared weights, and costly full-parameter tuning of large models. They propose UC-UIE, an LLM-based extractor with three parts:

- **Unified schema.** Drawing on frame semantics and slot-filling, every extraction target is recast as a frame with a type and typed slots, so all tasks produce outputs in one format.
- **Capability decomposition.** Extraction is split into three generic skills: judging whether a frame is present, locating its text spans, and associating the pieces into a structure.
- **Hierarchical MoE adapter.** These skills are modeled by a LoRA-based mixture-of-experts adapter with a task-driven router and an entropy-based balancing loss.

Across 34 benchmarks, UC-UIE beats full-parameter tuning baselines while training only a small fraction of weights. It also generalizes reasonably to unseen domains and schemas in zero-shot settings. Analyses show that the experts genuinely specialize and combine. This makes the approach attractive for adapting LLMs to IE cheaply and with more interpretable internals.

## Key points
- Evaluated on 4 IE task families across 34 benchmarks. UC-UIE uses only 1.24% trainable parameters, yet outperforms full-parameter tuning methods.
- In zero-shot evaluation, the model reaches more than half of the performance of supervised systems. Paraphrasing task instructions during training further improves zero-shot robustness.
- Expert specialization is clear:
  - Disabling the expert a capability subtask depends on reduces scores by roughly 18–26%.
  - Without the associating expert, RE drops by 15.46% and EAE by 15.77%.
- Learned soft routing outperforms hard binary expert activation by 0.64%–5.13%. About 4 sub-experts per low-level MoE is optimal; adding more saturates or hurts performance, especially in low-resource and zero-shot settings.
- The entropy-based loss prevents routing collapse. Without it, a few sub-experts dominate, with some routing weights above 0.7.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university-cn/) — University, Nanjing, China · 32 works · 453 citations
- [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university/) — University, Dhaka, Bangladesh · 9 works · 160 citations

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🧭 Topics (1): [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · 175 works

## Cite

Cite as: Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu and Yao He. 2026. Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhou2026capability,
  title = {{Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts}},
  author = {Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu and Yao He},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.561},
  url = {https://doi.org/10.18653/v1/2026.acl-long.561}
}
```

- [← previous CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/)
- [next → CaPEdit: Capability-Preserving Lifelong Knowledge Editing For Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-capedit-capability-preserving-lifelong-knowledge/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/26/)

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*Updated 2 November 2026*