mlsanityData to 7 Oct 2026

YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction

arXiv 2312.15548 ↗First posted 24 Dec 2023cs.CL cs.AIcited by 19

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Abstract (arXiv)

The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on large language models to uniformly model different information extraction tasks. However, these existing methods are deficient in their information extraction capabilities for Chinese languages other than English. In this paper, we propose an end-to-end chat-enhanced instruction tuning framework for universal information extraction (YAYI-UIE), which supports both Chinese and English. Specifically, we utilize dialogue data and information extraction data to enhance the information extraction performance jointly. Experimental results show that our proposed framework achieves state-of-the-art performance on Chinese datasets while also achieving comparable performance on English datasets under both supervised settings and zero-shot settings.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 24 Dec 2023.

Builds on

22 of 35 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
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  10. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  11. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  12. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
10 more
  1. 2020CrossNER: Evaluating Cross-Domain Named Entity Recognitioncited by 102
  2. 2020ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learningcited by 72
  3. 2023Universal Information Extraction as Unified Semantic Matchingcited by 47
  4. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  5. 2021CLEVE: Contrastive Pre-training for Event Extractioncited by 36
  6. 2020Coarse-to-Fine Pre-training for Named Entity Recognitioncited by 28
  7. 2022MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspectivecited by 17
  8. 2022CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extractioncited by 10
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 8; 2026: 2.

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  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  2. 2024AutoRE: Document-Level Relation Extraction with Large Language Modelscited by 16
  3. 2025Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generationcited by 7
  4. 2025MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extractioncited by 6
  5. 2024IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpuscited by 5
  6. 2024Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognitioncited by 5
  7. 2024CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graphcited by 3
  8. 2024RUIE: Retrieval-based Unified Information Extraction using Large Language Modelcited by 3
  9. 2024KnowCoder-X: Boosting Multilingual Information Extraction via Codecited by 3
  10. 2026Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMscited by 3
  11. 2025MAQInstruct: Instruction-based Unified Event Relation Extractioncited by 2
  12. 2025Schema as Parameterized Tools for Universal Information Extractioncited by 2
7 more
  1. 2024Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Taskcited by 1
  2. 2024A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extractioncited by 1
  3. 2025Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesiscited by 1
  4. 2025Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solutioncited by 1
  5. 2025ToMMeR -- Efficient Entity Mention Detection from Large Language Modelscited by 0
  6. 2025SCIR: A Self-Correcting Iterative Refinement Framework for Enhanced Information Extraction Based on Schemacited by 0
  7. 2026ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extractioncited by 0